> ## Documentation Index > Fetch the complete documentation index at: https://docs.wafer.ai/llms.txt > Use this file to discover all available pages before exploring further. # API Reference > Direct curl requests, endpoints, headers, and request parameters for Wafer Serverless. Use the Serverless API directly when you are building against Wafer from your own application, scripts, or low-level tooling. For Claude Code, Codex, Cline, Roo Code, and other agent harnesses, use [Agent Setup](/serverless/setup) instead. ## Base URL | Surface | URL | | --------------------------------- | ----------------------------------- | | OpenAI-compatible API | `https://pass.wafer.ai/v1` | | Anthropic-compatible Messages API | `https://pass.wafer.ai/v1/messages` | Send your API key on every request: ```http theme={null} Authorization: Bearer ``` To require Zero Data Retention for a single request, add: ```http theme={null} Wafer-ZDR: required ``` ## List Models ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/models" \ -H "Authorization: Bearer " ``` The model list is the source of truth for currently available Serverless model IDs. Each card layers Wafer-specific capabilities and pricing on top of the standard OpenAI shape: ```json theme={null} { "object": "list", "data": [ { "id": "GLM-5.1", "object": "model", "created": 1779148800, "owned_by": "wafer", "max_model_len": 202752, "zdr_supported": true, "wafer": { "display_name": "GLM-5.1", "description": "General Language Model 5.1 — high-quality bilingual (EN/ZH) generation with strong coding and reasoning capabilities.", "tier": "pass_included", "context_length": 202752, "capabilities": { "vision": false, "tools": true, "reasoning": true, "chat_completions": { "supported": true, "streaming": true, "tools": true, "tool_streaming": true, "json_schema": true, "json_schema_refs": true, "grammar": true, "regex": true, "tools_with_response_format": true, "n": true }, "messages": { "supported": true, "streaming": true, "tools": true }, "responses": { "supported": true, "streaming": true, "text_format": ["text", "json_object", "json_schema"], "raw_json_schema_text": true }, "zdr": { "supported": true, "same_capabilities": true } }, "pricing": { "input_cents_per_million": 100, "output_cents_per_million": 320, "cache_read_cents_per_million": 10 } } } ] } ``` * `id`, `object`, `created`, `owned_by` are the stable OpenAI fields — SDKs that only read these keep working. * `max_model_len` is the hard context-window cap; requests past it return `context_length_exceeded`. * `zdr_supported: true` means the model accepts `Wafer-ZDR: required`. Models without ZDR support omit the field or set it `false`. * `wafer.capabilities.{vision, tools, reasoning}` are the legacy summary flags. Newer model cards also include per-surface flags under `chat_completions`, `messages`, `responses`, and `zdr`; branch on those when using structured outputs, grammar, regex, tools, or ZDR-specific behavior. * `wafer.pricing` is in cents per million tokens and is what we'll bill at; check it whenever pricing changes matter to your code path. ## Compatibility Notes `pass.wafer.ai` validates and normalizes a few model-specific features before a request is sent upstream: * Safe local JSON Schema references (`#/$defs/...` and `#/definitions/...`) are automatically inlined for tool schemas and structured outputs. Remote, unresolved, and recursive refs are rejected. * `response_format.type = "grammar"` is supported only on models whose `wafer.capabilities.chat_completions.grammar` flag is `true`. * Top-level `regex` is rejected when the selected model or ZDR partition would ignore it. For example, `Kimi-K2.6` supports regex on the ZDR self-hosted partition but not on the non-ZDR Moonshot partition. * When `tools` and `response_format` are both present, tools keep OpenAI-style precedence so a tool-selected request can still return tool calls. * `n > 1` is passed through only on models whose `wafer.capabilities.chat_completions.n` flag is `true`. Unsupported models fail fast with `unsupported_feature` and `param: "n"` instead of silently returning one choice. * OpenAI-compatible `role: "tool"` messages may send `content: null`; Wafer normalizes that to an empty tool result before dispatch so common SDK histories keep working. * `/v1/responses` with `text.format.type = "json_schema"` returns raw JSON text instead of wrapping JSON in Markdown fences. ## Chat Completions Use `POST /v1/chat/completions` for ordinary text prompts and OpenAI-compatible clients: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.1", "messages": [ {"role": "user", "content": "Reply with the single word: ready."} ], "max_tokens": 16, "temperature": 0 }' ``` Add `Wafer-ZDR: required` when the request must only route to ZDR-capable infrastructure: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Wafer-ZDR: required" \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.2", "messages": [{"role": "user", "content": "Summarize what Wafer does."