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#!/usr/bin/env node
import { existsSync, mkdirSync, mkdtempSync, readFileSync, readdirSync, renameSync, rmSync, writeFileSync } from "fs";
import { dirname, join, resolve } from "path";
import { fileURLToPath } from "url";
import { getEffortThinkingLevelMap, type ModelsDevReasoningOption } from "./models-dev-reasoning-options.ts";
import {
getOpenRouterThinkingLevelMap,
type OpenRouterReasoningMetadata,
} from "./openrouter-reasoning-options.ts";
import {
CLOUDFLARE_AI_GATEWAY_ANTHROPIC_BASE_URL,
CLOUDFLARE_AI_GATEWAY_COMPAT_BASE_URL,
CLOUDFLARE_AI_GATEWAY_OPENAI_BASE_URL,
CLOUDFLARE_WORKERS_AI_BASE_URL,
} from "../src/api/cloudflare.ts";
import type {
AnthropicMessagesCompat,
Api,
KnownProvider,
Model,
ModelCost,
OpenAICompletionsCompat,
OpenAIResponsesCompat,
} from "../src/types.ts";
import {
assertExactModelIds,
createModelDataManifest,
type ModelDataStructure,
MODEL_DATA_MANIFEST_FILE,
readModelDataProviderIds,
validateGeneratedModelData,
validateModelDataDirectory,
} from "./model-data.ts";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const packageRoot = join(__dirname, "..");
function readGeneratorOptions(args: string[]): {
strict: boolean;
dataOnly: boolean;
jsonOnly: boolean;
jsonOutputDir: string | undefined;
pretty: boolean;
} {
let strict = false;
let dataOnly = false;
let jsonOnly = false;
let jsonOutputDir: string | undefined;
let pretty = false;
for (let index = 0; index < args.length; index++) {
const arg = args[index];
if (arg === "--strict") {
strict = true;
continue;
}
if (arg === "--data-only") {
dataOnly = true;
continue;
}
if (arg === "--json-only") {
jsonOnly = true;
continue;
}
if (arg === "--pretty") {
pretty = true;
continue;
}
if (arg === "--json-output") {
const value = args[++index];
if (!value) throw new Error("--json-output requires a directory");
jsonOutputDir = resolve(value);
continue;
}
throw new Error(`Unknown argument: ${arg}`);
}
if (jsonOnly && !jsonOutputDir) throw new Error("--json-only requires --json-output");
if (dataOnly && (jsonOnly || jsonOutputDir)) throw new Error("--data-only cannot be combined with JSON catalog output");
return { strict, dataOnly, jsonOnly, jsonOutputDir, pretty };
}
const generatorOptions = readGeneratorOptions(process.argv.slice(2));
interface ModelsDevModel {
id: string;
name: string;
tool_call?: boolean;
structured_output?: boolean;
reasoning?: boolean;
reasoning_options?: ModelsDevReasoningOption[];
status?: string;
limit?: {
context?: number;
output?: number;
};
cost?: {
input?: number;
output?: number;
cache_read?: number;
cache_write?: number;
tiers?: {
input?: number;
output?: number;
cache_read?: number;
cache_write?: number;
tier?: {
type?: string;
size?: number;
};
}[];
};
modalities?: {
input?: string[];
output?: string[];
};
provider?: {
npm?: string;
};
}
interface ModelsDevProvider {
models?: Record<string, ModelsDevModel>;
}
type ModelsDevCatalog = Record<string, ModelsDevProvider>;
interface NvidiaNimModelListItem {
id: string;
}
interface OpenRouterModelListItem {
id: string;
name: string;
supported_parameters?: string[];
architecture?: { modality?: string };
pricing?: {
prompt?: string;
completion?: string;
input_cache_read?: string;
input_cache_write?: string;
};
top_provider?: {
context_length?: number;
max_completion_tokens?: number;
};
context_length?: number;
reasoning?: OpenRouterReasoningMetadata;
}
interface AiGatewayModel {
id: string;
name?: string;
context_window?: number;
max_tokens?: number;
tags?: string[];
pricing?: {
input?: string | number;
output?: string | number;
input_cache_read?: string | number;
input_cache_write?: string | number;
};
}
const COPILOT_STATIC_HEADERS = {
"User-Agent": "GitHubCopilotChat/0.35.0",
"Editor-Version": "vscode/1.107.0",
"Editor-Plugin-Version": "copilot-chat/0.35.0",
"Copilot-Integration-Id": "vscode-chat",
} as const;
const TOGETHER_BASE_URL = "https://api.together.ai/v1";
const TOGETHER_BASE_COMPAT: OpenAICompletionsCompat = {
supportsStore: false,
supportsDeveloperRole: false,
supportsReasoningEffort: false,
maxTokensField: "max_tokens",
supportsStrictMode: false,
supportsLongCacheRetention: false,
};
const TOGETHER_TOGGLE_REASONING_COMPAT: OpenAICompletionsCompat = {
...TOGETHER_BASE_COMPAT,
thinkingFormat: "together",
};
const TOGETHER_REASONING_EFFORT_COMPAT: OpenAICompletionsCompat = {
...TOGETHER_BASE_COMPAT,
supportsReasoningEffort: true,
thinkingFormat: "openai",
};
const TOGETHER_TOGGLE_REASONING_EFFORT_COMPAT: OpenAICompletionsCompat = {
...TOGETHER_TOGGLE_REASONING_COMPAT,
supportsReasoningEffort: true,
};
const TOGETHER_REASONING_ONLY_MODELS = new Set([
"deepseek-ai/DeepSeek-R1",
"MiniMaxAI/MiniMax-M2.7",
]);
const TOGETHER_REASONING_EFFORT_MODELS = new Set(["openai/gpt-oss-20b", "openai/gpt-oss-120b"]);
const TOGETHER_TOGGLE_REASONING_EFFORT_MODELS = new Set(["deepseek-ai/DeepSeek-V4-Pro"]);
const TOGETHER_FIXED_REASONING_LEVEL_MAP = {
off: null,
minimal: null,
low: null,
medium: null,
} as const;
const TOGETHER_REASONING_EFFORT_LEVEL_MAP = {
off: null,
minimal: null,
} as const;
const TOGETHER_DEEPSEEK_V4_THINKING_LEVEL_MAP = {
minimal: null,
low: null,
medium: null,
high: "high",
xhigh: null,
} as const;
const TOGETHER_TOGGLE_REASONING_LEVEL_MAP = {
minimal: null,
low: null,
medium: null,
} as const;
const AI_GATEWAY_MODELS_URL = "https://ai-gateway.vercel.sh/v1";
const AI_GATEWAY_BASE_URL = "https://ai-gateway.vercel.sh";
const VERTEX_BASE_URL = "https://{location}-aiplatform.googleapis.com";
const NVIDIA_BASE_URL = "https://integrate.api.nvidia.com/v1";
const NVIDIA_HEADERS = {
"NVCF-POLL-SECONDS": "3600",
} as const;
const NVIDIA_OPENAI_COMPAT: OpenAICompletionsCompat = {
supportsStore: false,
supportsDeveloperRole: false,
supportsReasoningEffort: false,
maxTokensField: "max_tokens",
supportsStrictMode: false,
supportsLongCacheRetention: false,
};
const NVIDIA_NIM_UNSUPPORTED_MODELS = new Set([
"abacusai/dracarys-llama-3.1-70b-instruct",
"bytedance/seed-oss-36b-instruct",
"deepseek-ai/deepseek-v4-flash",
"deepseek-ai/deepseek-v4-pro",
"google/gemma-2-2b-it",
"google/gemma-3n-e2b-it",
"google/gemma-3n-e4b-it",
"google/gemma-4-31b-it",
"meta/llama-3.2-1b-instruct",
"meta/llama-4-maverick-17b-128e-instruct",
"microsoft/phi-4-mini-instruct",
"minimaxai/minimax-m2.7",
"mistralai/mistral-nemotron",
"nvidia/nemotron-mini-4b-instruct",
"qwen/qwen3-next-80b-a3b-instruct",
"qwen/qwen3.5-397b-a17b",
"sarvamai/sarvam-m",
"upstage/solar-10.7b-instruct",
]);
const ZAI_TOOL_STREAM_UNSUPPORTED_MODELS = new Set(["glm-4.5", "glm-4.5-air", "glm-4.5-flash", "glm-4.5v"]);
const OPENCODE_GO_GLM52_THINKING_LEVEL_MAP = {
off: null,
minimal: null,
low: null,
medium: null,
high: "high",
max: "max",
} as const;
const EAGER_TOOL_INPUT_STREAMING_UNSUPPORTED_ANTHROPIC_MODELS = new Set([
"github-copilot:claude-haiku-4.5",
"github-copilot:claude-sonnet-4",
"github-copilot:claude-sonnet-4.5",
]);
const ANTHROPIC_ALLOWED_FALLBACK_MODELS = {
"claude-fable-5": ["claude-opus-4-8", "claude-opus-5"],
"claude-opus-5": ["claude-opus-4-8"],
} satisfies Record<string, string[]>;
const DEEPSEEK_V4_THINKING_LEVEL_MAP = {
minimal: null,
low: null,
medium: null,
high: "high",
max: "max",
} as const;
const DEEPSEEK_V4_FLASH_THINKING_LEVEL_MAP = {
...DEEPSEEK_V4_THINKING_LEVEL_MAP,
low: "low",
} as const;
// Verified against Fireworks Messages raw_output on 2026-09-10 (#9323).
// Fall back to verified support when models.dev omits effort metadata; this is
// not an allowlist. Any Fireworks Messages model advertising effort uses adaptive thinking.
const FIREWORKS_ADAPTIVE_THINKING_FALLBACK_MODELS = new Set([
"accounts/fireworks/models/deepseek-v4-flash-0731",
"accounts/fireworks/models/deepseek-v4-flash-vision-exp",
"accounts/fireworks/models/deepseek-v4-pro-0813",
"accounts/fireworks/models/qwen3p8-max",
"accounts/fireworks/models/qwen3p8-2p4t-a95b",
]);
const QWEN_TOKEN_PLAN_FALLBACK_THINKING_LEVEL_MAP = {
minimal: null,
low: null,
medium: null,
high: "high",
xhigh: null,
max: "max",
} as const;
const QWEN_TOKEN_PLAN_REASONING_EFFORT_FALLBACK_MODEL_IDS = new Set(["glm-5", "glm-5.1"]);
// Retired preview id — models.dev may still list it after GA ships.
const QWEN_TOKEN_PLAN_EXCLUDED_MODEL_IDS = new Set(["qwen3.8-max-preview"]);
const QWEN_TOKEN_PLAN_PROVIDER_IDS = new Set<string>([
"qwen-token-plan",
"qwen-token-plan-cn",
"qwen-token-plan-individual",
]);
// QwenCloud Token Plan Individual text-model allowlist, verified 2026-09-03.
// Retired models remain excluded above even if the public catalog lags.
// https://docs.qwencloud.com/token-plan/personal/token-plan-personal-overview
const QWEN_TOKEN_PLAN_INDIVIDUAL_MODEL_IDS = new Set<string>([
"deepseek-v4-flash-0731",
"deepseek-v4-pro",
"deepseek-v4-pro-0813",
"glm-5.2",
"qwen3.6-flash",
"qwen3.7-max",
"qwen3.7-plus",
"qwen3.8-flash",
"qwen3.8-max",
]);
const KIMI_K3_MAX_TOKENS = 131072;
const KIMI_K3_COST = {
input: 3,
output: 15,
cacheRead: 0.3,
cacheWrite: 0,
} as const;
// Kimi Coding is subscription-backed, so models.dev reports zero cost. Use the
// equivalent Moonshot API rates to estimate the value of subscription usage.
const KIMI_CODING_IMPLIED_COSTS: Record<string, Model<Api>["cost"]> = {
k3: KIMI_K3_COST,
"kimi-for-coding": { input: 0.95, output: 4, cacheRead: 0.19, cacheWrite: 0 },
"kimi-for-coding-highspeed": { input: 1.9, output: 8, cacheRead: 0.38, cacheWrite: 0 },
"kimi-k2-thinking": { input: 0.6, output: 2.5, cacheRead: 0.15, cacheWrite: 0 },
};
const OPENROUTER_KIMI_K3_MODEL_IDS = new Set(["moonshotai/kimi-k3", "~moonshotai/kimi-latest"]);
const ANT_LING_RING_THINKING_LEVEL_MAP = {
off: null,
minimal: null,
low: null,
medium: null,
high: "high",
xhigh: "xhigh",
} as const;
const BEDROCK_INFERENCE_PROFILE_ONLY_MODEL_IDS = new Set(["anthropic.claude-opus-5"]);
const MODELS_DEV_OPENAI_UNSUPPORTED_MODEL_IDS = new Set(["gpt-5.6"]);
const OPENAI_TOOL_SEARCH_MODEL_IDS = new Set([
"gpt-5.4",
"gpt-5.4-mini",
"gpt-5.4-pro",
"gpt-5.5",
"gpt-5.6-sol",
"gpt-5.6-terra",
"gpt-5.6-luna",
"gpt-6-astra",
]);
// Public OpenAI documents additional_tools for applications that load tools
// outside the normal tool-search flow. Codex currently uses the input item for
// its Responses Lite GPT-5.6 models.
