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

# 3. Discover models

> List the chat and embedding models available on your deployment.

Before you call chat or embeddings, list what's actually available on this deployment. Which models appear depends on how your administrator has configured backends — self-hosted (Ollama, vLLM) and cloud (OpenAI, Anthropic, Mistral, Gemini, Groq, SambaNova, Cohere) can be mixed.

## List models

```bash theme={null}
curl {BASE_URL}/v1/models \
  -H "Authorization: Bearer $PAT"
```

```json theme={null}
{
  "object": "list",
  "data": [
    { "id": "llama3.2:3b", "object": "model", "provider": "ollama" },
    { "id": "nomic-embed-text", "object": "model", "provider": "ollama" },
    { "id": "gpt-4o", "object": "model", "provider": "openai" }
  ]
}
```

## Picking the right one

Chat-class and embedding-class models are separate — chat models won't produce embeddings and vice versa. The `id` field is what you'll pass to `/v1/chat/completions`, `/v1/embeddings`, and the knowledge-base chat endpoint.

<Tip>
  Save a chat model id and an embedding model id from this list — you'll use both in the next few steps.

  ```bash theme={null}
  export CHAT_MODEL="llama3.2:3b"
  export EMBED_MODEL="nomic-embed-text"
  ```
</Tip>

Next: [Compute embeddings →](/guides/api-walkthrough/embeddings)