}], "max_tokens": 128 }' ``` ## Reasoning Controls Reasoning-capable models can return a separate `reasoning_content` field alongside the final answer. Discover support from `GET /v1/models` by checking `wafer.capabilities.reasoning`. Wafer accepts three equivalent control shapes: | Shape | Use | | ------------------------------------------------------------------- | ------------------------------------------- | | `thinking: {"type": "enabled"}` or `thinking: {"type": "disabled"}` | Recommended for simple on/off examples. | | `reasoning_effort` set to `none`, `low`, `medium`, `high`, or `max` | Use when you want an explicit effort level. | | `enable_thinking: true` or `enable_thinking: false` | Qwen/DashScope-compatible shape. | Default behavior is reasoning **off** unless you explicitly enable it — for every reasoning-capable model. The same on/off curl shape works across reasoning-capable models. For example, swap the `model` value to `GLM-5.2` to run the 1M-context GLM route with the same toggle. You can also use `reasoning_effort` (`none`, `low`, `medium`, `high`, or `max`) when you want an explicit effort level. **Where the reasoning text appears.** Most reasoning-capable models (`GLM-5.1`, `GLM-5.2`, `glm5.2-fast`, `Kimi-K2.6`, `Kimi-K2.7-Code`, `Qwen3.6-35B-A3B`, and `qwen3.7-max`) return reasoning in a **separate `reasoning_content`** field on the assistant message. Some upstream-routed models can expose reasoning in provider-specific ways. Check the model card before parsing reasoning programmatically. ### Kimi-K2.6 With reasoning off: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "Kimi-K2.6", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 64, "thinking": {"type": "disabled"} }' ``` With reasoning on: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "Kimi-K2.6", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 256, "thinking": {"type": "enabled"} }' ``` ### Kimi-K2.7-Code `Kimi-K2.7-Code` is a coding-focused model with **reasoning always on** — there is no reasoning-off mode. You don't need to send `thinking`/`reasoning_effort`; any attempt to disable thinking is treated as enabled. Give it room for the reasoning pass with a generous `max_tokens`. ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "Kimi-K2.7-Code", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 512 }' ``` **Tool use must be model-decided.** Because thinking is always on, `Kimi-K2.7-Code` rejects forced tool calls — `tool_choice: "required"` and a specific `{"type": "function", ...}` choice both return `400` with `code: "unsupported_feature"` and `param: "tool_choice"`. Pass your `tools` with `tool_choice: "auto"` (or `"none"`) and let the model decide. ### GLM-5.1 With reasoning off: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.1", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 64, "thinking": {"type": "disabled"} }' ``` With reasoning on: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.1", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 256, "thinking": {"type": "enabled"} }' ``` ### Qwen3.5-397B-A17B With reasoning off: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen3.5-397B-A17B", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 64, "thinking": {"type": "disabled"} }' ``` With reasoning on: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen3.5-397B-A17B", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 256, "thinking": {"type": "enabled"} }' ``` ### GLM-5.2 With reasoning off: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.2", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 64, "thinking": {"type": "disabled"} }' ``` With reasoning on: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.2", "messages": [{"role": "user", "content": "Reply with exactly: ok"}], "max_tokens": 256, "thinking": {"type": "enabled"} }' ``` ## Streaming Set `stream` to `true` and add `-N` to receive server-sent events as they arrive: ```bash theme={null} curl -N -sS "https://pass.wafer.ai/v1/chat/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.1", "messages": [{"role": "user", "content": "Write a one-sentence haiku."}], "max_tokens": 64, "temperature": 0.7, "stream": true }' ``` **Usage chunks are always included on streaming requests.** Wafer automatically sets `stream_options: {"include_usage": true, "continuous_usage_stats": true}` on every streaming chat completion so the final SSE chunk carries `usage.{prompt_tokens, completion_tokens, total_tokens}`. You don't need to send `stream_options` yourself — and if you do, the auto-injected values still win. This means you can reliably bill / track token spend from streaming responses the same way you would from non-streaming. **Tool calls and streaming.** When the model decides to call a tool, the full `tool_calls` array arrives in a single chunk rather than streamed argument- by-argument. Buffer the chunk before processing — partial tool-call deltas will not occur on Wafer. ## Chat Request Body | Field | Type | Required | Notes | | --------------------- | ----------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `model` | string | Yes | Any Serverless model ID from `GET /v1/models`, such as `GLM-5.1` or `Qwen3.5-397B-A17B`. | | `messages` | array | Yes | OpenAI-compatible chat messages with `role` and `content`. | | `max_tokens` | integer | No | Maximum generated tokens. Must be positive when provided. | | `temperature` | number | No | Sampling temperature. Use `0` for deterministic decoding. | | `top_p` | number | No | Nucleus sampling cutoff. | | `top_k` | integer | No | Limits sampling to the top K candidate tokens on supported models. | | `min_p` | number | No | Minimum probability threshold on supported models. | | `frequency_penalty` | number | No | Penalizes repeated tokens by frequency. | | `presence_penalty` | number | No | Penalizes tokens that have already appeared. | | `repetition_penalty` | number | No | SGLang repetition penalty on supported models. | | `stop` | string or array | No | Stop sequence or sequences. | | `stream` | boolean | No | When `true`, returns streaming chat completion chunks. | | `tools` | array | No | OpenAI-compatible tool definitions on models that support tool calling. | | `tool_choice` | string or object | No | Controls tool selection for compatible models. | | `response_format` | object | No | Use JSON mode or structured outputs on compatible models. | | `logprobs` | boolean | No | Request token log probabilities on compatible models. | | `top_logprobs` | integer | No | Number of log probabilities to include when `logprobs` is enabled. | | `thinking` | object or boolean | No | Recommended reasoning on/off control, for example `{"type": "enabled"}` or `{"type": "disabled"}`. | | `reasoning_effort` | string | No | Reasoning effort: `none`, `low`, `medium`, `high`, or `max`. | | `enable_thinking` | boolean | No | Compatibility reasoning on/off control. | | `preserve_thinking` | boolean | No | Wafer-shape for preserved reasoning across turns. Supported on `Kimi-K2.6`, `Kimi-K2.7-Code`, and `GLM-5.1`. See [Multi-turn preserved thinking](#multi-turn-preserved-thinking). | | `seed` | integer | No | Deterministic sampling seed on backends that support it. | | `n` | integer | No | Number of completions to generate. Stripped on Kimi-K2.6 (see caveat below). | | `logit_bias` | object | No | Token-level logit adjustments. Supported on sglang backends. | | `parallel_tool_calls` | boolean | No | Allow the model to emit multiple `tool_calls` in a single response. | | `stream_options` | object | No | `{"include_usage": true}` to include token counts in the final streaming chunk. **Wafer sets this automatically on every streaming request** — see [Streaming](#streaming). | Unsupported or model-specific parameters return a request error instead of being silently ignored — except where noted in [Model-specific behavior](#model-specific-behavior). ## Model-specific Behavior A handful of routes intentionally diverge from the generic OpenAI/Anthropic contract. Know these before you ship. ### Kimi-K2.6 sampling params are stripped Kimi-K2.6 forwards to Moonshot's hosted `kimi-k2.6`, which enforces fixed sampling values (`temperature=1.0`, `top_p=0.95`, `n=1`, `presence_penalty=0`, `frequency_penalty=0`) and rejects anything else. Wafer **strips** `temperature`, `top_p`, `n`, `presence_penalty`, and `frequency_penalty` from Kimi-K2.6 requests before forwarding. If you send `temperature: 0` to `Kimi-K2.6`, expect Moonshot-default sampling (`temperature=1.0`) at the model. Either pick a model where those controls take effect (`GLM-5.1`, `Qwen3.5-397B-A17B`, etc.), or compensate with prompt engineering / `reasoning_effort`. ### Multi-turn preserved thinking `Kimi-K2.6`, `Kimi-K2.7-Code`, and `GLM-5.1` accept either `preserve_thinking: true` (Wafer shape) or `thinking: {"type": "enabled", "keep": "all"}` (Moonshot shape) to carry prior turns' reasoning back into the next turn's context. The previous turn's `reasoning_content` is inlined as `…` inside the assistant message before the chat template runs, so the model can build on its own earlier chain of thought. ```json theme={null} { "model": "Kimi-K2.6", "thinking": {"type": "enabled"}, "preserve_thinking": true, "messages": [ {"role": "user", "content": "Hard problem…"}, { "role": "assistant", "content": "…final answer from turn 1…", "reasoning_content": "…chain of thought from turn 1…" }, {"role": "user", "content": "Follow-up…"} ] } ``` Default is **off** — reasoning is not preserved across turns unless you opt in. ### JSON Schema references in tools and structured outputs Wafer Serverless accepts common MCP, Zod, and Pydantic JSON Schemas that use safe local references such as `#/$defs/...` or `#/definitions/...`. For compatible models, Wafer inlines those local definitions before dispatching the request upstream. Remote references and recursive schemas are not supported. Inline those schemas client-side or simplify them before retrying. ## Text Completions Use `POST /v1/completions` only when you need token-ID prompts or constrained decoding on a supported route: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/completions" \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.1", "prompt": [9703], "max_tokens": 2, "temperature": 0, "ebnf": "root ::= \"A\" | \"B\"" }' ``` For the full `/v1/completions` request shape, streaming example, parameter table, and response shape, see [Tokenized Completions and Constrained Decoding](/serverless/tokenized-completions). ## Anthropic Messages Wafer also exposes an Anthropic-compatible Messages endpoint at `https://pass.wafer.ai/v1/messages`. Most users reach it through Claude Code or Conductor; see [Agent Setup](/serverless/setup) for the required environment variables.