// https://developers.openai.com/api/docs/guides/tools-tool-search#add-tools-at-a-specific-point-in-the-input
const OPENAI_ADDITIONAL_TOOLS_MODEL_IDS = OPENAI_TOOL_SEARCH_MODEL_IDS;
const OPENAI_CODEX_ADDITIONAL_TOOLS_MODEL_IDS = new Set([
"gpt-5.6-sol",
"gpt-5.6-terra",
"gpt-5.6-luna",
"gpt-6-astra",
]);
const OPENAI_LONG_CONTEXT_INPUT_THRESHOLD = 272000;
const OPENAI_SHORT_CONTEXT_CAPPED_MODEL_IDS = new Set([
"gpt-5.4",
"gpt-5.5",
"gpt-5.6-sol",
"gpt-5.6-terra",
"gpt-5.6-luna",
"gpt-6-astra",
]);
const OPENAI_LONG_CONTEXT_PRICING_MODEL_IDS = new Set([
"gpt-5.4",
"gpt-5.4-pro",
"gpt-5.5",
"gpt-5.5-pro",
"gpt-5.6-sol",
"gpt-5.6-terra",
"gpt-5.6-luna",
"gpt-6-astra",
]);
function withOpenAiLongContextPricing(cost: Model<Api>["cost"]): Model<Api>["cost"] {
return {
...cost,
tiers: [
{
inputTokensAbove: OPENAI_LONG_CONTEXT_INPUT_THRESHOLD,
input: roundCost(cost.input * 2),
output: roundCost(cost.output * 1.5),
cacheRead: roundCost(cost.cacheRead * 2),
cacheWrite: roundCost(cost.cacheWrite * 2),
},
],
};
}
// OpenAI reduced GPT-5.6 Terra and Luna prices on 2026-07-30. Keep these
// authoritative values until models.dev and passthrough catalogs catch up.
// https://developers.openai.com/api/docs/pricing
const OPENAI_GPT_56_STANDARD_COSTS: Record<string, ModelCost> = {
"gpt-5.6-luna": { input: 0.2, output: 1.2, cacheRead: 0.02, cacheWrite: 0.25 },
"gpt-5.6-terra": { input: 2, output: 12, cacheRead: 0.2, cacheWrite: 2.5 },
};
const OPENAI_RESPONSES_NONE_REASONING_MODELS = new Set([
"gpt-5.1",
"gpt-5.2",
"gpt-5.3-codex",
"gpt-5.4",
"gpt-5.4-mini",
"gpt-5.4-nano",
"gpt-5.5",
"gpt-5.6-sol",
"gpt-5.6-terra",
"gpt-5.6-luna",
]);
const XAI_BUILTIN_EXCLUDED_MODEL_IDS = new Set([
"grok-3",
"grok-3-fast",
"grok-4.20-0309-non-reasoning",
"grok-4.20-0309-reasoning",
"grok-build-0.1",
"grok-code-fast-1",
]);
const XAI_RESPONSES_COMPAT: OpenAIResponsesCompat = {
supportsLongCacheRetention: false,
};
const OPENCODE_OPENAI_COMPLETIONS_LONG_CACHE_RETENTION_UNSUPPORTED_MODELS = new Set([
"opencode:deepseek-v4-flash",
"opencode:deepseek-v4-pro",
"opencode:kimi-k2.5",
"opencode:kimi-k2.6",
"opencode:minimax-m2.7",
"opencode-go:kimi-k2.6",
]);
// GitHub's "Models with extended capabilities" table lists these Copilot models as supporting
// the extended 1 million token context window.
const GITHUB_COPILOT_EXTENDED_CONTEXT_MODELS = new Set([
"claude-fable-5",
"claude-opus-4.6",
"claude-opus-4.7",
"claude-opus-4.8",
"claude-opus-5",
"claude-sonnet-4.6",
"claude-sonnet-5",
"gpt-5.3-codex",
"gpt-5.4",
"gpt-5.5",
]);
// Checked manually against the authenticated GitHub Copilot /models endpoint on 2026-06-15.
// Keep this to narrow corrections over models.dev metadata instead of snapshotting Copilot's catalog.
const GITHUB_COPILOT_THINKING_LEVEL_OVERRIDES = {
"claude-opus-4.7": { minimal: "low" },
"claude-opus-4.8": { minimal: "low" },
"claude-opus-5": { minimal: "low" },
"claude-sonnet-4.6": { minimal: "low", max: "max" },
} satisfies Record<string, NonNullable<Model<Api>["thinkingLevelMap"]>>;
function mergeThinkingLevelMap(model: Model<any>, map: NonNullable<Model<any>["thinkingLevelMap"]>): void {
model.thinkingLevelMap = { ...model.thinkingLevelMap, ...map };
}
const modelsDevReasoningOptions = new Map<string, ModelsDevReasoningOption[]>();
function getModelKey(model: Pick<Model<Api>, "provider" | "id">): string {
return `${model.provider}:${model.id}`;
}
function recordModelsDevReasoningOptions(provider: string, id: string, sourceModel: ModelsDevModel): void {
if (sourceModel.reasoning_options !== undefined) {
modelsDevReasoningOptions.set(`${provider}:${id}`, sourceModel.reasoning_options);
}
}
function supportsDirectReasoningEffort(model: Model<Api>): boolean {
if (model.api === "anthropic-messages") return model.compat?.forceAdaptiveThinking === true;
if (
model.api === "openai-responses" ||
model.api === "azure-openai-responses" ||
model.api === "openai-codex-responses"
) {
return true;
}
if (model.api !== "openai-completions") return false;
const compat = {
...detectOpenAICompletionsCompat(model as Model<"openai-completions">),
...(model.compat as OpenAICompletionsCompat | undefined),
};
return compat.thinkingFormat === "openai" && compat.supportsReasoningEffort;
}
function applyModelsDevReasoningOptionMetadata(model: Model<Api>): void {
const reasoningOptions = modelsDevReasoningOptions.get(getModelKey(model));
if (!reasoningOptions || !supportsDirectReasoningEffort(model)) return;
const thinkingLevelMap = getEffortThinkingLevelMap(reasoningOptions);
if (thinkingLevelMap) mergeThinkingLevelMap(model, thinkingLevelMap);
}
function getTogetherCompat(modelId: string, reasoning: boolean): OpenAICompletionsCompat {
if (!reasoning) return TOGETHER_BASE_COMPAT;
if (TOGETHER_REASONING_EFFORT_MODELS.has(modelId)) return TOGETHER_REASONING_EFFORT_COMPAT;
if (TOGETHER_TOGGLE_REASONING_EFFORT_MODELS.has(modelId)) return TOGETHER_TOGGLE_REASONING_EFFORT_COMPAT;
if (TOGETHER_REASONING_ONLY_MODELS.has(modelId)) return TOGETHER_BASE_COMPAT;
return TOGETHER_TOGGLE_REASONING_COMPAT;
}
function getTogetherThinkingLevelMap(
modelId: string,
reasoning: boolean,
): NonNullable<Model<any>["thinkingLevelMap"]> | undefined {
if (!reasoning) return undefined;
if (TOGETHER_REASONING_EFFORT_MODELS.has(modelId)) return { ...TOGETHER_REASONING_EFFORT_LEVEL_MAP };
if (TOGETHER_TOGGLE_REASONING_EFFORT_MODELS.has(modelId)) return { ...TOGETHER_DEEPSEEK_V4_THINKING_LEVEL_MAP };
if (TOGETHER_REASONING_ONLY_MODELS.has(modelId)) return { ...TOGETHER_FIXED_REASONING_LEVEL_MAP };
return { ...TOGETHER_TOGGLE_REASONING_LEVEL_MAP };
}
function supportsOpenAiXhigh(modelId: string): boolean {
return (
modelId.includes("gpt-5.2") ||
modelId.includes("gpt-5.3") ||
modelId.includes("gpt-5.4") ||
modelId.includes("gpt-5.5") ||
modelId.includes("gpt-5.6") ||
modelId.includes("gpt-6-astra")
);
}
function supportsOpenAiMax(model: Model<Api>): boolean {
return (
(model.id.includes("gpt-5.6") || model.id.includes("gpt-6-astra")) &&
(model.api === "openai-responses" ||
model.api === "azure-openai-responses" ||
model.api === "openai-codex-responses" ||
model.api === "openai-completions")
);
}
function isGoogleThinkingApi(model: Model<any>): boolean {
return model.api === "google-generative-ai" || model.api === "google-vertex";
}
const VERIFIED_ANTHROPIC_MID_CONVO_EFFORT_PROVIDERS = new Set(["anthropic", "openrouter"]);
function supportsAnthropicMidConvoEffort(modelId: string): boolean {
const id = modelId.toLowerCase().replace(/^~?anthropic\//, "");
return (
/^claude-opus-5(?:-\d{8})?$/.test(id) ||
/^claude-(?:fable|mythos)-5(?:[.-]1)(?:-\d{8})?$/.test(id)
);
}
function isAnthropicAdaptiveThinkingModel(modelId: string): boolean {
return (
modelId.includes("opus-4-6") ||
modelId.includes("opus-4.6") ||
modelId.includes("opus-4-7") ||
modelId.includes("opus-4.7") ||
modelId.includes("opus-4-8") ||
modelId.includes("opus-4.8") ||
modelId.includes("opus-5") ||
modelId.includes("opus.5") ||
modelId.includes("sonnet-4-6") ||
modelId.includes("sonnet-4.6") ||
modelId.includes("sonnet-5") ||
modelId.includes("sonnet.5") ||
modelId.includes("fable-5") ||
modelId.includes("mythos-5")
);
}
function isAnthropicTemperatureUnsupportedModel(modelId: string): boolean {
const id = modelId.toLowerCase();
return (
id.includes("opus-4-7") ||
id.includes("opus-4.7") ||
id.includes("opus-4-8") ||
id.includes("opus-4.8") ||
id.includes("opus-5") ||
id.includes("opus.5")
);
}
const OPENAI_COMPLETIONS_DEFAULT_COMPAT = {
supportsStore: true,
supportsDeveloperRole: true,
supportsReasoningEffort: true,
supportsUsageInStreaming: true,
supportsFinishReason: true,
maxTokensField: "max_completion_tokens",
requiresToolResultName: false,
requiresAssistantAfterToolResult: false,
requiresThinkingAsText: false,
requiresReasoningContentOnAssistantMessages: false,
thinkingFormat: "openai",
openRouterRouting: {},
vercelGatewayRouting: {},
chatTemplateKwargs: {},
chatTemplateArgs: {},
zaiToolStream: false,
supportsStrictMode: true,
supportsOpenAIGrammarTools: false,
sendSessionAffinityHeaders: false,
supportsLongCacheRetention: true,
} satisfies Required<
Omit<
OpenAICompletionsCompat,
"cacheControlFormat" | "deferredToolsMode" | "supportsThinkingTokenBudget" | "thinkingTokenBudgetField"
>
> & {
cacheControlFormat?: OpenAICompletionsCompat["cacheControlFormat"];
deferredToolsMode?: OpenAICompletionsCompat["deferredToolsMode"];
};
type OpenAICompletionsResolvedCompat = typeof OPENAI_COMPLETIONS_DEFAULT_COMPAT & {
cacheControlFormat?: OpenAICompletionsCompat["cacheControlFormat"];
};
function mergeAnthropicMessagesCompat(model: Model<Api>, compat: AnthropicMessagesCompat): void {
model.compat = { ...(model.compat as AnthropicMessagesCompat | undefined), ...compat };
}
function detectOpenAICompletionsCompat(model: Model<"openai-completions">): OpenAICompletionsResolvedCompat {
const provider = model.provider;
const baseUrl = model.baseUrl;
const isZai =
provider === "zai" ||
provider === "zai-coding-cn" ||
baseUrl.includes("api.z.ai") ||
baseUrl.includes("open.bigmodel.cn");
const isTogether =
provider === "together" || baseUrl.includes("api.together.ai") || baseUrl.includes("api.together.xyz");
const isMoonshot = provider === "moonshotai" || provider === "moonshotai-cn" || baseUrl.includes("api.moonshot.");
const isOpenRouter = provider === "openrouter" || baseUrl.includes("openrouter.ai");
const isCloudflareWorkersAI = provider === "cloudflare-workers-ai" || baseUrl.includes("api.cloudflare.com");
const isCloudflareAiGateway = provider === "cloudflare-ai-gateway" || baseUrl.includes("gateway.ai.cloudflare.com");
const isNvidia = provider === "nvidia" || baseUrl.includes("integrate.api.nvidia.com");
const isAntLing = provider === "ant-ling" || baseUrl.includes("api.ant-ling.com");
const isTogetherReasoningOnly = isTogether && TOGETHER_REASONING_ONLY_MODELS.has(model.id);
const isDeepSeek = provider === "deepseek" || baseUrl.toLowerCase().includes("deepseek.com");
const isNonStandard =
isNvidia ||
provider === "cerebras" ||
baseUrl.includes("cerebras.ai") ||
provider === "xai" ||
baseUrl.includes("api.x.ai") ||
isTogether ||
baseUrl.includes("chutes.ai") ||
isDeepSeek ||
isZai ||
isMoonshot ||
provider === "opencode" ||
baseUrl.includes("opencode.ai") ||
isCloudflareWorkersAI ||
isCloudflareAiGateway ||
isAntLing;
const useMaxTokens =
baseUrl.includes("chutes.ai") ||
isDeepSeek ||
isMoonshot ||
isCloudflareAiGateway ||
isTogether ||
isNvidia ||
isAntLing ||
isZai;
const isGrok = provider === "xai" || baseUrl.includes("api.x.ai");
const isOpenRouterDeveloperRoleModel =
isOpenRouter && (model.id.startsWith("anthropic/") || model.id.startsWith("openai/"));
const cacheControlFormat =
provider === "openrouter" && /^~?anthropic\//.test(model.id) ? "anthropic" : undefined;
return {
supportsStore: !isNonStandard,
supportsDeveloperRole: isOpenRouterDeveloperRoleModel || (!isNonStandard && !isOpenRouter),
supportsReasoningEffort:
!isGrok && !isZai && !isMoonshot && !isTogether && !isCloudflareAiGateway && !isNvidia && !isAntLing,
supportsUsageInStreaming: true,
supportsFinishReason: true,
maxTokensField: useMaxTokens ? "max_tokens" : "max_completion_tokens",
requiresToolResultName: false,
