> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Helicone/helicone/llms.txt
> Use this file to discover all available pages before exploring further.
# List Models
> Get available models through the Helicone AI Gateway
## Overview
The models endpoint returns a list of all available models that can be used with the Helicone AI Gateway. This endpoint is OpenAI-compatible and returns models in the same format as OpenAI's `/v1/models` endpoint.
## Authentication
This endpoint does **not** require authentication and can be called without an API key.
## Endpoint
```bash theme={null}
GET https://ai-gateway.helicone.ai/v1/models
```
## Request
This endpoint accepts no query parameters and returns all available models.
```bash cURL theme={null}
curl https://ai-gateway.helicone.ai/v1/models
```
```python Python theme={null}
import requests
response = requests.get(
"https://ai-gateway.helicone.ai/v1/models"
)
models = response.json()
print(f"Available models: {len(models['data'])}")
```
```javascript JavaScript theme={null}
fetch('https://ai-gateway.helicone.ai/v1/models')
.then(response => response.json())
.then(data => {
console.log(`Available models: ${data.data.length}`);
data.data.forEach(model => {
console.log(`- ${model.id}`);
});
});
```
```typescript TypeScript theme={null}
interface Model {
id: string;
object: 'model';
created: number;
owned_by: string;
}
interface ModelsResponse {
object: 'list';
data: Model[];
}
const response = await fetch('https://ai-gateway.helicone.ai/v1/models');
const models: ModelsResponse = await response.json();
console.log(`Found ${models.data.length} models`);
```
## Response Format
Always returns `list` to indicate this is a list response.
Array of model objects available through the gateway.
The model identifier. This is the string you use in the `model` parameter when making chat completion requests.
Examples: `gpt-4`, `gpt-3.5-turbo`, `claude-3-opus-20240229`, `gemini-pro`
Object type, always `model`.
Unix timestamp of when the model was created or added to the registry.
The organization or company that created the model.
Examples: `openai`, `anthropic`, `google`, `meta`, `mistral`
## Example Response
```json theme={null}
{
"object": "list",
"data": [
{
"id": "gpt-4",
"object": "model",
"created": 1704067200,
"owned_by": "openai"
},
{
"id": "gpt-4-turbo",
"object": "model",
"created": 1704067200,
"owned_by": "openai"
},
{
"id": "gpt-3.5-turbo",
"object": "model",
"created": 1704067200,
"owned_by": "openai"
},
{
"id": "claude-3-opus-20240229",
"object": "model",
"created": 1704067200,
"owned_by": "anthropic"
},
{
"id": "claude-3-sonnet-20240229",
"object": "model",
"created": 1704067200,
"owned_by": "anthropic"
},
{
"id": "claude-3-haiku-20240307",
"object": "model",
"created": 1704067200,
"owned_by": "anthropic"
},
{
"id": "gemini-pro",
"object": "model",
"created": 1704067200,
"owned_by": "google"
},
{
"id": "gemini-1.5-pro",
"object": "model",
"created": 1704067200,
"owned_by": "google"
},
{
"id": "llama-3-70b",
"object": "model",
"created": 1704067200,
"owned_by": "meta"
},
{
"id": "mistral-large",
"object": "model",
"created": 1704067200,
"owned_by": "mistral"
}
]
}
```
## Model Filtering
The endpoint automatically filters out models that:
* Require explicit routing configuration
* Are not available through the standard gateway endpoints
* Have no registered endpoints in the cost registry
This ensures you only see models that are immediately usable with the chat completions endpoint.
## Using Model IDs
The `id` field from each model object can be used directly in your chat completion requests:
```json theme={null}
{
"model": "gpt-4",
"messages": [
{"role": "user", "content": "Hello!"}
]
}
```
## Model Support
The Helicone AI Gateway supports models from multiple providers:
* **OpenAI**: GPT-4, GPT-3.5, and variants
* **Anthropic**: Claude 3 family (Opus, Sonnet, Haiku)
* **Google**: Gemini models
* **Meta**: Llama models
* **Mistral**: Mistral and Mixtral models
* **And many more**: The registry is continuously updated with new models
## Error Handling
Error information if the request fails.
Human-readable error message describing what went wrong.
Error type, typically `internal_error` for this endpoint.
### Example Error Response
```json theme={null}
{
"error": {
"message": "Failed to fetch models from registry",
"type": "internal_error"
}
}
```
## Implementation Details
The models list is generated from Helicone's internal cost registry (`@helicone-package/cost/models/registry`), which:
* Tracks pricing and capabilities for each model
* Is automatically synced with provider model releases
* Includes metadata like creation dates and ownership
* Powers cost calculation and analytics in Helicone
## Rate Limits
This endpoint has generous rate limits as it's a read-only operation that doesn't consume compute resources. It can be called frequently for model discovery without concerns about rate limiting.
## Best Practices
* **Cache the response**: The model list doesn't change frequently, so cache it client-side to reduce API calls
* **Filter by provider**: If you only use specific providers, filter the response by the `owned_by` field
* **Dynamic model selection**: Use this endpoint to build dynamic model selection UIs
* **Validation**: Validate user-provided model IDs against this list before making completion requests
## Client-Side Filtering Example
```javascript theme={null}
// Get all OpenAI models
const openaiModels = models.data.filter(m => m.owned_by === 'openai');
// Get all Claude models
const claudeModels = models.data.filter(m => m.owned_by === 'anthropic');
// Check if a model exists
const hasGPT4 = models.data.some(m => m.id === 'gpt-4');
// Get the newest models (sort by created timestamp)
const newestModels = [...models.data]
.sort((a, b) => b.created - a.created)
.slice(0, 5);
```
## Related Endpoints
* [Chat Completions](/api/gateway/chat-completions) - Use models from this list to create completions