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

curl -sS "https://pass.wafer.ai/v1/messages" \
  -H "x-api-key: <YOUR_WAFER_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.
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. Some models use fixed sampling; see the note on sampling.
stream	Stream server-sent events. See Streaming.

metadata and cache_control are accepted and ignored. Prompt caching is automatic; see Prompt caching.

Response

{
  "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.

Thinking

On /v1/messages, reasoning is off unless you enable it, whatever the model’s default on other APIs.

{
  "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.

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:

{
  "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:

{
  "type": "image",
  "source": {"type": "base64", "media_type": "image/png", "data": "<BASE64_IMAGE>"}
}

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:

curl -sS "https://pass.wafer.ai/v1/messages/count_tokens" \
  -H "x-api-key: <YOUR_WAFER_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "GLM-5.3",
    "messages": [{"role": "user", "content": "Hello there"}]
  }'

{"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.

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 generation fails after the stream starts, Wafer sends an error event and ends the stream without message_stop. If the connection closes before message_stop, the request failed too. See Errors during streaming.

Errors

Errors on /v1/messages and /v1/messages/count_tokens use Anthropic’s error format, with Wafer’s code and request_id added:

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "tools[0].name is required",
    "code": "missing_tool_name",
    "request_id": "20b6009020a3"
  }
}

Anthropic SDKs pick the exception class from the HTTP status. In your own code, branch on the HTTP status, then on error.code. See Errors and Rate Limits for the codes.