requiresAssistantAfterToolResult: false,
requiresThinkingAsText: false,
requiresReasoningContentOnAssistantMessages: isDeepSeek,
thinkingFormat: isDeepSeek
? "deepseek"
: isZai
? "zai"
: isTogether && !isTogetherReasoningOnly
? "together"
: isAntLing
? "ant-ling"
: isOpenRouter
? "openrouter"
: "openai",
openRouterRouting: {},
vercelGatewayRouting: {},
chatTemplateKwargs: {},
chatTemplateArgs: {},
zaiToolStream: false,
supportsStrictMode: !isMoonshot && !isTogether && !isCloudflareAiGateway && !isNvidia,
supportsOpenAIGrammarTools: false,
...(cacheControlFormat ? { cacheControlFormat } : {}),
sendSessionAffinityHeaders: isOpenRouter,
supportsLongCacheRetention: !(
isTogether ||
isCloudflareWorkersAI ||
isCloudflareAiGateway ||
isNvidia ||
isAntLing
),
};
}
function isPlainEmptyObject(value: unknown): boolean {
return typeof value === "object" && value !== null && !Array.isArray(value) && Object.keys(value).length === 0;
}
function openAICompletionsCompatDelta(compat: OpenAICompletionsResolvedCompat): OpenAICompletionsCompat {
const delta: OpenAICompletionsCompat = {};
for (const [key, value] of Object.entries(compat)) {
const defaultValue = OPENAI_COMPLETIONS_DEFAULT_COMPAT[key as keyof typeof OPENAI_COMPLETIONS_DEFAULT_COMPAT];
if (isPlainEmptyObject(value) && isPlainEmptyObject(defaultValue)) continue;
if (value !== defaultValue) {
(delta as Record<string, unknown>)[key] = value;
}
}
return delta;
}
function mergeOpenAICompletionsCompat(model: Model<Api>, compat: OpenAICompletionsCompat): void {
model.compat = { ...(model.compat as OpenAICompletionsCompat | undefined), ...compat };
}
function applyOpenAICompletionsCompatMetadata(model: Model<Api>): void {
if (model.api !== "openai-completions") return;
const detected = openAICompletionsCompatDelta(detectOpenAICompletionsCompat(model as Model<"openai-completions">));
model.compat = { ...detected, ...(model.compat as OpenAICompletionsCompat | undefined) };
if (Object.keys(model.compat).length === 0) {
delete model.compat;
}
}
function applyAnthropicMessagesCompatMetadata(model: Model<Api>): void {
if (model.api !== "anthropic-messages") return;
const compat = getAnthropicMessagesCompat(model.provider, model.id);
if (compat) {
mergeAnthropicMessagesCompat(model, compat);
if (compat.supportsMidConvoEffort) mergeThinkingLevelMap(model, { off: null });
}
}
function isAnthropicFallbackMetadataModel(model: Model<Api>): model is Model<"anthropic-messages"> {
if (model.provider !== "anthropic" || model.api !== "anthropic-messages") return false;
return (
model.id in ANTHROPIC_ALLOWED_FALLBACK_MODELS ||
Object.values(ANTHROPIC_ALLOWED_FALLBACK_MODELS).some((fallbackModelIds) => fallbackModelIds.includes(model.id))
);
}
function applyAnthropicAllowedFallbackModelMetadata(models: readonly Model<"anthropic-messages">[]): void {
const modelsById = new Map(models.map((model) => [model.id, model]));
for (const [modelId, fallbackModelIds] of Object.entries(ANTHROPIC_ALLOWED_FALLBACK_MODELS)) {
const model = modelsById.get(modelId);
if (!model) continue;
const compatibleFallbackModelIds = model.compat?.supportsMidConvoEffort
? fallbackModelIds.filter(supportsAnthropicMidConvoEffort)
: fallbackModelIds;
const allowedFallbackModels = compatibleFallbackModelIds.flatMap((fallbackModelId) => {
const fallbackModel = modelsById.get(fallbackModelId);
return fallbackModel
? [{ provider: fallbackModel.provider, model: fallbackModel.id, cost: fallbackModel.cost }]
: [];
});
if (allowedFallbackModels.length > 0) {
mergeAnthropicMessagesCompat(model, { allowedFallbackModels });
}
}
}
function applyStrictToolCompatMetadata(model: Model<Api>): void {
if (
(model.provider === "openai" || model.provider === "cloudflare-ai-gateway") &&
model.api === "openai-responses"
) {
model.compat = { ...(model.compat as OpenAIResponsesCompat | undefined), supportsStrictMode: true };
} else if (model.provider === "anthropic" && model.api === "anthropic-messages") {
mergeAnthropicMessagesCompat(model, { supportsStrictTools: true });
}
}
// Responses endpoints verified (OpenAI, ChatGPT Codex backend, GitHub Copilot,
// opencode zen) or documented (Azure OpenAI, Cloudflare AI Gateway) to pass
// OpenAI custom grammar tools through. OpenAI rejects `type: "custom"` tools
// for pre-GPT-5 models (gpt-4.x, gpt-4o, o-series).
const OPENAI_GRAMMAR_TOOL_PROVIDERS = new Set([
"openai",
"openai-codex",
"azure-openai-responses",
"github-copilot",
"opencode",
"cloudflare-ai-gateway",
]);
const OPENAI_GRAMMAR_TOOL_APIS = new Set<Api>([
"openai-responses",
"azure-openai-responses",
"openai-codex-responses",
]);
function applyOpenAIGrammarToolCompatMetadata(model: Model<Api>): void {
if (!OPENAI_GRAMMAR_TOOL_APIS.has(model.api) || !OPENAI_GRAMMAR_TOOL_PROVIDERS.has(model.provider)) return;
const match = /^gpt-(\d+)/.exec(model.id);
if (!match || Number(match[1]) < 5) return;
model.compat = { ...(model.compat as OpenAIResponsesCompat | undefined), supportsOpenAIGrammarTools: true };
}
function applyOpenAIToolSearchMetadata(model: Model<Api>): void {
const isOpenAIResponses = model.provider === "openai" && model.api === "openai-responses";
const isOpenAICodex = model.provider === "openai-codex" && model.api === "openai-codex-responses";
if (!(isOpenAIResponses || isOpenAICodex) || !OPENAI_TOOL_SEARCH_MODEL_IDS.has(model.id)) return;
const supportsAdditionalTools =
(isOpenAIResponses && OPENAI_ADDITIONAL_TOOLS_MODEL_IDS.has(model.id)) ||
(isOpenAICodex && OPENAI_CODEX_ADDITIONAL_TOOLS_MODEL_IDS.has(model.id));
model.compat = {
...(model.compat as OpenAIResponsesCompat | undefined),
...(supportsAdditionalTools ? { supportsAdditionalTools: true } : {}),
supportsToolSearch: true,
};
}
// OpenAI charges prompt-cache writes starting with the GPT-5.6 family, and exactly
// those models accept `prompt_cache_options`; older models reject the parameter.
// https://developers.openai.com/api/docs/guides/prompt-caching
function applyOpenAIExplicitPromptCacheMetadata(model: Model<Api>): void {
if (model.provider !== "openai" || model.api !== "openai-responses") return;
if (!(model.cost.cacheWrite > 0)) return;
model.compat = {
...(model.compat as OpenAIResponsesCompat | undefined),
supportsExplicitPromptCacheMode: true,
};
}
function isGemini3ProModel(modelId: string): boolean {
return /gemini-3(?:\.\d+)?-pro/.test(modelId.toLowerCase());
}
function isGemini3FlashModel(modelId: string): boolean {
const id = modelId.toLowerCase();
return /gemini-3(?:\.\d+)?-flash/.test(id) || id === "gemini-flash-latest" || id === "gemini-flash-lite-latest";
}
function isGemma4Model(modelId: string): boolean {
return /gemma-?4/.test(modelId.toLowerCase());
}
function applyThinkingLevelMetadata(model: Model<any>): void {
if (
(model.api === "openai-responses" || model.api === "azure-openai-responses") &&
model.id.startsWith("gpt-5")
) {
mergeThinkingLevelMap(model, { off: null });
}
if (
model.id === "gpt-6-astra" &&
(model.api === "openai-responses" ||
model.api === "azure-openai-responses" ||
model.api === "openai-codex-responses")
) {
mergeThinkingLevelMap(model, {
off: null,
minimal: null,
low: "low",
medium: "medium",
high: "high",
xhigh: "xhigh",
max: "max",
});
}
if (model.provider === "github-copilot" && model.id.startsWith("gpt-5")) {
mergeThinkingLevelMap(model, { minimal: "low" });
}
if (
model.api === "openai-responses" &&
model.provider === "openai" &&
OPENAI_RESPONSES_NONE_REASONING_MODELS.has(model.id)
) {
mergeThinkingLevelMap(model, { off: "none" });
}
// xAI models without verified effort options must not send the undocumented
// "none"/"minimal" efforts.
if (model.provider === "xai" && model.api === "openai-responses" && model.thinkingLevelMap === undefined) {
mergeThinkingLevelMap(model, { off: null, minimal: null });
}
if (supportsOpenAiXhigh(model.id)) {
mergeThinkingLevelMap(model, { xhigh: "xhigh" });
}
if (supportsOpenAiMax(model)) {
mergeThinkingLevelMap(model, { max: "max" });
}
if (model.provider === "openai" && model.id === "gpt-5.5") {
mergeThinkingLevelMap(model, { minimal: null });
}
if (model.id.endsWith("gpt-5.5-pro")) {
mergeThinkingLevelMap(model, { off: null, minimal: null, low: null });
}
// Anthropic adaptive-thinking effort support (per Anthropic adaptive thinking docs):
// - "max" is available on all adaptive-thinking Claude models.
// - "xhigh" is only available on Opus 4.7/4.8/5, Sonnet 5, and Fable 5.
if (
model.id.includes("opus-4-6") ||
model.id.includes("opus-4.6") ||
model.id.includes("sonnet-4-6") ||
model.id.includes("sonnet-4.6")
) {
mergeThinkingLevelMap(model, { max: "max" });
}
if (
model.id.includes("opus-4-7") ||
model.id.includes("opus-4.7") ||
model.id.includes("opus-4-8") ||
model.id.includes("opus-4.8") ||
model.id.includes("opus-5") ||
model.id.includes("opus.5") ||
model.id.includes("sonnet-5") ||
model.id.includes("sonnet.5")
) {
mergeThinkingLevelMap(model, { xhigh: "xhigh", max: "max" });
}
if (model.id.includes("fable-5")) {
mergeThinkingLevelMap(model, { off: null, xhigh: "xhigh", max: "max" });
}
if (model.api === "anthropic-messages" && isAnthropicAdaptiveThinkingModel(model.id)) {
mergeAnthropicMessagesCompat(model, { forceAdaptiveThinking: true });
}
if (model.api === "anthropic-messages" && isAnthropicTemperatureUnsupportedModel(model.id)) {
mergeAnthropicMessagesCompat(model, { supportsTemperature: false });
}
if (model.api === "openai-completions" && model.id.includes("deepseek-v4")) {
mergeThinkingLevelMap(
model,
model.provider === "openrouter"
? { ...DEEPSEEK_V4_THINKING_LEVEL_MAP, xhigh: "xhigh", max: null }
: (model.provider === "deepseek" || model.provider === "opencode" || model.provider === "opencode-go") &&
model.id.includes("deepseek-v4-flash")
? DEEPSEEK_V4_FLASH_THINKING_LEVEL_MAP
: DEEPSEEK_V4_THINKING_LEVEL_MAP,
);
}
if (isGoogleThinkingApi(model) && isGemini3ProModel(model.id)) {
mergeThinkingLevelMap(model, { off: null, minimal: null, low: "LOW", medium: null, high: "HIGH" });
}
if (isGoogleThinkingApi(model) && isGemini3FlashModel(model.id)) {
mergeThinkingLevelMap(model, { off: null });
}
if (isGoogleThinkingApi(model) && isGemma4Model(model.id)) {
mergeThinkingLevelMap(model, { off: null, minimal: "MINIMAL", low: null, medium: null, high: "HIGH" });
}
if (model.provider === "groq" && model.id === "qwen/qwen3.6-27b") {
mergeThinkingLevelMap(model, { minimal: null, low: null, medium: null, high: "default" });
}
if (model.provider === "openai-codex" && supportsOpenAiXhigh(model.id)) {
mergeThinkingLevelMap(model, { minimal: "low" });
}
if (
(model.provider === "moonshotai" || model.provider === "moonshotai-cn") &&
(model.id === "kimi-k2.7-code" || model.id === "kimi-k2.7-code-highspeed")
) {
// Kimi K2.7 Code is always-thinking. Official docs say
// `thinking: { type: "disabled" }` is rejected, and callers can omit
// the thinking parameter to use the enabled default.
mergeThinkingLevelMap(model, { off: null });
}
if (model.provider === "openrouter" && model.id.startsWith("inception/mercury-2")) {
// Mercury 2 in instant mode (reasoning_effort: "none") disables tool calling.
// Mark "off" unsupported so the openai-completions provider omits the reasoning param
// instead of defaulting to {reasoning:{effort:"none"}} (see openai-completions.ts:575).
