Chat Completions API

/ API docs

POST https://api.opencheese.dev/v1/chat/completions is the OpenAI Chat Completions API. The OpenAI SDKs, OpenCode, Zed, Harness AI and most chat apps use it.

A first request

bash

curl https://api.opencheese.dev/v1/chat/completions \
  -H "Authorization: Bearer $OPENCHEESE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "MODEL_ID", "messages": [{"role": "system", "content": "Answer in one sentence."}, {"role": "user", "content": "What is a gateway?"}]}'

model and messages are required. Take the model ID from GET /v1/models.

The answer has the usual shape:

json

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "model": "MODEL_ID",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "A gateway passes your requests on to the models behind it." },
      "finish_reason": "stop"
    }
  ],
  "usage": { "prompt_tokens": 24, "completion_tokens": 13, "total_tokens": 37 }
}

Parameters

Field	Notes
`model`	Required. An `id` from `/v1/models`.
`messages`	Required. `system`, `user`, `assistant` and `tool` messages.
`stream`	`true` for server-sent events.
`tools`, `tool_choice`	Function calling, where the model supports tools.
`reasoning_effort`	A level from the model's `supported_reasoning_levels`.
`response_format`	Structured output, where the model supports it.
`temperature`, `top_p`, `max_tokens`	Optional. If you don't set them, the model's defaults are used.

Streaming

bash

curl -N https://api.opencheese.dev/v1/chat/completions \
  -H "Authorization: Bearer $OPENCHEESE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "MODEL_ID", "stream": true, "messages": [{"role": "user", "content": "Count to three."}]}'

Each event is a chat.completion.chunk; the text arrives in choices[0].delta.content. Near the end come the finish_reason and the usage, and the stream ends with data: [DONE]:

text

data: {"object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","content":"One"},"finish_reason":null}]}

data: {"object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":", two, three."},"finish_reason":null}]}

data: {"object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: {"object":"chat.completion.chunk","choices":[],"usage":{"prompt_tokens":12,"completion_tokens":6,"total_tokens":18}}

data: [DONE]

Tool calls

bash

curl https://api.opencheese.dev/v1/chat/completions \
  -H "Authorization: Bearer $OPENCHEESE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "MODEL_ID", "messages": [{"role": "user", "content": "What is the weather in Paris?"}], "tools": [{"type": "function", "function": {"name": "get_weather", "description": "Current weather for a city", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}}}]}'

When the model calls a tool, the reply's finish_reason is tool_calls and message.tool_calls holds the call. Run the tool, then send the history back with a tool message carrying the result:

json

{
  "model": "MODEL_ID",
  "messages": [
    { "role": "user", "content": "What is the weather in Paris?" },
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [{ "id": "call_1", "type": "function", "function": { "name": "get_weather", "arguments": "{\"city\":\"Paris\"}" } }]
    },
    { "role": "tool", "tool_call_id": "call_1", "content": "18 degrees, sunny" }
  ]
}

A reply cut off by the output limit in the middle of a tool call ends with finish_reason: "length": treat its tool call as incomplete and do not run it.

Images in the prompt

Models whose modalities.input includes image read pictures sent as image_url parts, either an https:// address or a data: URL:

json

{
  "model": "MODEL_ID",
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "text", "text": "What is in this picture?" },
        { "type": "image_url", "image_url": { "url": "data:image/png;base64,iVBORw0KGgo..." } }
      ]
    }
  ]
}

Reasoning effort

Set reasoning_effort to one of the levels the model lists in supported_reasoning_levels (reasoning.effort and Anthropic's output_config.effort are read too). If you don't set one, the model's default is used.

bash

curl https://api.opencheese.dev/v1/chat/completions \
  -H "Authorization: Bearer $OPENCHEESE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "MODEL_ID", "reasoning_effort": "high", "messages": [{"role": "user", "content": "Plan a three-day trip to Lyon."}]}'

A level the model doesn't offer is refused with unsupported_reasoning_effort, and the error lists the levels it does offer. Reasoning text, where the model returns it, arrives in message.reasoning_content (delta.reasoning_content when streaming).

Image models in a chat

Choose an image model (its modalities.output is image) and the latest user message becomes the image prompt. The picture arrives in message.images as a data: URL, and an image sent in the conversation is edited where the model can edit. Image models answer one picture per request; for every image parameter use the Image API.

Errors that arrive as a reply

Problems you need to act on, such as an invalid key, an exhausted plan or an empty balance, come back as the assistant's message in a 200 response streamed as text/event-stream, even when stream is false, so chat apps show them to you. Every other error is a normal HTTP status with a JSON body. Check the response's Content-Type before parsing it as JSON; see Errors and rate limits.

SDKs

Python:

python

from openai import OpenAI

client = OpenAI(base_url="https://api.opencheese.dev/v1", api_key="YOUR_OPENCHEESE_API_KEY")
stream = client.chat.completions.create(
    model="MODEL_ID",
    messages=[{"role": "user", "content": "Write a haiku about cheese."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

JavaScript:

javascript

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.opencheese.dev/v1", apiKey: process.env.OPENCHEESE_API_KEY });
const reply = await client.chat.completions.create({
  model: "MODEL_ID",
  messages: [{ role: "user", content: "Write a haiku about cheese." }]
});
console.log(reply.choices[0].message.content);

To keep a conversation's requests together in the dashboard, add the opencheese-session-uuid header.

The cheese has holes. Your infrastructure should not.