---
title: Using the Chat API
description: This page explains how to use the Chat API to query models
tags: generative-apis ai-data chat-api
dates:
  validation: 2025-09-22
  posted: 2024-09-03
---

Scaleway Generative APIs are designed as a drop-in replacement for the OpenAI APIs. If you have an LLM-driven application that uses one of OpenAI's client libraries, you can easily configure it to point to Scaleway's Chat API, and get your existing applications running using open-weight instruct models hosted at Scaleway.

<Message type="note">
You can also try the **Responses** API for LLM-driven tasks. Released in 2025, it is designed to combine the simplicity of Chat Completions with the ability to do more agentic tasks and reasoning. [Find out more about the Responses API](/generative-apis/how-to/query-language-models/#chat-completions-api-or-responses-api).
</Message>

## Create chat completion

Creates a model response for the given chat conversation.

**Request sample:**

```
curl --request POST \
     --url https://api.scaleway.ai/v1/chat/completions \
     --header 'Authorization: Bearer ${SCW_SECRET_KEY}' \
     --header 'Content-Type: application/json' \
     --data '{
     "model": "llama-3.1-8b-instruct",
     "messages": [
      {
        "role": "system",
        "content": "<string>"
      },
      {
        "role": "user",
        "content": "<string>"
      }
     ],
     "max_tokens": integer,
     "temperature": float,
     "top_p": float,
     "presence_penalty": float,
     "stop": "<string>",
     "stream": boolean,
     }'
```


## Headers

For information about the required headers, see the [Using Generative APIs](/generative-apis/api-cli/using-generative-apis/) page.

## Body

### Required parameters

| Param  | Type | Description |
| ------------- |-------------|-------------|
| **messages**     | array of objects     | A list of messages comprising the conversation so far.     |
| **model**      | string     | The name of the model to query.     |

Our Chat API is OpenAI compatible. Refer to OpenAI’s [API reference](https://platform.openai.com/docs/api-reference/chat/create) for detailed information on usage.

### Supported parameters

- `temperature`
- `top_p`
- `max_tokens`
- `stream`
- `stream_options`
- `presence_penalty`
- `response_format` (For more information, see [How to use structured outputs](/generative-apis/how-to/use-structured-outputs).)
- `logprobs`
- `stop`
- `seed`
- `tools` (For more information, see [How to use function calling](/generative-apis/how-to/use-function-calling).)
- `tool_choice` (For more information, see [How to use function calling](/generative-apis/how-to/use-function-calling).)

### Unsupported parameters

- `frequency_penalty`
- `n`
- `top_logprobs`
- `logit_bias`
- `user`

If you have a use case requiring one of these unsupported parameters, [contact us via Slack](https://slack.scaleway.com/) using the `#ai` channel.

## Going further

1. [Python code examples](/generative-apis/how-to/query-language-models/#querying-language-models-via-api) to query text models using Scaleway's Chat API
2. [How to use structured outputs](/generative-apis/how-to/use-structured-outputs) with the `response_format` parameter
3. [How to use function calling](/generative-apis/how-to/use-function-calling) with the `tools` and `tool_choice` parameters