---
title: Chat Completions
description: POST /v1/chat/completions — the OpenAI-compatible chat endpoint of Clipia AI Gateway. Non-streaming and streaming responses, parameters, tool calling and structured outputs.
---

`POST /v1/chat/completions` is the core endpoint of Clipia AI Gateway, fully compatible with OpenAI Chat Completions. It takes a `messages` array and returns the assistant's response in non-streaming or streaming mode, with support for tool calling and structured output by JSON schema.

<Method name="POST" path="/v1/chat/completions" />

## Non-streaming response

<Tabs items={['Python', 'Node.js', 'cURL']}>
<Tab value="Python">

```python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["CLIPIA_API_KEY"],
    base_url="https://api.clipia.ai/v1",
)

resp = client.chat.completions.create(
    model="claude-opus-5",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Tell me three facts about Mars."},
    ],
    temperature=0.7,
    max_tokens=512,
)

print(resp.choices[0].message.content)
print(resp.usage)
```

</Tab>
<Tab value="Node.js">

```ts
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.CLIPIA_API_KEY,
  baseURL: "https://api.clipia.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "claude-opus-5",
  messages: [
    { role: "system", content: "You are a concise assistant." },
    { role: "user", content: "Tell me three facts about Mars." },
  ],
  temperature: 0.7,
  max_tokens: 512,
});

console.log(resp.choices[0].message.content);
```

</Tab>
<Tab value="cURL">

```bash
curl https://api.clipia.ai/v1/chat/completions \
  -H "Authorization: Bearer $CLIPIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-5",
    "messages": [
      { "role": "system", "content": "You are a concise assistant." },
      { "role": "user", "content": "Tell me three facts about Mars." }
    ],
    "temperature": 0.7,
    "max_tokens": 512
  }'
```

</Tab>
</Tabs>

**Response `200`**

```json
{
  "id": "chatcmpl-3f9a1c7e2b41",
  "object": "chat.completion",
  "created": 1782300000,
  "model": "claude-opus-5",
  "provider": "Clipia",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "1. ...\n2. ...\n3. ..." },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 28,
    "completion_tokens": 64,
    "total_tokens": 92,
    "cost": 0.046
  }
}
```

`usage.cost` is the cost of the request in **credits**. `finish_reason` is normalized to the OpenAI enum: `stop`, `length`, `tool_calls`, `content_filter`.

## Streaming response

Pass `stream: true` — the response arrives incrementally as Server-Sent Events. Each event is a `chat.completion.chunk` with a delta; the stream ends with the literal `data: [DONE]`.

<Tabs items={['Python', 'Node.js', 'cURL']}>
<Tab value="Python">

```python
stream = client.chat.completions.create(
    model="claude-opus-5",
    messages=[{"role": "user", "content": "Write a haiku about the sea."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.content:
        print(delta.content, end="", flush=True)
```

</Tab>
<Tab value="Node.js">

```ts
const stream = await client.chat.completions.create({
  model: "claude-opus-5",
  messages: [{ role: "user", content: "Write a haiku about the sea." }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}
```

</Tab>
<Tab value="cURL">

```bash
curl -N https://api.clipia.ai/v1/chat/completions \
  -H "Authorization: Bearer $CLIPIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-5",
    "messages": [{ "role": "user", "content": "Write a haiku about the sea." }],
    "stream": true
  }'
```

</Tab>
</Tabs>

**Event stream**

```text
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1782300000,"model":"claude-opus-5","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","model":"claude-opus-5","choices":[{"index":0,"delta":{"content":"Waves "},"finish_reason":null}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","model":"claude-opus-5","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]
```

<Callout type="info" title="Final usage in a stream">
To receive `usage` while streaming, pass `stream_options: { "include_usage": true }`. An extra chunk with a populated `usage` and an empty `choices: []` is then sent right before `data: [DONE]`.
</Callout>