// Pi's low/medium/high pass through verbatim; OpenRouter normalizes to Mercury's vocabulary.
mergeThinkingLevelMap(model, { off: null });
}
if (model.provider === "openrouter" && model.id === "z-ai/glm-5.2") {
mergeThinkingLevelMap(model, { xhigh: "xhigh" });
}
if (model.provider === "fireworks") {
if (model.api === "anthropic-messages" && model.compat?.forceAdaptiveThinking) {
// Qwen Max currently advertises only a toggle. Prefer upstream effort
// metadata once available instead of replacing it with this fallback.
if (model.id === "accounts/fireworks/models/qwen3p8-max" && !model.thinkingLevelMap) {
model.thinkingLevelMap = getEffortThinkingLevelMap([
{ type: "effort", values: ["low", "medium", "xhigh"] },
]);
}
const reasoningOptions = modelsDevReasoningOptions.get(getModelKey(model));
if (
reasoningOptions?.some((option) => option.type === "toggle") ||
// The 2.4T alias omits the verified toggle in models.dev.
model.id === "accounts/fireworks/models/qwen3p8-2p4t-a95b"
) {
mergeThinkingLevelMap(model, { off: "none" });
}
if (model.id === "accounts/fireworks/models/deepseek-v4-pro-0813") {
mergeThinkingLevelMap(model, { low: "low" });
}
}
if (model.id.includes("glm-5p2")) {
// GLM 5.2 and its fast router support off/high/max. Fireworks maps low
// and medium to high, so do not expose those aliases as distinct levels.
mergeThinkingLevelMap(model, { off: "none", minimal: null, low: null, medium: null, max: "max" });
}
if (model.id.includes("kimi-k3")) {
// Fireworks maps medium to high on both APIs; do not expose it as a distinct level.
mergeThinkingLevelMap(model, { medium: null });
}
}
if (model.provider === "opencode-go" && model.id === "glm-5.2") {
mergeThinkingLevelMap(model, OPENCODE_GO_GLM52_THINKING_LEVEL_MAP);
}
if (model.provider === "opencode-go" && model.id === "kimi-k2.6") {
// OpenCode Go exposes Kimi K2.6 thinking as on/off, not distinct effort tiers.
mergeThinkingLevelMap(model, { minimal: null, low: null, medium: null });
}
if (model.provider === "opencode" && model.id === "grok-build-0.1") {
// OpenCode Zen Grok Build reasons by default but rejects explicit reasoningEffort.
mergeThinkingLevelMap(model, { off: null, minimal: null, low: null, medium: null });
}
if (model.provider === "ant-ling" && model.reasoning) {
// Ring reasons by default. Only high/xhigh have documented explicit effort controls.
mergeThinkingLevelMap(model, ANT_LING_RING_THINKING_LEVEL_MAP);
}
if (model.provider === "github-copilot") {
const override = GITHUB_COPILOT_THINKING_LEVEL_OVERRIDES[model.id];
if (override) {
mergeThinkingLevelMap(model, override);
}
}
}
function getAnthropicMessagesCompat(provider: string, modelId: string): AnthropicMessagesCompat | undefined {
const compat: AnthropicMessagesCompat = {};
if (
VERIFIED_ANTHROPIC_MID_CONVO_EFFORT_PROVIDERS.has(provider) &&
supportsAnthropicMidConvoEffort(modelId)
) {
compat.supportsMidConvoEffort = true;
}
if (EAGER_TOOL_INPUT_STREAMING_UNSUPPORTED_ANTHROPIC_MODELS.has(`${provider}:${modelId}`)) {
compat.supportsEagerToolInputStreaming = false;
}
if (provider === "xiaomi" || provider.startsWith("xiaomi-token-plan-")) {
compat.allowEmptySignature = true;
}
return Object.keys(compat).length > 0 ? compat : undefined;
}
function getBedrockBaseUrl(modelId: string): string {
return modelId.startsWith("eu.")
? "https://bedrock-runtime.eu-central-1.amazonaws.com"
: "https://bedrock-runtime.us-east-1.amazonaws.com";
}
function normalizeNvidiaModelId(modelId: string): string {
return modelId.toLowerCase().replaceAll("_", ".");
}
function roundCost(value: number): number {
return Number(value.toFixed(6));
}
function getModelsDevCost(cost: ModelsDevModel["cost"]): ModelCost {
const tiers = cost?.tiers?.flatMap((tier) => {
const context = tier.tier;
if (context?.type !== "context" || context.size === undefined) return [];
return [
{
inputTokensAbove: context.size,
input: tier.input || 0,
output: tier.output || 0,
cacheRead: tier.cache_read || 0,
cacheWrite: tier.cache_write || 0,
},
];
});
return {
input: cost?.input || 0,
output: cost?.output || 0,
cacheRead: cost?.cache_read || 0,
cacheWrite: cost?.cache_write || 0,
...(tiers && tiers.length > 0 ? { tiers } : {}),
};
}
async function fetchNvidiaNimModelIds(): Promise<Map<string, string>> {
try {
console.log("Fetching models from NVIDIA NIM API...");
const response = await fetch(`${NVIDIA_BASE_URL}/models`);
if (!response.ok) throw new Error(`NVIDIA NIM API returned ${response.status}`);
const data = (await response.json()) as { data?: NvidiaNimModelListItem[] };
const modelIds = new Map<string, string>();
for (const model of data.data ?? []) {
modelIds.set(model.id, model.id);
modelIds.set(normalizeNvidiaModelId(model.id), model.id);
}
console.log(`Fetched ${data.data?.length ?? 0} model IDs from NVIDIA NIM`);
return modelIds;
} catch (error) {
console.error("Failed to fetch NVIDIA NIM models:", error);
if (generatorOptions.strict) throw error;
return new Map();
}
}
async function fetchOpenRouterModels(): Promise<Model<any>[]> {
try {
console.log("Fetching models from OpenRouter API...");
const response = await fetch("https://openrouter.ai/api/v1/models");
if (!response.ok) throw new Error(`OpenRouter API returned ${response.status}`);
const data = (await response.json()) as { data?: OpenRouterModelListItem[] };
const models: Model<any>[] = [];
for (const model of data.data ?? []) {
// Only include models that support tools
if (!model.supported_parameters?.includes("tools")) continue;
// Parse provider from model ID
let provider: KnownProvider = "openrouter";
let modelKey = model.id;
modelKey = model.id; // Keep full ID for OpenRouter
// Parse input modalities
const input: ("text" | "image")[] = ["text"];
if (model.architecture?.modality?.includes("image")) {
input.push("image");
}
// Convert pricing from $/token to $/million tokens
const inputCost = roundCost(parseFloat(model.pricing?.prompt || "0") * 1_000_000);
const outputCost = roundCost(parseFloat(model.pricing?.completion || "0") * 1_000_000);
const cacheReadCost = roundCost(parseFloat(model.pricing?.input_cache_read || "0") * 1_000_000);
const cacheWriteCost = roundCost(parseFloat(model.pricing?.input_cache_write || "0") * 1_000_000);
const contextWindow = model.top_provider?.context_length || model.context_length || 4096;
const thinkingLevelMap = getOpenRouterThinkingLevelMap(model.reasoning);
const useAnthropicMessages = /^anthropic\//.test(modelKey) && !modelKey.endsWith(":batch");
const normalizedModel: Model<any> = {
id: modelKey,
name: model.name,
api: useAnthropicMessages ? "anthropic-messages" : "openai-completions",
baseUrl: useAnthropicMessages ? "https://openrouter.ai/api" : "https://openrouter.ai/api/v1",
provider,
reasoning: model.supported_parameters?.includes("reasoning") || false,
...(thinkingLevelMap && { thinkingLevelMap }),
input,
cost: {
input: inputCost,
output: outputCost,
cacheRead: cacheReadCost,
cacheWrite: cacheWriteCost,
},
contextWindow,
maxTokens: model.top_provider?.max_completion_tokens || 4096,
};
models.push(normalizedModel);
}
console.log(`Fetched ${models.length} tool-capable models from OpenRouter`);
return models;
} catch (error) {
console.error("Failed to fetch OpenRouter models:", error);
if (generatorOptions.strict) throw error;
return [];
}
}
async function fetchAiGatewayModels(): Promise<Model<any>[]> {
try {
console.log("Fetching models from Vercel AI Gateway API...");
const response = await fetch(`${AI_GATEWAY_MODELS_URL}/models`);
if (!response.ok) throw new Error(`Vercel AI Gateway API returned ${response.status}`);
const data = await response.json();
const models: Model<any>[] = [];
const toNumber = (value: string | number | undefined): number => {
if (typeof value === "number") {
return Number.isFinite(value) ? value : 0;
}
const parsed = parseFloat(value ?? "0");
return Number.isFinite(parsed) ? parsed : 0;
};
const items = Array.isArray(data.data) ? (data.data as AiGatewayModel[]) : [];
for (const model of items) {
const tags = Array.isArray(model.tags) ? model.tags : [];
// Only include models that support tools
if (!tags.includes("tool-use")) continue;
const input: ("text" | "image")[] = ["text"];
if (tags.includes("vision")) {
input.push("image");
}
const inputCost = roundCost(toNumber(model.pricing?.input) * 1_000_000);
const outputCost = roundCost(toNumber(model.pricing?.output) * 1_000_000);
const cacheReadCost = roundCost(toNumber(model.pricing?.input_cache_read) * 1_000_000);
const cacheWriteCost = roundCost(toNumber(model.pricing?.input_cache_write) * 1_000_000);
models.push({
id: model.id,
name: model.name || model.id,
api: "anthropic-messages",
baseUrl: AI_GATEWAY_BASE_URL,
provider: "vercel-ai-gateway",
reasoning: tags.includes("reasoning"),
input,
cost: {
input: inputCost,
output: outputCost,
cacheRead: cacheReadCost,
cacheWrite: cacheWriteCost,
},
contextWindow: model.context_window || 4096,
maxTokens: model.max_tokens || 4096,
});
}
console.log(`Fetched ${models.length} tool-capable models from Vercel AI Gateway`);
return models;
} catch (error) {
console.error("Failed to fetch Vercel AI Gateway models:", error);
if (generatorOptions.strict) throw error;
return [];
}
}
function processZaiModels(data: ModelsDevCatalog): Model<Api>[] {
const variants = [
{
source: "zai-coding-plan",
provider: "zai",
baseUrl: "https://api.z.ai/api/coding/paas/v4",
},
{
source: "zhipuai-coding-plan",
provider: "zai-coding-cn",
baseUrl: "https://open.bigmodel.cn/api/coding/paas/v4",
},
] as const;
const models: Model<Api>[] = [];
for (const { source, provider, baseUrl } of variants) {
for (const [modelId, model] of Object.entries(data[source]?.models ?? {})) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
const supportsImage = m.modalities?.input?.includes("image");
const thinkingLevelMap = getEffortThinkingLevelMap(m.reasoning_options ?? []);
const isGlm52 = modelId === "glm-5.2" || modelId === "glm-5.2-highspeed";
if (thinkingLevelMap && isGlm52) {
thinkingLevelMap.off = "none";
}
const supportsReasoningEffort = thinkingLevelMap !== undefined;
const referenceCost = data.zai?.models[modelId]?.cost ?? m.cost;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider,
baseUrl,
reasoning: m.reasoning === true,
...(thinkingLevelMap ? { thinkingLevelMap } : {}),
input: supportsImage ? ["text", "image"] : ["text"],
cost: {
input: referenceCost?.input || 0,
output: referenceCost?.output || 0,
cacheRead: referenceCost?.cache_read || 0,
cacheWrite: referenceCost?.cache_write || 0,
},
compat: {
supportsDeveloperRole: false,
thinkingFormat: "zai",
...(supportsReasoningEffort ? { supportsReasoningEffort: true } : {}),
...(!ZAI_TOOL_STREAM_UNSUPPORTED_MODELS.has(modelId) ? { zaiToolStream: true } : {}),
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions(provider, modelId, m);
}
}
return models;
}
function processBasetenModels(provider: ModelsDevProvider | undefined): Model<Api>[] {
if (!provider?.models) return [];
const baseUrl = "https://inference.baseten.co/v1";
const baseCompat: OpenAICompletionsCompat = {
supportsStore: false,
supportsDeveloperRole: false,
supportsReasoningEffort: false,
supportsUsageInStreaming: true,
maxTokensField: "max_tokens",
supportsStrictMode: true,
supportsLongCacheRetention: false,
};
const reasoningEffortCompat: OpenAICompletionsCompat = {
...baseCompat,
supportsReasoningEffort: true,
thinkingFormat: "openai",
};
const toggleReasoningCompat: OpenAICompletionsCompat = {
...baseCompat,
thinkingFormat: "baseten",
chatTemplateArgs: { enable_thinking: { $var: "thinking.enabled" } },
};
const toggleReasoningEffortCompat: OpenAICompletionsCompat = {
...reasoningEffortCompat,
thinkingFormat: "baseten",
chatTemplateArgs: { enable_thinking: { $var: "thinking.enabled" } },
};
const toggleThinkingLevelMap = {
off: "off",
minimal: null,
low: null,
medium: null,
high: "high",
xhigh: null,
max: null,
} as const;
const glm52ThinkingLevelMap = {
off: "none",
minimal: null,
low: null,
medium: null,
high: "high",
xhigh: null,
max: "max",
} as const;
const models: Model<Api>[] = [];
for (const [modelId, model] of Object.entries(provider.models)) {
if (model.status === "deprecated") continue;
const reasoning = model.reasoning === true;
const reasoningOptions = model.reasoning_options ?? [];
const isGlm52 = modelId === "zai-org/GLM-5.2" || modelId === "zai-org/GLM-5.2-Fast";
const supportsToggle = reasoningOptions.some((option) => option.type === "toggle") || isGlm52;
const supportsEffort = reasoningOptions.some((option) => option.type === "effort") || isGlm52;
const compat =
supportsToggle && supportsEffort
? toggleReasoningEffortCompat
: supportsToggle
? toggleReasoningCompat
: supportsEffort
? reasoningEffortCompat
: baseCompat;
const thinkingLevelMap = isGlm52
? glm52ThinkingLevelMap
: supportsToggle
? toggleThinkingLevelMap
: getEffortThinkingLevelMap(reasoningOptions);
// Baseten's GLM-5.2 endpoints are text-only despite models.dev reporting image input.
