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> 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 <YOUR_WAFER_API_KEY>
```
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 <YOUR_WAFER_API_KEY>"
```
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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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.
<Note>
**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.
</Note>
### Kimi-K2.6
With reasoning off:
```bash theme={null}
curl -sS "https://pass.wafer.ai/v1/chat/completions" \
-H "Authorization: Bearer <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-H "Content-Type: application/json" \
-d '{
"model": "Kimi-K2.7-Code",
"messages": [{"role": "user", "content": "Reply with exactly: ok"}],
"max_tokens": 512
}'
```
<Note>
**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.
</Note>
### GLM-5.1
With reasoning off:
```bash theme={null}
curl -sS "https://pass.wafer.ai/v1/chat/completions" \
-H "Authorization: Bearer <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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 <YOUR_WAFER_API_KEY>" \
-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
}'
```
<Note>
**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.
</Note>
**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 `<think>…</think>` 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 <YOUR_WAFER_API_KEY>" \
-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.
|