> ## 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. # Messages > Anthropic-compatible Messages API: supported fields, tools, vision, token counting, and streaming events. `POST https://pass.wafer.ai/v1/messages` accepts Anthropic Messages API requests. Use it with the Anthropic SDKs, Claude Code, and other Anthropic-format clients. Set the client's base URL to `https://pass.wafer.ai`; the client appends `/v1/messages`. ## Request ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/messages" \ -H "x-api-key: " \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.3", "max_tokens": 1024, "system": "Answer in one sentence.", "messages": [ {"role": "user", "content": "What is Wafer Serverless?"} ] }' ``` Authenticate with `x-api-key` or `Authorization: Bearer`. The `anthropic-version` and `anthropic-beta` headers are accepted and ignored. Use a Wafer model ID from `GET /v1/models` as `model`. Anthropic model names such as `claude-...` are not available. ## Supported fields | Field | Notes | | - | - | | `model` | Required. A model ID from `GET /v1/models`. | | `max_tokens` | Required. Must be a positive integer. | | `messages` | Required. `user` and `assistant` turns with string content or content blocks: `text`, `image`, `tool_use`, `tool_result`, and `thinking` (from earlier assistant turns). `document` (PDF) blocks are not supported. | | `system` | System prompt, as a string or an array of `text` blocks. | | `tools` | Tool definitions with `name`, `description`, and `input_schema`. Anthropic server tools (web search, bash, text editor, computer use) are exposed to the model as regular tools that your client must execute. | | `tool_choice` | `{"type": "auto"}`, `{"type": "none"}`, or `{"type": "tool", "name": ...}` to force a specific tool. `{"type": "any"}` is accepted but behaves like `auto`. | | `thinking` | `{"type": "enabled"}` turns reasoning on. See [Thinking](#thinking). | | `output_config.effort` | Reasoning effort when thinking is on: `low`, `medium`, `high`, or `max`. Other values are ignored. | | `stop_sequences` | Stop generation at any of these strings. | | `temperature`, `top_p`, `top_k` | Sampling controls. Most models use fixed sampling; see the [note on sampling](/serverless/chat-completions#request-fields). | | `stream` | Stream server-sent events. See [Streaming](#streaming). | `metadata` and `cache_control` are accepted and ignored. Prompt caching is automatic; see [Prompt caching](/serverless/usage#prompt-caching). ## Response ```json theme={null} { "id": "msg_953d3af2bec34ee6a5a29c455601af21", "type": "message", "role": "assistant", "model": "GLM-5.3", "content": [ {"type": "text", "text": "It's 18C and sunny in Paris."} ], "stop_reason": "end_turn", "usage": { "input_tokens": 338, "output_tokens": 29, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 256 } } ``` `stop_reason` is `end_turn`, `max_tokens`, `stop_sequence`, or `tool_use`. `usage.input_tokens` counts all prompt tokens, **including** `cache_read_input_tokens`. This differs from Anthropic's API. See [Usage and Billing](/serverless/usage#messages-usage). ## Thinking On `/v1/messages`, reasoning is **off** unless you enable it, whatever the model's default on other APIs. ```json theme={null} { "model": "GLM-5.3", "max_tokens": 4096, "thinking": {"type": "enabled", "budget_tokens": 2048}, "output_config": {"effort": "high"}, "messages": [{"role": "user", "content": "Is 391 prime?"}] } ``` * `thinking: {"type": "enabled"}` turns reasoning on. Set `output_config.effort` to choose the effort; without it, the effort depends on the model. * `budget_tokens` is accepted but ignored. Use `output_config.effort` and `max_tokens` to control reasoning length. * Reasoning is returned as `thinking` content blocks before the answer. Their `signature` is an empty string. * You can pass earlier `thinking` blocks back in assistant turns. * On `GLM-5.3` and `GLM-5.3-Flash`, reasoning can't be fully turned off. With thinking off, the model can still spend output tokens reasoning that isn't returned; they count toward `max_tokens` and `usage.output_tokens`. See the [reasoning warning](/serverless/chat-completions#reasoning). ## Tools When the model calls a tool, the response contains a `tool_use` block and `stop_reason` is `tool_use`. Send the result back in a `tool_result` block: ```json theme={null} { "model": "GLM-5.3", "max_tokens": 1024, "tools": [ { "name": "get_weather", "description": "Get the current weather for a city.", "input_schema": { "type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"] } } ], "messages": [ {"role": "user", "content": "What's the weather in Paris?"