Chat and streaming

POST /v1/chat/completions is the main endpoint. Request and response match OpenAI, so any compatible SDK works.

Parameters

Field	Type	Description
model	string, required	Catalog identifier or alias (gpt-4o, claude-opus)
messages	array, required	Conversation so far; the last user message is the prompt
stream	boolean	true streams the answer over SSE
stream_options	object	{"include_usage": true} appends a final chunk with token usage
temperature	number	As in OpenAI
max_tokens	integer	Ceiling on answer length
max_completion_tokens	integer	Synonym for max_tokens, for newer SDKs
reasoning_effort	string	Reasoning depth on models that support it

Streaming

Python

stream = client.chat.completions.create(
    model="gpt-5.6-luna",
    messages=[{"role": "user", "content": "Write a haiku about deadlines"}],
    stream=True,
    stream_options={"include_usage": True},
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

TypeScript

const stream = await client.chat.completions.create({
  model: 'gpt-5.6-luna',
  messages: [{ role: 'user', content: 'Write a haiku about deadlines' }],
  stream: true,
  stream_options: { include_usage: true },
})
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '')
}

cURL

curl -N https://api.mixen.ai/v1/chat/completions \
  -H "Authorization: Bearer $MIXEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.6-luna",
    "messages": [{"role": "user", "content": "Write a haiku about deadlines"}],
    "stream": true,
    "stream_options": {"include_usage": true}
  }'

The stream is text/event-stream and ends with data: [DONE]. With include_usage, a chunk carrying usage arrives just before it — a convenient place to log spend.

Image input

Models with capabilities.vision: true accept an image inside a message:

resp = client.chat.completions.create(
    model="gpt-5.6-terra",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's wrong with this chart?"},
            {"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBORw0KG..."}},
        ],
    }],
)

Both data: URLs and ordinary HTTPS image links are accepted.

File input

Models with capabilities.file: true read a whole PDF — scans and tables included, no external OCR:

{
  "role": "user",
  "content": [
    {"type": "text", "text": "Summarise the Risks section"},
    {"type": "file", "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0..."}}
  ]
}

A model that cannot read files returns 400, so check the flag in the catalog first.

Reasoning effort

Some models let you dial reasoning depth. The accepted values arrive in capabilities.reasoning_efforts — usually low, medium, high, xhigh, max.

{"model": "claude-opus-4.8", "reasoning_effort": "high", "messages": [...]}

Two things to watch:

• Reasoning consumes the max_tokens budget. If you expect long JSON from a model that reasons, set the ceiling generously or the answer gets cut mid-structure.
• Reasoning cannot always be switched off — check capabilities.can_disable_reasoning. Where it reads false, the model always reasons, and the price reflects that.

Web search

Models with type: search (the Perplexity Sonar family) reach the internet themselves. There is no separate endpoint or tool — just name one:

curl https://api.mixen.ai/v1/chat/completions \
  -H "Authorization: Bearer $MIXEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sonar-pro","messages":[{"role":"user","content":"What did Anthropic ship this week?"}]}'

On a scoped key these models need the search scope rather than chat.