const supportsImageInput = !isGlm52 && model.modalities?.input?.includes("image");
models.push({
id: modelId,
name: model.name || modelId,
api: "openai-completions",
provider: "baseten",
baseUrl,
reasoning,
...(thinkingLevelMap ? { thinkingLevelMap } : {}),
input: supportsImageInput ? ["text", "image"] : ["text"],
cost: {
input: model.cost?.input || 0,
output: model.cost?.output || 0,
cacheRead: model.cost?.cache_read || 0,
cacheWrite: model.cost?.cache_write || 0,
},
compat,
contextWindow: model.limit?.context || 4096,
maxTokens: model.limit?.output || 4096,
});
}
return models;
}
function processFireworksModels(provider: ModelsDevProvider | undefined): Model<Api>[] {
if (!provider?.models) return [];
const anthropicCompat: AnthropicMessagesCompat = {
// Arbitrary loader names work, but Fireworks only defers the prefix for ToolSearch/tool_search.
supportsToolReferences: true,
allowEmptySignature: true,
sendSessionAffinityHeaders: true,
supportsEagerToolInputStreaming: false,
supportsCacheControlOnTools: false,
supportsLongCacheRetention: false,
};
const openAICompat: OpenAICompletionsCompat = {
supportsStore: false,
supportsDeveloperRole: false,
sendSessionAffinityHeaders: true,
supportsLongCacheRetention: false,
};
const kimiK3Compat: OpenAICompletionsCompat = {
...openAICompat,
requiresReasoningContentOnAssistantMessages: true,
thinkingFormat: "openai",
deferredToolsMode: "kimi",
};
const models: Model<Api>[] = [];
for (const [modelId, model] of Object.entries(provider.models)) {
if (model.tool_call !== true) continue;
const input: ("text" | "image")[] = model.modalities?.input?.includes("image")
? ["text", "image"]
: ["text"];
const common = {
id: modelId,
name: model.name || modelId,
provider: "fireworks",
reasoning: model.reasoning === true,
input,
cost: {
input: model.cost?.input || 0,
output: model.cost?.output || 0,
cacheRead: model.cost?.cache_read || 0,
cacheWrite: model.cost?.cache_write || 0,
},
contextWindow: model.limit?.context || 4096,
maxTokens: model.limit?.output || 4096,
};
if (modelId.includes("glm-")) {
models.push({
...common,
api: "openai-completions",
baseUrl: "https://api.fireworks.ai/inference/v1",
compat: openAICompat,
});
} else if (modelId.includes("kimi-k3")) {
models.push({
...common,
api: "openai-completions",
baseUrl: "https://api.fireworks.ai/inference/v1",
compat: kimiK3Compat,
});
} else {
models.push({
...common,
api: "anthropic-messages",
// Fireworks Anthropic-compatible API - SDK appends /v1/messages.
baseUrl: "https://api.fireworks.ai/inference",
// Fireworks prompt caching uses automatic prefix matching + session affinity.
// x-session-affinity routes requests to the same replica for cache hits.
// cache_control on tools and eager_input_streaming are not supported.
// See: https://docs.fireworks.ai/tools-sdks/anthropic-compatibility
// Use adaptive thinking for cataloged effort controls, with verified
// fallbacks where models.dev is incomplete. New models need no allowlist entry.
compat: {
...anthropicCompat,
...(model.reasoning_options?.some((option) => option.type === "effort") ||
FIREWORKS_ADAPTIVE_THINKING_FALLBACK_MODELS.has(modelId)
? { forceAdaptiveThinking: true }
: {}),
},
});
}
recordModelsDevReasoningOptions("fireworks", modelId, model);
}
return models;
}
async function loadModelsDevData(): Promise<Model<any>[]> {
try {
console.log("Fetching models from models.dev API...");
const response = await fetch("https://models.dev/api.json");
if (!response.ok) throw new Error(`models.dev API returned ${response.status}`);
const data = (await response.json()) as ModelsDevCatalog;
const models: Model<any>[] = [];
const nvidiaNimModelIds = data.nvidia?.models ? await fetchNvidiaNimModelIds() : new Map<string, string>();
// Process Amazon Bedrock models
if (data["amazon-bedrock"]?.models) {
for (const [modelId, model] of Object.entries(data["amazon-bedrock"].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (BEDROCK_INFERENCE_PROFILE_ONLY_MODEL_IDS.has(modelId)) continue;
let id = modelId;
if (id.startsWith("ai21.jamba")) {
// These models doesn't support tool use in streaming mode
continue;
}
if (id.startsWith("mistral.mistral-7b-instruct-v0")) {
// These models doesn't support system messages
continue;
}
models.push({
id,
name: m.name || id,
api: "bedrock-converse-stream" as const,
provider: "amazon-bedrock" as const,
baseUrl: getBedrockBaseUrl(id),
reasoning: m.reasoning === true,
input: (m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"]) as ("text" | "image")[],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
...(m.structured_output === true && { compat: { supportsStrictMode: true } }),
});
recordModelsDevReasoningOptions("amazon-bedrock" as const, id, m);
}
}
// Process Anthropic models
if (data.anthropic?.models) {
for (const [modelId, model] of Object.entries(data.anthropic.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "anthropic-messages",
provider: "anthropic",
baseUrl: "https://api.anthropic.com",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("anthropic", modelId, m);
}
}
// Process Google models
if (data.google?.models) {
for (const [modelId, model] of Object.entries(data.google.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
let source = m;
if (modelId === "gemini-flash-latest") {
source = (data.google.models["gemini-3.5-flash"] as ModelsDevModel | undefined) ?? m;
}
if (modelId === "gemini-flash-lite-latest") {
source = (data.google.models["gemini-3.1-flash-lite"] as ModelsDevModel | undefined) ?? m;
}
models.push({
id: modelId,
name: m.name || modelId,
api: "google-generative-ai",
provider: "google",
baseUrl: "https://generativelanguage.googleapis.com/v1beta",
reasoning: source.reasoning === true,
input: source.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: source.cost?.input || 0,
output: source.cost?.output || 0,
cacheRead: source.cost?.cache_read || 0,
cacheWrite: source.cost?.cache_write || 0,
},
contextWindow: source.limit?.context || 4096,
maxTokens: source.limit?.output || 4096,
});
recordModelsDevReasoningOptions("google", modelId, source);
}
}
// Process Google Vertex Gemini models. The google-vertex models.dev catalog also includes
// Claude, OpenAI, and other MaaS models that do not use the @google/genai Gemini streaming
// path implemented by our google-vertex provider.
if (data["google-vertex"]?.models) {
for (const [modelId, model] of Object.entries(data["google-vertex"].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (!modelId.startsWith("gemini-")) continue;
if (modelId === "gemini-3.1-flash-lite-preview") continue;
let source = m;
if (modelId === "gemini-flash-latest") {
source = (data["google-vertex"].models["gemini-3.5-flash"] as ModelsDevModel | undefined) ?? m;
}
if (modelId === "gemini-flash-lite-latest") {
source = (data["google-vertex"].models["gemini-3.1-flash-lite"] as ModelsDevModel | undefined) ?? m;
}
// models.dev reports Vertex cache_read/cache_write values for Gemini 2.5 Flash that
// do not match the official Gemini API standard pricing table. pi only accounts
// cachedContentTokenCount as cacheRead.
const cacheRead = modelId === "gemini-2.5-flash" ? 0.03 : source.cost?.cache_read || 0;
models.push({
id: modelId,
name: m.name || modelId,
api: "google-vertex",
provider: "google-vertex",
baseUrl: VERTEX_BASE_URL,
reasoning: source.reasoning === true,
input: source.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: source.cost?.input || 0,
output: source.cost?.output || 0,
cacheRead,
cacheWrite: 0,
},
contextWindow: source.limit?.context || 4096,
maxTokens: source.limit?.output || 4096,
});
recordModelsDevReasoningOptions("google-vertex", modelId, source);
}
}
// Process OpenAI models
if (data.openai?.models) {
for (const [modelId, model] of Object.entries(data.openai.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
// models.dev lists this alias, but it is not accepted by OpenAI APIs.
if (MODELS_DEV_OPENAI_UNSUPPORTED_MODEL_IDS.has(modelId)) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-responses",
provider: "openai",
baseUrl: "https://api.openai.com/v1",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("openai", modelId, m);
}
}
// Process Groq models
if (data.groq?.models) {
for (const [modelId, model] of Object.entries(data.groq.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider: "groq",
baseUrl: "https://api.groq.com/openai/v1",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("groq", modelId, m);
}
}
// Process Cerebras models
if (data.cerebras?.models) {
for (const [modelId, model] of Object.entries(data.cerebras.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider: "cerebras",
baseUrl: "https://api.cerebras.ai/v1",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("cerebras", modelId, m);
}
}
// Process Cloudflare Workers AI models
if (data["cloudflare-workers-ai"]?.models) {
for (const [modelId, model] of Object.entries(data["cloudflare-workers-ai"].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider: "cloudflare-workers-ai",
baseUrl: CLOUDFLARE_WORKERS_AI_BASE_URL,
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
compat: { sendSessionAffinityHeaders: true },
});
recordModelsDevReasoningOptions("cloudflare-workers-ai", modelId, m);
}
}
// Process Cloudflare AI Gateway models
const cloudflareAIGatewayModelIds = new Set<string>();
if (data["cloudflare-ai-gateway"]?.models) {
for (const [prefixedId, model] of Object.entries(data["cloudflare-ai-gateway"].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
const slashIdx = prefixedId.indexOf("/");
if (slashIdx === -1) continue;
const upstream = prefixedId.slice(0, slashIdx);
const nativeId = prefixedId.slice(slashIdx + 1);
let api: "anthropic-messages" | "openai-completions" | "openai-responses";
let baseUrl: string;
let id: string;
if (upstream === "openai") {
api = "openai-responses";
baseUrl = CLOUDFLARE_AI_GATEWAY_OPENAI_BASE_URL;
id = nativeId;
} else if (upstream === "anthropic") {
api = "anthropic-messages";
baseUrl = CLOUDFLARE_AI_GATEWAY_ANTHROPIC_BASE_URL;
id = nativeId;
} else if (upstream === "workers-ai") {
api = "openai-completions";
baseUrl = CLOUDFLARE_AI_GATEWAY_COMPAT_BASE_URL;
id = prefixedId;
} else {
continue;
}
// Gateway passthroughs forward session affinity headers to upstreams that
// use them for cache/routing affinity.
const compat =
upstream === "anthropic" || upstream === "workers-ai" ? { sendSessionAffinityHeaders: true } : undefined;
cloudflareAIGatewayModelIds.add(id);
models.push({
id,
name: m.name || id,
api,
provider: "cloudflare-ai-gateway",
baseUrl,
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
...(compat ? { compat } : {}),
});
recordModelsDevReasoningOptions("cloudflare-ai-gateway", id, m);
}
}
// The gateway proxies Workers AI through its OpenAI-compatible /compat endpoint,
// but models.dev may omit or intermittently drop those `workers-ai/*` entries
// from the AI Gateway catalog. Mirror the Workers AI catalog under the documented
// prefix so the gateway keeps its OpenAI-compatible models stable.