}, {"role": "assistant", "content": [ {"type": "tool_use", "id": "toolu_1", "name": "get_weather", "input": {"city": "Paris"}} ]}, {"role": "user", "content": [ {"type": "tool_result", "tool_use_id": "toolu_1", "content": "18C and sunny"} ]} ] } ``` ## Vision On models whose catalog card has `wafer.capabilities.messages.vision: true`, send images as base64 `image` blocks: ```json theme={null} { "type": "image", "source": {"type": "base64", "media_type": "image/png", "data": ""} } ``` `url` image sources are also accepted; Wafer fetches them, and an image that can't be fetched fails the request with `400` code `model_request_rejected`. Base64 avoids fetch failures. Images sent to models without vision support fail with `400` code `model_request_rejected`. ## Count tokens `POST /v1/messages/count_tokens` takes the same body without `max_tokens` and returns the prompt token count: ```bash theme={null} curl -sS "https://pass.wafer.ai/v1/messages/count_tokens" \ -H "x-api-key: " \ -H "Content-Type: application/json" \ -d '{ "model": "GLM-5.3", "messages": [{"role": "user", "content": "Hello there"}] }' ``` ```json theme={null} {"input_tokens": 6} ``` If the exact count isn't available, the response is an estimate and carries the header `x-wafer-input-tokens-estimated: true`. ## Streaming With `"stream": true`, Wafer sends Anthropic-format server-sent events: `message_start`, then `content_block_start`, `content_block_delta`, and `content_block_stop` for each content block, then `message_delta` with `stop_reason` and final `usage`, then `message_stop`. Text arrives as `text_delta` deltas. Reasoning arrives in `thinking` blocks as `thinking_delta` deltas when thinking is on. Tool input arrives as `input_json_delta` deltas; concatenate `partial_json` to get the full input. Wafer doesn't send `ping` or `signature_delta` events. ```text theme={null} event: message_start data: {"type":"message_start","message":{"id":"msg_b9f3b97e1c8a43439bf3c8ccd73e4205","type":"message","role":"assistant","content":[],"model":"GLM-5.3","usage":{"input_tokens":0,"output_tokens":0,"cache_creation_input_tokens":0,"cache_read_input_tokens":0}}} event: content_block_start data: {"type":"content_block_start","index":0,"content_block":{"type":"tool_use","id":"toolu_01A09q90qw90lq917835lq9","name":"get_weather","input":{}}} event: content_block_delta data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"{"}} event: content_block_delta data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"\"city\": \"Paris\"}"}} event: content_block_stop data: {"type":"content_block_stop","index":0} event: message_delta data: {"type":"message_delta","delta":{"stop_reason":"tool_use"},"usage":{"input_tokens":278,"output_tokens":54,"cache_creation_input_tokens":0,"cache_read_input_tokens":0}} event: message_stop data: {"type":"message_stop"} ``` `message_start` carries zero usage. Read final usage from `message_delta`. If the connection closes before `message_stop`, the request failed. `message_stop` doesn't guarantee success: a failure during generation can end the stream normally with truncated output. See [Errors during streaming](/serverless/errors#errors-during-streaming). ## Errors Errors on `/v1/messages` always carry an `error` object with `type` and `message`, but the wrapper varies: * Authentication, access, unknown-model, credit, body-size, ZDR, and account-concurrency errors use the [Wafer error format](/serverless/errors#error-format), with `code` and `request_id`. * Model, context-length, and capacity errors use Anthropic's shape, `{"type": "error", "error": {...}}`, with `code`. * Request-validation errors use Anthropic's shape without `code`. Anthropic SDKs pick the exception class from the HTTP status, so they handle all three. In your own code, branch on the HTTP status, then on `error.code` when present.