## Request parameters

<TypeTable
  type={{
    model: { type: 'string', description: 'Model identifier, e.g. claude-opus-5 (see the /v1/models catalog).' },
    messages: { type: 'object[]', description: 'Conversation history. role: system | user | assistant | tool (developer is treated as system). content is a string or an array of parts.' },
    temperature: { type: 'number', description: 'Randomness, typically 0–2. Defaults to the model default.' },
    top_p: { type: 'number', description: 'Nucleus sampling, 0–1. An alternative to temperature.' },
    max_tokens: { type: 'integer', description: 'Max tokens in the response. The max_completion_tokens alias is also accepted.' },
    stream: { type: 'boolean', description: 'true — streaming response (SSE). Defaults to false.' },
    stream_options: { type: 'object', description: '{ include_usage: true } — add a final usage chunk to the stream.' },
    stop: { type: 'string | string[]', description: 'Up to 4 stop sequences.' },
    seed: { type: 'integer', description: 'Request determinism (best-effort).' },
    n: { type: 'integer', description: 'How many response variants to generate. Defaults to 1.' },
    presence_penalty: { type: 'number', description: 'Penalty for repeating topics, −2…2.' },
    frequency_penalty: { type: 'number', description: 'Penalty for repeating tokens, −2…2.' },
    response_format: { type: 'object', description: '{ type: "json_object" } or { type: "json_schema", json_schema: {...} } — structured output.' },
    tools: { type: 'object[]', description: 'Descriptions of functions the model may call.' },
    tool_choice: { type: 'string | object', description: 'auto | none | required | { type: "function", function: { name } }.' },
    parallel_tool_calls: { type: 'boolean', description: 'Allow multiple tool calls in one turn.' },
    user: { type: 'string', description: 'Stable end-user identifier (for analytics / anti-abuse).' },
  }}
/>

<Callout type="info" title="Parameter compatibility">
Both token-limit names are accepted — `max_tokens` and `max_completion_tokens`. Unknown top-level fields are ignored (they do not cause a `400`), so code written for OpenAI ports over unchanged.
</Callout>

## Tool / function calling

Full cycle: you describe tools in `tools`, the model returns `tool_calls`, you run the function on your side and send the result back as a message with `role: "tool"`, after which the model produces the final answer.

```python
import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Current weather in a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"},
                },
                "required": ["city"],
            },
        },
    }
]

messages = [{"role": "user", "content": "What's the weather in Moscow right now?"}]

# 1) The model decides to call a tool
resp = client.chat.completions.create(
    model="claude-opus-5", messages=messages, tools=tools,
)
msg = resp.choices[0].message
# resp.choices[0].finish_reason == "tool_calls"

# 2) Run the function on your side
call = msg.tool_calls[0]
args = json.loads(call.function.arguments)   # {"city": "Moscow"}
result = {"temp_c": 14, "condition": "cloudy"}

# 3) Send the result back and get the final answer
messages.append(msg)  # assistant echo with tool_calls
messages.append({
    "role": "tool",
    "tool_call_id": call.id,
    "content": json.dumps(result),
})

final = client.chat.completions.create(
    model="claude-opus-5", messages=messages, tools=tools,
)
print(final.choices[0].message.content)
```

When streaming, `tool_calls` fragments arrive grouped by `index`: the function name and `id` come in the first delta for that index, then `function.arguments` fragments you concatenate into valid JSON. A completed tool call is marked with `finish_reason: "tool_calls"`.

## Structured outputs

To force a response that matches a JSON schema, pass `response_format` with `type: "json_schema"` and `strict: true`.

```python
resp = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "Extract name and age: Anna is 30."}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"},
                },
                "required": ["name", "age"],
                "additionalProperties": False,
            },
        },
    },
)

print(resp.choices[0].message.content)  # {"name": "Anna", "age": 30}
```

<Callout type="info" title="json_object vs json_schema">
`response_format: { "type": "json_object" }` guarantees valid JSON but without a specific schema — and requires the word "json" to appear in `messages`. For strict structural conformance use `json_schema` with `strict: true`.
</Callout>

## Errors

Errors arrive in the standard OpenAI envelope: `{ "error": { "message", "type", "param", "code" } }`, always with `Content-Type: application/json` and the correct HTTP status.

| Status | Code | When |
|--------|------|------|
| `400` | `invalid_request_error` | Malformed request body or parameters. |
| `401` | `invalid_api_key` | Missing or invalid key. |
| `402` | `insufficient_credits` | Not enough credits on the balance. |
| `404` | `model_not_found` | Unknown `model`. |
| `429` | `rate_limit_exceeded` | Rate limit exceeded — see [Account & limits](/en/docs/llm-gateway/account). |

```json
{
  "error": {
    "message": "Not enough credits on the balance.",
    "type": "insufficient_credits",
    "param": null,
    "code": "insufficient_credits"
  }
}
```