if (data["cloudflare-workers-ai"]?.models) {
for (const [modelId, model] of Object.entries(data["cloudflare-workers-ai"].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
const id = `workers-ai/${modelId}`;
if (cloudflareAIGatewayModelIds.has(id)) continue;
cloudflareAIGatewayModelIds.add(id);
models.push({
id,
name: m.name || id,
api: "openai-completions",
provider: "cloudflare-ai-gateway",
baseUrl: CLOUDFLARE_AI_GATEWAY_COMPAT_BASE_URL,
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
compat: { sendSessionAffinityHeaders: true },
});
recordModelsDevReasoningOptions("cloudflare-ai-gateway", id, m);
}
}
// Process xAi models
if (data.xai?.models) {
for (const [modelId, model] of Object.entries(data.xai.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-responses",
provider: "xai",
baseUrl: "https://api.x.ai/v1",
compat: { ...XAI_RESPONSES_COMPAT },
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("xai", modelId, m);
}
}
models.push(...processZaiModels(data));
// Process Mistral models
if (data.mistral?.models) {
for (const [modelId, model] of Object.entries(data.mistral.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "mistral-conversations",
provider: "mistral",
baseUrl: "https://api.mistral.ai",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read ?? (m.cost?.input ? roundCost(m.cost.input * 0.1) : 0),
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("mistral", modelId, m);
}
}
// Process Hugging Face models
if (data.huggingface?.models) {
for (const [modelId, model] of Object.entries(data.huggingface.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider: "huggingface",
baseUrl: "https://router.huggingface.co/v1",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
compat: {
supportsDeveloperRole: false,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("huggingface", modelId, m);
}
}
models.push(...processFireworksModels(data["fireworks-ai"]));
// Process NVIDIA NIM models
if (data.nvidia?.models) {
for (const [modelId, model] of Object.entries(data.nvidia.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (!m.modalities?.input?.includes("text")) continue;
if (!m.modalities?.output?.includes("text")) continue;
const liveModelId = nvidiaNimModelIds.get(modelId) ?? nvidiaNimModelIds.get(normalizeNvidiaModelId(modelId));
if (!liveModelId) continue;
if (NVIDIA_NIM_UNSUPPORTED_MODELS.has(liveModelId)) continue;
models.push({
id: liveModelId,
name: m.name || liveModelId,
api: "openai-completions",
provider: "nvidia",
baseUrl: NVIDIA_BASE_URL,
headers: { ...NVIDIA_HEADERS },
reasoning: m.reasoning === true,
input: m.modalities.input.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
compat: NVIDIA_OPENAI_COMPAT,
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("nvidia", liveModelId, m);
}
}
// Process Together AI models
const togetherProvider = data.together ?? data.togetherai ?? data["together-ai"];
if (togetherProvider?.models) {
for (const [modelId, model] of Object.entries(togetherProvider.models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (m.status === "deprecated") continue;
const reasoning = m.reasoning === true;
const thinkingLevelMap = getTogetherThinkingLevelMap(modelId, reasoning);
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider: "together",
baseUrl: TOGETHER_BASE_URL,
reasoning,
...(thinkingLevelMap ? { thinkingLevelMap } : {}),
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
compat: getTogetherCompat(modelId, reasoning),
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("together", modelId, m);
}
}
models.push(...processBasetenModels(data.baseten));
// Process OpenCode models (Zen and Go)
// API mapping based on provider.npm field:
// - @ai-sdk/openai → openai-responses
// - @ai-sdk/anthropic → anthropic-messages
// - @ai-sdk/google → google-generative-ai
// - null/undefined/@ai-sdk/openai-compatible → openai-completions
const opencodeVariants = [
{ key: "opencode", provider: "opencode", basePath: "https://opencode.ai/zen" },
{ key: "opencode-go", provider: "opencode-go", basePath: "https://opencode.ai/zen/go" },
] as const;
for (const variant of opencodeVariants) {
if (!data[variant.key]?.models) continue;
for (const [modelId, model] of Object.entries(data[variant.key].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (m.status === "deprecated") continue;
const npm = m.provider?.npm;
let api: Api;
let baseUrl: string;
let compat: OpenAICompletionsCompat | OpenAIResponsesCompat | undefined;
if (npm === "@ai-sdk/openai") {
api = "openai-responses";
baseUrl = `${variant.basePath}/v1`;
compat = { sessionAffinityFormat: "openai-nosession" };
} else if (npm === "@ai-sdk/anthropic") {
api = "anthropic-messages";
// Anthropic SDK appends /v1/messages to baseURL
baseUrl = variant.basePath;
} else if (npm === "@ai-sdk/google") {
api = "google-generative-ai";
baseUrl = `${variant.basePath}/v1`;
} else if (npm === "@ai-sdk/alibaba") {
api = "openai-completions";
baseUrl = `${variant.basePath}/v1`;
compat = { cacheControlFormat: "anthropic" };
} else {
// null, undefined, or @ai-sdk/openai-compatible
api = "openai-completions";
baseUrl = `${variant.basePath}/v1`;
}
if (variant.provider === "opencode" && modelId === "grok-build-0.1") {
compat = { ...(compat ?? {}), supportsReasoningEffort: false };
}
if ((variant.provider === "opencode" || variant.provider === "opencode-go") && modelId === "kimi-k2.6") {
// OpenCode Kimi K2.6 accepts Anthropic-style thinking objects
// and rejects string thinking values or combined reasoning_effort.
compat = { ...(compat ?? {}), thinkingFormat: "deepseek", supportsReasoningEffort: false };
}
// Fix known mismatches between models.dev npm data and actual
// OpenCode Go endpoint behaviour. models.dev reports these models
// as @ai-sdk/anthropic, but the OpenCode Go endpoints either don't
// accept Anthropic SDK auth (MiniMax M2.7) or are served through
// the OpenAI-compatible /v1/chat/completions path (Qwen 3.5/3.6).
// Switch them to openai-completions so requests use Bearer auth
// and the standard /v1/chat/completions endpoint.
if (variant.provider === "opencode-go") {
if (modelId === "minimax-m2.7") {
api = "openai-completions";
baseUrl = `${variant.basePath}/v1`;
}
if (modelId === "qwen3.5-plus" || modelId === "qwen3.6-plus") {
api = "openai-completions";
baseUrl = `${variant.basePath}/v1`;
// Qwen/DashScope uses enable_thinking at the top level.
compat = { ...(compat ?? {}), thinkingFormat: "qwen" };
}
}
if (api === "openai-completions") {
compat = { ...(compat ?? {}), maxTokensField: "max_tokens" };
if (
OPENCODE_OPENAI_COMPLETIONS_LONG_CACHE_RETENTION_UNSUPPORTED_MODELS.has(
`${variant.provider}:${modelId}`,
)
) {
compat = { ...compat, supportsLongCacheRetention: false };
}
}
models.push({
id: modelId,
name: m.name || modelId,
api,
provider: variant.provider,
baseUrl,
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
...(compat ? { compat } : {}),
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions(variant.provider, modelId, m);
}
}
// Process GitHub Copilot models
if (data["github-copilot"]?.models) {
for (const [modelId, model] of Object.entries(data["github-copilot"].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (m.status === "deprecated") continue;
// Claude 4.x and 5.x models route to Anthropic Messages API
const isCopilotClaude = /^claude-(haiku|sonnet|opus|fable)-[45]([.\-]|$)/.test(modelId);
// GPT, Grok, OSWE, and MAI-Code models are only served through
// the Copilot /responses endpoint.
const needsResponsesApi =
modelId.startsWith("gpt-") ||
modelId.startsWith("grok-") ||
modelId.startsWith("oswe") ||
modelId.startsWith("mai-");
const api: Api = isCopilotClaude
? "anthropic-messages"
: needsResponsesApi
? "openai-responses"
: "openai-completions";
const anthropicCompat =
api === "anthropic-messages" ? getAnthropicMessagesCompat("github-copilot", modelId) : undefined;
const copilotModel: Model<any> = {
id: modelId,
name: m.name || modelId,
api,
provider: "github-copilot",
baseUrl: "https://api.individual.githubcopilot.com",
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: getModelsDevCost(m.cost),
contextWindow: m.limit?.context || 128000,
maxTokens: m.limit?.output || 8192,
headers: { ...COPILOT_STATIC_HEADERS },
...(anthropicCompat ? { compat: anthropicCompat } : {}),
// compat only applies to openai-completions
...(api === "openai-completions" ? {
compat: {
supportsStore: false,
supportsDeveloperRole: false,
supportsReasoningEffort: false,
},
} : {}),
};
models.push(copilotModel);
recordModelsDevReasoningOptions("github-copilot", modelId, m);
}
}
// Process MiniMax models
const minimaxVariants = [
{ key: "minimax", provider: "minimax", baseUrl: "https://api.minimax.io/anthropic" },
{ key: "minimax-cn", provider: "minimax-cn", baseUrl: "https://api.minimaxi.com/anthropic" },
] as const;
for (const { key, provider, baseUrl } of minimaxVariants) {
if (data[key]?.models) {
for (const [modelId, model] of Object.entries(data[key].models)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "anthropic-messages",
provider,
// MiniMax's Anthropic-compatible API - SDK appends /v1/messages
baseUrl,
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions(provider, modelId, m);
}
}
}
// Process Kimi For Coding models
if (data["kimi-for-coding"]?.models) {
const kimiModels = data["kimi-for-coding"].models as Record<string, ModelsDevModel>;
const hasCanonicalModel = Object.prototype.hasOwnProperty.call(kimiModels, "kimi-for-coding");
const kimiAliases = new Set(["k2p5", "k2p6", "k2p7"]);
for (const [modelId, model] of Object.entries(kimiModels)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
// models.dev may expose versioned aliases (e.g. k2p5/k2p6/k2p7).
// Normalize aliases to the canonical model id and drop duplicates when canonical exists.
if (kimiAliases.has(modelId) && hasCanonicalModel) continue;
const normalizedId = kimiAliases.has(modelId) ? "kimi-for-coding" : modelId;
const normalizedName = kimiAliases.has(modelId) ? "Kimi For Coding" : m.name || normalizedId;
const isKimiK3 = normalizedId === "k3";
const allowEmptySignature = isKimiK3 || normalizedId === "kimi-for-coding";
const impliedCost = KIMI_CODING_IMPLIED_COSTS[normalizedId];
models.push({
id: normalizedId,
name: normalizedName,
api: "anthropic-messages",
provider: "kimi-coding",
// Kimi For Coding's Anthropic-compatible API - SDK appends /v1/messages
baseUrl: "https://api.kimi.com/coding",
compat: {
...(allowEmptySignature ? { allowEmptySignature: true } : {}),
forceAdaptiveThinking: true,
},
reasoning: isKimiK3 || m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || impliedCost?.input || 0,
output: m.cost?.output || impliedCost?.output || 0,
cacheRead: m.cost?.cache_read || impliedCost?.cacheRead || 0,
cacheWrite: m.cost?.cache_write || impliedCost?.cacheWrite || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions("kimi-coding", normalizedId, m);
}
}
// Process Moonshot AI models
const moonshotVariants = [
{ key: "moonshotai", provider: "moonshotai", baseUrl: "https://api.moonshot.ai/v1" },
{ key: "moonshotai-cn", provider: "moonshotai-cn", baseUrl: "https://api.moonshot.cn/v1" },
] as const;
const moonshotCompat: OpenAICompletionsCompat = {
supportsStore: false,
supportsDeveloperRole: false,
supportsReasoningEffort: false,
maxTokensField: "max_tokens",
supportsStrictMode: false,
thinkingFormat: "deepseek",
};
const getMoonshotProviderModels = (key: "moonshotai" | "moonshotai-cn"): Record<string, ModelsDevModel> => {
const providerModels = data[key]?.models as Record<string, ModelsDevModel> | undefined;
return providerModels ? { ...providerModels } : {};
};
const moonshotModels = {
moonshotai: getMoonshotProviderModels("moonshotai"),
"moonshotai-cn": getMoonshotProviderModels("moonshotai-cn"),
};
for (const { key, provider, baseUrl } of moonshotVariants) {
for (const [modelId, m] of Object.entries(moonshotModels[key])) {
if (m.tool_call !== true) continue;
const isKimiK3 = modelId === "kimi-k3";
const compat = isKimiK3 ? { ...moonshotCompat } : moonshotCompat;
if (isKimiK3) {
compat.requiresReasoningContentOnAssistantMessages = true;
compat.deferredToolsMode = "kimi";
compat.thinkingFormat = "openai";
compat.supportsReasoningEffort = true;
}
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider,
baseUrl,
reasoning: isKimiK3 || m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || (isKimiK3 ? KIMI_K3_COST.input : 0),
output: m.cost?.output || (isKimiK3 ? KIMI_K3_COST.output : 0),
cacheRead: m.cost?.cache_read || (isKimiK3 ? KIMI_K3_COST.cacheRead : 0),
cacheWrite: m.cost?.cache_write || (isKimiK3 ? KIMI_K3_COST.cacheWrite : 0),
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
compat,
});
recordModelsDevReasoningOptions(provider, modelId, m);
}
}
// Process Xiaomi MiMo models
// Built-in `xiaomi` targets the API billing endpoint (single stable URL,
// keys from platform.xiaomimimo.com). The three `xiaomi-token-plan-*`
// providers cover prepaid Token Plan endpoints in cn / ams / sgp.
const xiaomiCompat: OpenAICompletionsCompat = {
requiresReasoningContentOnAssistantMessages: true,
thinkingFormat: "deepseek",
};
const xiaomiVariants = [
{ source: "xiaomi", provider: "xiaomi", baseUrl: "https://api.xiaomimimo.com/v1" },
{
source: "xiaomi-token-plan-cn",
provider: "xiaomi-token-plan-cn",
baseUrl: "https://token-plan-cn.xiaomimimo.com/v1",
},
{
source: "xiaomi-token-plan-ams",
provider: "xiaomi-token-plan-ams",
baseUrl: "https://token-plan-ams.xiaomimimo.com/v1",
},
{
source: "xiaomi-token-plan-sgp",
provider: "xiaomi-token-plan-sgp",
baseUrl: "https://token-plan-sgp.xiaomimimo.com/v1",
},
] as const;
for (const { source, provider, baseUrl } of xiaomiVariants) {
const providerModels = data[source]?.models;
if (!providerModels) continue;
for (const [modelId, model] of Object.entries(providerModels)) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (m.status === "deprecated") continue;
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider,
baseUrl,
compat: xiaomiCompat,
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
recordModelsDevReasoningOptions(provider, modelId, m);
}
}
// Process Alibaba Cloud Model Studio Token Plan models. International and
// China use separate endpoints and API keys (sk-sp- prefix). The Individual
// provider reuses the international source and endpoint with a narrower catalog.
