Chat Completions · Docs

POST /v1/chat/completions is the main inference endpoint. It follows the OpenAI Chat Completions format, so existing OpenAI clients work by changing only the base URL, the key, and the model id.

from openai import OpenAI

client = OpenAI(base_url="https://api.kurrens.ai/v1", api_key="<KURRENS_API_KEY>")

completion = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Explain KV caching in two sentences."},
    ],
    temperature=0.6,
    max_tokens=512,
)
print(completion.choices[0].message.content)
print(completion.usage)

import OpenAI from 'openai';

const client = new OpenAI({ baseURL: 'https://api.kurrens.ai/v1', apiKey: process.env.KURRENS_API_KEY });

const completion = await client.chat.completions.create({
  model: 'Qwen/Qwen3.8-27B',
  messages: [
    { role: 'system', content: 'You are a concise assistant.' },
    { role: 'user', content: 'Explain KV caching in two sentences.' },
  ],
  temperature: 0.6,
  max_tokens: 512,
});
console.log(completion.choices[0].message.content, completion.usage);

curl https://api.kurrens.ai/v1/chat/completions \
  -H "Authorization: Bearer $KURRENS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3.8-27B",
    "messages": [
      {"role": "system", "content": "You are a concise assistant."},
      {"role": "user", "content": "Explain KV caching in two sentences."}
    ],
    "temperature": 0.6,
    "max_tokens": 512
  }'

Messages

Role	Purpose
`system` / `developer`	Instructions that shape the model’s behavior
`user`	Your input — text, or text plus images for vision models
`assistant`	Earlier model turns, including `tool_calls`
`tool`	Results of a tool call, with `tool_call_id`

Common parameters

Parameter	Notes
`model`	Required. A model id from the Model catalog
`max_tokens`	Upper bound on generated tokens, including reasoning tokens
`temperature`, `top_p`	Sampling controls
`stop`	Up to 4 stop sequences
`stream`	Stream tokens as server-sent events — see Streaming
`tools`, `tool_choice`	Tool calling
`response_format`	Structured outputs
`seed`	Best-effort reproducibility
`service_tier`	`priority` or `flex` where offered

Supported parameters and their ranges vary by model; the full per-model list is published in models.json. Unsupported parameters return 400.

Usage

Every response includes a usage object with prompt_tokens, completion_tokens, and — where applicable — cached and reasoning token counts. This is what you’re billed for.

Data handling

The request and response are processed in memory and discarded when the response completes. See Zero data retention.