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

# List LLM Models

> Use the /v3/models endpoint to retrieve all LLM models available through Eden AI, along with their capabilities: PDF support, reasoning, web search, and tool calling.

export const TechArticleSchema = ({title, description, path, articleSection, about, proficiencyLevel = "Beginner", dependencies, keywords = [], datePublished, dateModified, image, inLanguage = "en"}) => {
  const baseUrl = "https://www.edenai.co/docs";
  const canonicalUrl = `${baseUrl}/${path}`.replace(/\/+$/, "");
  const ogParams = new URLSearchParams({
    division: articleSection || "",
    title: title || "",
    description: description || ""
  });
  const resolvedImage = image || `https://edenai.mintlify.app/_mintlify/api/og?${ogParams.toString()}`;
  const data = {
    "@context": "https://schema.org",
    "@type": "TechArticle",
    "@id": `${canonicalUrl}#techarticle`,
    mainEntityOfPage: {
      "@type": "WebPage",
      "@id": canonicalUrl
    },
    headline: title,
    name: title,
    description: description,
    url: canonicalUrl,
    inLanguage: inLanguage,
    isPartOf: {
      "@type": "WebSite",
      name: "Eden AI Documentation",
      url: baseUrl
    },
    author: [{
      "@type": "Organization",
      name: "Eden AI",
      url: "https://www.edenai.co/"
    }],
    publisher: {
      "@type": "Organization",
      name: "Eden AI",
      url: "https://www.edenai.co/",
      logo: {
        "@type": "ImageObject",
        url: "https://www.edenai.co/assets/logo.png"
      }
    }
  };
  if (articleSection) data.articleSection = articleSection;
  if (about) data.about = {
    "@type": "Thing",
    name: about
  };
  if (proficiencyLevel) data.proficiencyLevel = proficiencyLevel;
  if (dependencies) data.dependencies = dependencies;
  if (keywords && keywords.length) data.keywords = keywords;
  if (datePublished) data.datePublished = datePublished;
  if (dateModified) data.dateModified = dateModified;
  data.image = Array.isArray(resolvedImage) ? resolvedImage : [resolvedImage];
  const json = JSON.stringify(data);
  const schemaId = `techarticle-${canonicalUrl}`;
  React.useEffect(() => {
    if (typeof document === "undefined") return;
    document.querySelectorAll(`script[data-schema-id="${schemaId}"]`).forEach(n => n.remove());
    const script = document.createElement("script");
    script.type = "application/ld+json";
    script.dataset.schemaId = schemaId;
    script.textContent = json;
    document.head.appendChild(script);
    return () => script.remove();
  }, [json, schemaId]);
  return null;
};

<TechArticleSchema title={"List LLM Models"} description={"Use the /v3/models endpoint to retrieve all LLM models available through Eden AI, along with their capabilities: PDF support, reasoning, web search, and tool calling."} path="v3/llms/listing-models" articleSection="LLMs" about={"LLM API"} proficiencyLevel="Intermediate" keywords={["Eden AI", "AI API", "LLM API", "chat completion", "OpenAI compatible"]} datePublished="2026-05-06T00:00:00Z" dateModified="2026-05-07T00:00:00Z" />

Use the `/v3/models` endpoint to retrieve all LLM models available through Eden AI, along with their capabilities: PDF support, reasoning, web search, and tool calling.

## Endpoint

```
GET /v3/models
```

## Example

<CodeGroup>
  ```bash cURL theme={null}
  curl https://api.edenai.run/v3/models \
    -H "Authorization: Bearer YOUR_API_KEY"
  ```

  ```python Python theme={null}
  import requests

  response = requests.get(
      "https://api.edenai.run/v3/models",
      headers={"Authorization": "Bearer YOUR_API_KEY"}
  )

  for model in response.json()["data"]:
      print(model["id"])
  ```

  ```javascript JavaScript theme={null}
  const response = await fetch("https://api.edenai.run/v3/models", {
    headers: { "Authorization": "Bearer YOUR_API_KEY" }
  });

  const { data } = await response.json();
  data.forEach(model => console.log(model.id));
  ```
</CodeGroup>

## Response

```json theme={null}
{
  "object": "list",
  "data": [
    {
      "id": "openai/gpt-4o",
      "object": "model",
      "owned_by": "openai",
      "capabilities": {
        "pdf": true,
        "reasoning": false,
        "web_search": true,
        "tool_calling": true
      }
    },
    {
      "id": "anthropic/claude-3-5-sonnet-latest",
      "object": "model",
      "owned_by": "anthropic",
      "capabilities": {
        "pdf": true,
        "reasoning": false,
        "web_search": false,
        "tool_calling": true
      }
    },
    {
      "id": "anthropic/claude-3-7-sonnet-latest",
      "object": "model",
      "owned_by": "anthropic",
      "capabilities": {
        "pdf": true,
        "reasoning": true,
        "web_search": false,
        "tool_calling": true
      }
    }
  ]
}
```

Each model `id` is used directly as the `model` parameter in your requests.

## Capabilities

The `capabilities` object describes what each model supports:

| Field          | Description                                       |
| -------------- | ------------------------------------------------- |
| `pdf`          | Model can process PDF files as input              |
| `reasoning`    | Model supports extended thinking / reasoning mode |
| `web_search`   | Model can perform live web searches               |
| `tool_calling` | Model supports function/tool calling              |

<Tip>
  You can also browse all features and providers visually in the [Eden AI model catalog](https://app.edenai.run/models).
</Tip>

<Note>
  Looking for OCR, image, or audio models? See [List Expert Models](/v3/expert-models/listing-models) for the full catalog of expert model features.
</Note>