// models.dev keys are "alibaba-token-plan[-cn]"; pi exposes them as
// "qwen-token-plan[-cn]" plus the Individual catalog view.
const qwenTokenPlanCompat: OpenAICompletionsCompat = {
thinkingFormat: "qwen",
supportsDeveloperRole: false,
supportsStore: false,
supportsReasoningEffort: true,
};
const qwenTokenPlanVariants = [
{
source: "alibaba-token-plan",
provider: "qwen-token-plan",
baseUrl: "https://token-plan.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
modelIds: undefined,
},
{
source: "alibaba-token-plan",
provider: "qwen-token-plan-individual",
baseUrl: "https://token-plan.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
modelIds: QWEN_TOKEN_PLAN_INDIVIDUAL_MODEL_IDS,
},
{
source: "alibaba-token-plan-cn",
provider: "qwen-token-plan-cn",
baseUrl: "https://token-plan.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
modelIds: undefined,
},
] as const;
for (const { source, provider, baseUrl, modelIds } of qwenTokenPlanVariants) {
const providerModels = data[source]?.models;
const emittedModelIds = modelIds ? new Set<string>() : undefined;
for (const [modelId, model] of Object.entries(providerModels ?? {})) {
const m = model as ModelsDevModel;
if (m.tool_call !== true) continue;
if (QWEN_TOKEN_PLAN_EXCLUDED_MODEL_IDS.has(modelId)) continue;
if (modelIds && !modelIds.has(modelId)) continue;
const thinkingLevelMap =
getEffortThinkingLevelMap(m.reasoning_options ?? []) ??
(QWEN_TOKEN_PLAN_REASONING_EFFORT_FALLBACK_MODEL_IDS.has(modelId)
? QWEN_TOKEN_PLAN_FALLBACK_THINKING_LEVEL_MAP
: undefined);
models.push({
id: modelId,
name: m.name || modelId,
api: "openai-completions",
provider,
baseUrl,
compat: thinkingLevelMap
? qwenTokenPlanCompat
: { ...qwenTokenPlanCompat, supportsReasoningEffort: false },
...(thinkingLevelMap ? { thinkingLevelMap } : {}),
reasoning: m.reasoning === true,
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
cost: {
input: m.cost?.input || 0,
output: m.cost?.output || 0,
cacheRead: m.cost?.cache_read || 0,
cacheWrite: m.cost?.cache_write || 0,
},
contextWindow: m.limit?.context || 4096,
maxTokens: m.limit?.output || 4096,
});
emittedModelIds?.add(modelId);
}
if (modelIds && emittedModelIds && generatorOptions.strict) {
assertExactModelIds(provider, modelIds, emittedModelIds);
}
}
console.log(`Loaded ${models.length} tool-capable models from models.dev`);
return models;
} catch (error) {
console.error("Failed to load models.dev data:", error);
if (generatorOptions.strict) throw error;
return [];
}
}
async function generateModels() {
// Fetch models from both sources
// models.dev: Anthropic, Google, OpenAI, Groq, Cerebras
// OpenRouter: xAI and other providers (excluding Anthropic, Google, OpenAI)
// AI Gateway: OpenAI-compatible catalog with tool-capable models
const modelsDevModels = await loadModelsDevData();
const openRouterModels = await fetchOpenRouterModels();
const aiGatewayModels = await fetchAiGatewayModels();
// Combine models (models.dev has priority)
const allModels = [...modelsDevModels, ...openRouterModels, ...aiGatewayModels].filter(
(model) =>
!(model.provider === "xai" && XAI_BUILTIN_EXCLUDED_MODEL_IDS.has(model.id)) &&
!((model.provider === "opencode" || model.provider === "opencode-go") && model.id === "gpt-5.3-codex-spark"),
);
// Temporary overrides until upstream model metadata is corrected.
for (const candidate of allModels) {
if (candidate.provider === "github-copilot" && GITHUB_COPILOT_EXTENDED_CONTEXT_MODELS.has(candidate.id)) {
candidate.contextWindow = 1000000;
}
if (
(candidate.provider === "anthropic" ||
candidate.provider === "opencode" ||
candidate.provider === "opencode-go") &&
(candidate.id === "claude-opus-4-6" ||
candidate.id === "claude-sonnet-4-6" ||
candidate.id === "claude-opus-4.6" ||
candidate.id === "claude-sonnet-4.6")
) {
candidate.contextWindow = 1000000;
}
// OpenCode variants list Claude Sonnet 4/4.5 with 1M context, actual limit is 200K
if (
(candidate.provider === "opencode" || candidate.provider === "opencode-go") &&
(candidate.id === "claude-sonnet-4-5" || candidate.id === "claude-sonnet-4")
) {
candidate.contextWindow = 200000;
}
if ((candidate.provider === "opencode" || candidate.provider === "opencode-go") && candidate.id === "gpt-5.4") {
candidate.contextWindow = 272000;
candidate.maxTokens = 128000;
}
// Keep direct OpenAI requests in the short-context pricing tier by default. Users can opt into the
// larger context through model overrides, so retain long-context cost metadata on the capped models.
if (candidate.provider === "openai" && OPENAI_SHORT_CONTEXT_CAPPED_MODEL_IDS.has(candidate.id)) {
candidate.contextWindow = OPENAI_LONG_CONTEXT_INPUT_THRESHOLD;
candidate.maxTokens = 128000;
}
if (candidate.provider === "openai" && OPENAI_LONG_CONTEXT_PRICING_MODEL_IDS.has(candidate.id)) {
const standardCost = OPENAI_GPT_56_STANDARD_COSTS[candidate.id];
candidate.cost = withOpenAiLongContextPricing(standardCost ?? candidate.cost);
}
// Cloudflare AI Gateway passes OpenAI usage through at OpenAI list prices.
if (candidate.provider === "cloudflare-ai-gateway") {
const standardCost = OPENAI_GPT_56_STANDARD_COSTS[candidate.id];
if (standardCost) candidate.cost = withOpenAiLongContextPricing(standardCost);
}
// models.dev reports gpt-5-pro output as 272000 (a duplicate of the input sub-limit);
// the actual max output is 128000. Also propagates to the derived Azure clone.
if (candidate.provider === "openai" && candidate.id === "gpt-5-pro") {
candidate.maxTokens = 128000;
}
// Keep Kimi K3's canonical output limit when gateway metadata is missing or incorrect.
if (
(candidate.provider === "openrouter" && OPENROUTER_KIMI_K3_MODEL_IDS.has(candidate.id)) ||
(candidate.provider === "vercel-ai-gateway" && candidate.id === "moonshotai/kimi-k3")
) {
candidate.maxTokens = KIMI_K3_MAX_TOKENS;
}
// Keep selected OpenRouter model metadata stable until upstream settles.
if (candidate.provider === "openrouter" && candidate.id === "moonshotai/kimi-k2.5") {
candidate.cost.input = 0.41;
candidate.cost.output = 2.06;
candidate.cost.cacheRead = 0.07;
candidate.maxTokens = 4096;
}
if (candidate.provider === "openrouter" && candidate.id.startsWith("moonshotai/kimi-k2.6")) {
candidate.compat = {
...candidate.compat,
supportsDeveloperRole: false,
requiresReasoningContentOnAssistantMessages: true,
};
}
if (candidate.provider === "openrouter" && candidate.id === "z-ai/glm-5") {
candidate.cost.input = 0.6;
candidate.cost.output = 1.9;
candidate.cost.cacheRead = 0.119;
}
}
// Add missing gpt models
const missingOpenAiModels: Model<"openai-responses">[] = [
{
id: "gpt-6-astra",
name: "GPT-6 Astra",
api: "openai-responses",
baseUrl: "https://api.openai.com/v1",
provider: "openai",
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing({ input: 10, output: 50, cacheRead: 1, cacheWrite: 12.5 }),
contextWindow: OPENAI_LONG_CONTEXT_INPUT_THRESHOLD,
maxTokens: 128000,
},
{
id: "gpt-5.6-sol",
name: "GPT-5.6 Sol",
api: "openai-responses",
baseUrl: "https://api.openai.com/v1",
provider: "openai",
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing({ input: 5, output: 30, cacheRead: 0.5, cacheWrite: 6.25 }),
contextWindow: OPENAI_LONG_CONTEXT_INPUT_THRESHOLD,
maxTokens: 128000,
},
{
id: "gpt-5.6-terra",
name: "GPT-5.6 Terra",
api: "openai-responses",
baseUrl: "https://api.openai.com/v1",
provider: "openai",
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing(OPENAI_GPT_56_STANDARD_COSTS["gpt-5.6-terra"]),
contextWindow: OPENAI_LONG_CONTEXT_INPUT_THRESHOLD,
maxTokens: 128000,
},
{
id: "gpt-5.6-luna",
name: "GPT-5.6 Luna",
api: "openai-responses",
baseUrl: "https://api.openai.com/v1",
provider: "openai",
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing(OPENAI_GPT_56_STANDARD_COSTS["gpt-5.6-luna"]),
contextWindow: OPENAI_LONG_CONTEXT_INPUT_THRESHOLD,
maxTokens: 128000,
},
{
id: "gpt-5-chat-latest",
name: "GPT-5 Chat Latest",
api: "openai-responses",
baseUrl: "https://api.openai.com/v1",
provider: "openai",
reasoning: false,
input: ["text", "image"],
cost: {
input: 1.25,
output: 10,
cacheRead: 0.125,
cacheWrite: 0,
},
contextWindow: 128000,
maxTokens: 16384,
},
];
for (const model of missingOpenAiModels) {
if (!allModels.some((m) => m.provider === model.provider && m.id === model.id)) {
allModels.push(model);
}
}
const deepseekCompat: OpenAICompletionsCompat = {
requiresReasoningContentOnAssistantMessages: true,
thinkingFormat: "deepseek",
};
const deepseekModels: Model<"openai-completions">[] = [
{
id: "deepseek-flash",
name: "DeepSeek V4.1 Flash",
api: "openai-completions",
baseUrl: "https://api.deepseek.com",
provider: "deepseek",
reasoning: true,
thinkingLevelMap: DEEPSEEK_V4_FLASH_THINKING_LEVEL_MAP,
input: ["text", "image"],
cost: {
// DeepSeek also offers time-based off-peak rates, which the cost schema cannot represent yet.
input: 0.3,
output: 1.2,
cacheRead: 0.006,
cacheWrite: 0,
},
contextWindow: 1000000,
maxTokens: 384000,
compat: deepseekCompat,
},
{
id: "deepseek-v4-pro",
name: "DeepSeek V4 Pro",
api: "openai-completions",
baseUrl: "https://api.deepseek.com",
provider: "deepseek",
reasoning: true,
input: ["text"],
cost: {
// DeepSeek also offers time-based off-peak rates, which the cost schema cannot represent yet.
input: 1.32,
output: 3.96,
cacheRead: 0.044,
cacheWrite: 0,
},
contextWindow: 1000000,
maxTokens: 384000,
compat: deepseekCompat,
},
];
allModels.push(...deepseekModels);
const antLingCompat: OpenAICompletionsCompat = {
supportsStore: false,
supportsDeveloperRole: false,
supportsReasoningEffort: false,
maxTokensField: "max_tokens",
supportsLongCacheRetention: false,
};
const antLingModels: Model<"openai-completions">[] = [
{
id: "Ling-2.6-flash",
name: "Ling 2.6 Flash",
api: "openai-completions",
baseUrl: "https://api.ant-ling.com/v1",
provider: "ant-ling",
reasoning: false,
input: ["text"],
cost: { input: 0.01, output: 0.02, cacheRead: 0, cacheWrite: 0 },
contextWindow: 262144,
maxTokens: 65536,
compat: antLingCompat,
},
{
id: "Ling-2.6-1T",
name: "Ling 2.6 1T",
api: "openai-completions",
baseUrl: "https://api.ant-ling.com/v1",
provider: "ant-ling",
reasoning: false,
input: ["text"],
cost: { input: 0.06, output: 0.25, cacheRead: 0, cacheWrite: 0 },
contextWindow: 262144,
maxTokens: 65536,
compat: antLingCompat,
},
{
id: "Ring-2.6-1T",
name: "Ring 2.6 1T",
api: "openai-completions",
baseUrl: "https://api.ant-ling.com/v1",
provider: "ant-ling",
reasoning: true,
input: ["text"],
cost: { input: 0.06, output: 0.25, cacheRead: 0, cacheWrite: 0 },
contextWindow: 262144,
maxTokens: 65536,
compat: { ...antLingCompat, thinkingFormat: "ant-ling" },
},
];
allModels.push(...antLingModels);
for (const candidate of allModels) {
if (
candidate.api === "openai-completions" &&
candidate.id.includes("deepseek-v4") &&
!QWEN_TOKEN_PLAN_PROVIDER_IDS.has(candidate.provider)
) {
const preservesNativeReasoningEffort = candidate.provider === "openrouter" || candidate.provider === "opencode";
candidate.compat = {
...candidate.compat,
...(preservesNativeReasoningEffort
? {
requiresReasoningContentOnAssistantMessages:
deepseekCompat.requiresReasoningContentOnAssistantMessages,
}
: deepseekCompat),
};
}
}
const minimaxDirectSupportedIds = new Set(["MiniMax-M2.7", "MiniMax-M2.7-highspeed", "MiniMax-M3"]);
for (let i = allModels.length - 1; i >= 0; i--) {
const candidate = allModels[i];
if (
(candidate.provider === "minimax" || candidate.provider === "minimax-cn") &&
!minimaxDirectSupportedIds.has(candidate.id)
) {
allModels.splice(i, 1);
}
}
// OpenAI Codex (ChatGPT OAuth) models
// NOTE: These are not fetched from models.dev; we keep a small, explicit list to avoid aliases.
