> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inferoute.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How to Select and Use Models Across Providers in Inferoute

> Specify models by provider-prefixed name, short alias, or use 'auto' to let Inferoute select the best available model for your request.

Inferoute uses the same `model` parameter you already know from the OpenAI API. You pass a model name in your request, and Inferoute resolves it to the appropriate provider and model version. You can be as specific as you want — pinning a request to a particular provider and model — or as general as you want, delegating the selection to Inferoute entirely.

<Tip>
  Use short aliases like `gpt-4o` and `claude-3-5-sonnet` instead of provider-prefixed names like `openai/gpt-4o`. Aliases keep your code provider-agnostic, making it easy to swap providers without modifying requests.
</Tip>

## Model naming formats

Inferoute supports three model naming formats.

### Provider-prefixed names

Fully qualified names that pin a request to a specific provider. Use this format when you need to guarantee which provider handles the request.

```
openai/gpt-4o
anthropic/claude-3-5-sonnet
google/gemini-1.5-pro
mistral/mistral-large
meta/llama-3.1-70b
```

### Short aliases

Shorthand names that map to a canonical model across providers. Inferoute resolves the alias to the best available endpoint for that model.

| Alias               | Resolves to                 |
| ------------------- | --------------------------- |
| `gpt-4o`            | OpenAI GPT-4o               |
| `gpt-4`             | OpenAI GPT-4                |
| `gpt-3.5-turbo`     | OpenAI GPT-3.5 Turbo        |
| `claude-3-5-sonnet` | Anthropic Claude 3.5 Sonnet |
| `claude-3-opus`     | Anthropic Claude 3 Opus     |
| `claude-3-haiku`    | Anthropic Claude 3 Haiku    |
| `gemini-1.5-pro`    | Google Gemini 1.5 Pro       |
| `gemini-1.5-flash`  | Google Gemini 1.5 Flash     |
| `mistral-large`     | Mistral Large               |

### Auto selection

Setting `model` to `"auto"` tells Inferoute to pick the most suitable model for your request based on its content, your active routing strategy, and current provider availability.

```python theme={null}
response = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Write a Python function to parse CSV files."}],
)
```

## Code examples

The following examples show how to use each naming format in a standard chat completion call.

<CodeGroup>
  ```python python theme={null}
  import openai

  client = openai.OpenAI(
      base_url="https://api.inferoute.ai/v1",
      api_key="YOUR_INFEROUTE_API_KEY",
  )

  # Provider-prefixed: always uses Anthropic
  response = client.chat.completions.create(
      model="anthropic/claude-3-5-sonnet",
      messages=[{"role": "user", "content": "What is the boiling point of water?"}],
  )

  # Short alias: provider-agnostic
  response = client.chat.completions.create(
      model="gpt-4o",
      messages=[{"role": "user", "content": "What is the boiling point of water?"}],
  )

  # Auto: Inferoute decides
  response = client.chat.completions.create(
      model="auto",
      messages=[{"role": "user", "content": "What is the boiling point of water?"}],
  )

  print(response.choices[0].message.content)
  ```

  ```javascript javascript theme={null}
  import OpenAI from "openai";

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

  // Provider-prefixed: always uses Google
  const response = await client.chat.completions.create({
    model: "google/gemini-1.5-pro",
    messages: [{ role: "user", content: "What is the boiling point of water?" }],
  });

  // Short alias: provider-agnostic
  const response2 = await client.chat.completions.create({
    model: "gemini-1.5-pro",
    messages: [{ role: "user", content: "What is the boiling point of water?" }],
  });

  // Auto: Inferoute decides
  const response3 = await client.chat.completions.create({
    model: "auto",
    messages: [{ role: "user", content: "What is the boiling point of water?" }],
  });

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

  ```bash bash theme={null}
  # Provider-prefixed
  curl https://api.inferoute.ai/v1/chat/completions \
    -H "Authorization: Bearer $INFEROUTE_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"model": "mistral/mistral-large", "messages": [{"role": "user", "content": "Hello"}]}'

  # Short alias
  curl https://api.inferoute.ai/v1/chat/completions \
    -H "Authorization: Bearer $INFEROUTE_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"model": "mistral-large", "messages": [{"role": "user", "content": "Hello"}]}'

  # Auto
  curl https://api.inferoute.ai/v1/chat/completions \
    -H "Authorization: Bearer $INFEROUTE_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"model": "auto", "messages": [{"role": "user", "content": "Hello"}]}'
  ```
</CodeGroup>

## Model capabilities

Different models support different features. The table below summarizes key capabilities for the models available on Inferoute.

| Model             | Context window | Multimodal input     | Max output tokens |
| ----------------- | -------------- | -------------------- | ----------------- |
| GPT-4o            | 128K tokens    | Images, audio        | 16K tokens        |
| GPT-4             | 128K tokens    | Images               | 8K tokens         |
| GPT-3.5 Turbo     | 16K tokens     | —                    | 4K tokens         |
| Claude 3.5 Sonnet | 200K tokens    | Images, documents    | 8K tokens         |
| Claude 3 Opus     | 200K tokens    | Images, documents    | 4K tokens         |
| Claude 3 Haiku    | 200K tokens    | Images               | 4K tokens         |
| Gemini 1.5 Pro    | 1M tokens      | Images, video, audio | 8K tokens         |
| Gemini 1.5 Flash  | 1M tokens      | Images, video, audio | 8K tokens         |
| Mistral Large     | 128K tokens    | —                    | 4K tokens         |
| Llama 3.1 70B     | 128K tokens    | —                    | 4K tokens         |

To list all currently available models and their capabilities programmatically, use the [`GET /v1/models`](/docs/api-reference/models) endpoint.