// Older model limits are based on observed server behavior; GPT-5.6 and GPT-6 Astra use Codex's 272k default catalog limit.
const CODEX_BASE_URL = "https://chatgpt.com/backend-api";
const CODEX_CONTEXT = 272000;
const CODEX_GPT_56_CONTEXT = 272000;
const CODEX_SPARK_CONTEXT = 128000;
const CODEX_MAX_TOKENS = 128000;
const codexModels: Model<"openai-codex-responses">[] = [
{
id: "gpt-6-astra",
name: "GPT-6 Astra",
api: "openai-codex-responses",
provider: "openai-codex",
baseUrl: CODEX_BASE_URL,
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing({ input: 10, output: 50, cacheRead: 1, cacheWrite: 12.5 }),
contextWindow: CODEX_CONTEXT,
maxTokens: CODEX_MAX_TOKENS,
},
{
id: "gpt-5.3-codex-spark",
name: "GPT-5.3 Codex Spark",
api: "openai-codex-responses",
provider: "openai-codex",
baseUrl: CODEX_BASE_URL,
reasoning: true,
input: ["text"],
cost: { input: 1.75, output: 14, cacheRead: 0.175, cacheWrite: 0 },
contextWindow: CODEX_SPARK_CONTEXT,
maxTokens: CODEX_MAX_TOKENS,
},
{
id: "gpt-5.5",
name: "GPT-5.5",
api: "openai-codex-responses",
provider: "openai-codex",
baseUrl: CODEX_BASE_URL,
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing({ input: 5, output: 30, cacheRead: 0.5, cacheWrite: 0 }),
contextWindow: CODEX_CONTEXT,
maxTokens: CODEX_MAX_TOKENS,
},
{
id: "gpt-5.6-luna",
name: "GPT-5.6 Luna",
api: "openai-codex-responses",
provider: "openai-codex",
baseUrl: CODEX_BASE_URL,
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing(OPENAI_GPT_56_STANDARD_COSTS["gpt-5.6-luna"]),
contextWindow: CODEX_GPT_56_CONTEXT,
maxTokens: CODEX_MAX_TOKENS,
},
{
id: "gpt-5.6-sol",
name: "GPT-5.6 Sol",
api: "openai-codex-responses",
provider: "openai-codex",
baseUrl: CODEX_BASE_URL,
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing({ input: 5, output: 30, cacheRead: 0.5, cacheWrite: 6.25 }),
contextWindow: CODEX_GPT_56_CONTEXT,
maxTokens: CODEX_MAX_TOKENS,
},
{
id: "gpt-5.6-terra",
name: "GPT-5.6 Terra",
api: "openai-codex-responses",
provider: "openai-codex",
baseUrl: CODEX_BASE_URL,
reasoning: true,
input: ["text", "image"],
cost: withOpenAiLongContextPricing(OPENAI_GPT_56_STANDARD_COSTS["gpt-5.6-terra"]),
contextWindow: CODEX_GPT_56_CONTEXT,
maxTokens: CODEX_MAX_TOKENS,
},
];
allModels.push(...codexModels);
// Add missing Mistral Medium 3.5 model until models.dev includes it
if (!allModels.some(m => m.provider === "mistral" && m.id === "mistral-medium-3.5")) {
allModels.push({
id: "mistral-medium-3.5",
name: "Mistral Medium 3.5",
api: "mistral-conversations",
provider: "mistral",
baseUrl: "https://api.mistral.ai",
reasoning: true,
input: ["text", "image"],
cost: {
input: 1.5,
output: 7.5,
cacheRead: 0,
cacheWrite: 0,
},
contextWindow: 262144, // 256k tokens
maxTokens: 262144,
});
}
// Add "auto" alias for openrouter/auto
if (!allModels.some(m => m.provider === "openrouter" && m.id === "auto")) {
allModels.push({
id: "auto",
name: "Auto",
api: "openai-completions",
provider: "openrouter",
baseUrl: "https://openrouter.ai/api/v1",
reasoning: true,
input: ["text", "image"],
cost: {
// we dont know about the costs because OpenRouter auto routes to different models
// and then charges you for the underlying used model
input:0,
output:0,
cacheRead:0,
cacheWrite:0,
},
contextWindow: 2000000,
maxTokens: 30000,
});
}
// Add "fusion" alias for openrouter/fusion. OpenRouter exposes Fusion as a
// router alias/plugin entry point; its model metadata does not advertise
// tools, but the alias resolves to a concrete model that can invoke caller
// tools and has the openrouter:fusion server tool auto-injected.
if (!allModels.some(m => m.provider === "openrouter" && m.id === "openrouter/fusion")) {
allModels.push({
id: "openrouter/fusion",
name: "OpenRouter: Fusion",
api: "openai-completions",
provider: "openrouter",
baseUrl: "https://openrouter.ai/api/v1",
reasoning: true,
input: ["text"],
cost: {
// we dont know about the costs because Fusion routes to multiple models
// and then charges you for the underlying used models
input: 0,
output: 0,
cacheRead: 0,
cacheWrite: 0,
},
contextWindow: 1000000,
maxTokens: 30000,
});
}
// Azure Foundry deploys these with larger context windows than OpenAI's own short-tier defaults.
// See models-sold-directly-by-azure docs.
const AZURE_CONTEXT_WINDOW_OVERRIDES: Record<string, number> = {
"gpt-5.4": 1050000,
"gpt-5.5": 1050000,
"gpt-5.6-luna": 1050000,
"gpt-5.6-sol": 1050000,
"gpt-5.6-terra": 1050000,
};
const azureOpenAiModels: Model<Api>[] = allModels
.filter((model) => model.provider === "openai" && model.api === "openai-responses")
.map((model) => ({
...model,
api: "azure-openai-responses",
provider: "azure-openai-responses",
baseUrl: "",
cost: {
input: model.cost.input,
output: model.cost.output,
cacheRead: model.cost.cacheRead,
cacheWrite: model.cost.cacheWrite,
},
contextWindow: AZURE_CONTEXT_WINDOW_OVERRIDES[model.id] ?? model.contextWindow,
}));
allModels.push(...azureOpenAiModels);
for (const model of allModels) {
applyOpenAICompletionsCompatMetadata(model);
applyAnthropicMessagesCompatMetadata(model);
applyModelsDevReasoningOptionMetadata(model);
applyThinkingLevelMetadata(model);
applyStrictToolCompatMetadata(model);
applyOpenAIGrammarToolCompatMetadata(model);
applyOpenAIToolSearchMetadata(model);
applyOpenAIExplicitPromptCacheMetadata(model);
}
applyAnthropicAllowedFallbackModelMetadata(allModels.filter(isAnthropicFallbackMetadataModel));
// Group by provider and deduplicate by model ID
const providers: Record<string, Record<string, Model<any>>> = {};
for (const model of allModels) {
if (!providers[model.provider]) {
providers[model.provider] = {};
}
// Use model ID as key to automatically deduplicate
// Only add if not already present (models.dev takes priority over OpenRouter)
if (!providers[model.provider][model.id]) {
providers[model.provider][model.id] = model;
}
}
const sortedProviderIds = Object.keys(providers).sort();
const jsonProviders: Record<string, Record<string, Model<any>>> = {};
for (const providerId of sortedProviderIds) {
jsonProviders[providerId] = {};
for (const modelId of Object.keys(providers[providerId]).sort()) {
jsonProviders[providerId][modelId] = providers[providerId][modelId];
}
}
const serializeJson = (value: unknown) => `${JSON.stringify(value, null, generatorOptions.pretty ? 2 : undefined)}\n`;
const writeJson = (path: string, value: unknown) => writeFileSync(path, serializeJson(value));
const generatedDataProviderIds = generatorOptions.dataOnly
? readModelDataProviderIds(packageRoot)
: sortedProviderIds;
const missingProviderIds = generatedDataProviderIds.filter((providerId) => !jsonProviders[providerId]);
if (missingProviderIds.length > 0) {
throw new Error(`Cannot hydrate missing providers: ${missingProviderIds.join(", ")}`);
}
// Only the ignored internal data is grouped by API for type derivation. Public JSON catalog output stays flat.
const generatedDataProviders: Record<string, Record<string, Record<string, Model<Api>>>> = {};
const modelDataStructure: ModelDataStructure = {};
for (const providerId of generatedDataProviderIds) {
const models = jsonProviders[providerId];
generatedDataProviders[providerId] = {};
modelDataStructure[providerId] = {};
const apiIds = Array.from(new Set(Object.values(models).map((model) => model.api))).sort();
for (const api of apiIds) {
generatedDataProviders[providerId][api] = {};
for (const [modelId, model] of Object.entries(models)) {
if (model.api !== api) continue;
generatedDataProviders[providerId][api][modelId] = model;
modelDataStructure[providerId][modelId] = api;
}
}
}
const generatedAt = new Date().toISOString();
if (!generatorOptions.jsonOnly) {
// Stage and validate all provider values before replacing the current generated data.
const providersDir = join(packageRoot, "src/providers");
const dataDir = join(providersDir, "data");
const stagingRoot = mkdtempSync(join(providersDir, ".model-generation-"));
const stagedDataDir = join(stagingRoot, "data");
const previousDataDir = join(stagingRoot, "previous-data");
let restoreGeneratedCatalog: (() => void) | undefined;
try {
mkdirSync(stagedDataDir, { recursive: true });
const fileContents: Record<string, string> = {};
for (const providerId of generatedDataProviderIds) {
const filename = `${providerId}.json`;
const content = serializeJson(generatedDataProviders[providerId]);
fileContents[filename] = content;
writeFileSync(join(stagedDataDir, filename), content);
}
writeJson(
join(stagedDataDir, MODEL_DATA_MANIFEST_FILE),
createModelDataManifest(modelDataStructure, fileContents, generatedAt),
);
validateModelDataDirectory(modelDataStructure, stagedDataDir);
if (!generatorOptions.dataOnly) {
const previousShardContents = new Map(
readdirSync(providersDir)
.filter((entry) => entry.endsWith(".models.ts"))
.map((entry) => [entry, readFileSync(join(providersDir, entry), "utf8")] as const),
);
const aggregatorPath = join(packageRoot, "src/models.generated.ts");
const previousAggregator = readFileSync(aggregatorPath, "utf8");
restoreGeneratedCatalog = () => {
for (const entry of readdirSync(providersDir)) {
if (entry.endsWith(".models.ts")) rmSync(join(providersDir, entry));
}
for (const [entry, content] of previousShardContents) {
writeFileSync(join(providersDir, entry), content);
}
writeFileSync(aggregatorPath, previousAggregator);
};
const generatedHeader = `// This file is auto-generated by scripts/generate-models.ts
// Do not edit manually - run 'npm run generate-models' to update
`;
const catalogConstName = (providerId: string) =>
`${providerId.toUpperCase().replace(/[^A-Z0-9]+/g, "_")}_MODELS`;
const generatedShardFiles = new Set<string>();
for (const providerId of sortedProviderIds) {
let output = generatedHeader;
output += `import values from "./data/${providerId}.json" with { type: "json" };\n`;
output += `import { flattenModelCatalog, type ModelCatalog } from "../model-catalog.ts";\n\n`;
output += `export const ${catalogConstName(providerId)}: ModelCatalog<typeof values, ${JSON.stringify(providerId)}> =\n`;
output += `\tflattenModelCatalog(${JSON.stringify(providerId)}, values);\n`;
const filename = `${providerId}.models.ts`;
generatedShardFiles.add(filename);
writeFileSync(join(providersDir, filename), output);
}
for (const entry of readdirSync(providersDir)) {
if (entry.endsWith(".models.ts") && !generatedShardFiles.has(entry)) rmSync(join(providersDir, entry));
}
let output = generatedHeader;
for (const providerId of sortedProviderIds) {
output += `import { ${catalogConstName(providerId)} } from "./providers/${providerId}.models.ts";\n`;
}
output += `\nexport const MODELS: {\n`;
for (const providerId of sortedProviderIds) {
output += `\treadonly ${JSON.stringify(providerId)}: typeof ${catalogConstName(providerId)};\n`;
}
output += `} = {\n`;
for (const providerId of sortedProviderIds) {
output += `\t${JSON.stringify(providerId)}: ${catalogConstName(providerId)},\n`;
}
output += `};\n`;
writeFileSync(aggregatorPath, output);
console.log("Generated provider catalogs and src/models.generated.ts");
}
const hadPreviousData = existsSync(dataDir);
if (hadPreviousData) renameSync(dataDir, previousDataDir);
try {
renameSync(stagedDataDir, dataDir);
validateGeneratedModelData(packageRoot);
} catch (error) {
rmSync(dataDir, { recursive: true, force: true });
if (hadPreviousData && existsSync(previousDataDir)) renameSync(previousDataDir, dataDir);
throw error;
}
restoreGeneratedCatalog = undefined;
console.log(
generatorOptions.dataOnly
? "Hydrated JSON model values under src/providers/data/"
: "Generated JSON model values under src/providers/data/",
);
} catch (error) {
restoreGeneratedCatalog?.();
throw error;
} finally {
rmSync(stagingRoot, { recursive: true, force: true });
}
}
if (generatorOptions.jsonOutputDir) {
const providerOutputDir = join(generatorOptions.jsonOutputDir, "providers");
rmSync(generatorOptions.jsonOutputDir, { recursive: true, force: true });
mkdirSync(providerOutputDir, { recursive: true });
writeJson(join(generatorOptions.jsonOutputDir, "models.json"), jsonProviders);
writeJson(join(generatorOptions.jsonOutputDir, "providers.json"), sortedProviderIds);
for (const providerId of sortedProviderIds) {
writeJson(join(providerOutputDir, `${providerId}.json`), jsonProviders[providerId]);
}
console.log(`Generated JSON model catalog under ${generatorOptions.jsonOutputDir}`);
}
// Print statistics
const totalModels = allModels.length;
const reasoningModels = allModels.filter(m => m.reasoning).length;
console.log(`\nModel Statistics:`);
console.log(` Total tool-capable models: ${totalModels}`);
console.log(` Reasoning-capable models: ${reasoningModels}`);
for (const [provider, models] of Object.entries(providers)) {
console.log(` ${provider}: ${Object.keys(models).length} models`);
}
}
// Run the generator
generateModels().catch((error) => {
console.error(error);
process.exitCode = 1;
});