> ## Documentation Index > Fetch the complete documentation index at: https://edenai-feat-automate-documentation-tests.mintlify.site/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: input modalities, reasoning, web search, and function 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; }; Use the `/v3/models` endpoint to retrieve all LLM models available through Eden AI, along with their capabilities: input modalities, reasoning, web search, and function calling. ## Endpoint ``` GET /v3/models ``` The endpoint is public: no API key is required. ## Example ```bash cURL theme={null} curl https://api.edenai.run/v3/models ``` ```python Python theme={null} import requests response = requests.get("https://api.edenai.run/v3/models") for model in response.json()["data"]: print(model["id"]) ``` ```javascript JavaScript theme={null} const response = await fetch("https://api.edenai.run/v3/models"); const { data } = await response.json(); data.forEach(model => console.log(model.id)); ``` ## Response Each entry includes the model's identity, context window, capabilities, pricing, and available regions (list truncated for readability): ```json theme={null} { "object": "list", "data": [ { "id": "google/gemini-flash-latest", "object": "model", "created": 1784646733, "owned_by": "google", "model_name": "gemini-3.6-flash", "context_length": 1048576, "description": "Gemini 3.6 Flash is a high-efficiency model from Google...", "capabilities": { "input_modalities": ["text", "image", "video", "file", "audio"], "output_modalities": ["text"], "supports_reasoning": true, "supports_web_search": true, "supports_tool_choice": true, "supports_computer_use": false, "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, "supports_function_calling": true, "supports_native_streaming": true, "supports_assistant_prefill": false, "supports_embedding_image_input": false, "supports_parallel_function_calling": true }, "pricing": { "input_cost_per_token": 1.5e-06, "output_cost_per_token": 7.5e-06, "cache_read_input_token_cost": 1.5e-07, "cache_creation_input_token_cost": 8.33e-08 }, "regions": [ { "code": "eu", "name": "Europe" } ], "alias_of": "google/gemini-3.6-flash" } ] } ``` Each model `id` is used directly as the `model` parameter in your requests. ## Model aliases Some models are available under a **stable alias**, a version-agnostic name that always points to the current release, alongside versioned or dated snapshot IDs: * **Stable alias:** `google/gemini-flash-latest`, `anthropic/claude-sonnet-4-6`, `deepseek/deepseek-chat` * **Versioned snapshot:** `anthropic/claude-opus-4-5-20251101` Use the **stable alias** when you want your integration to keep working as providers ship new versions. **Pin a versioned snapshot** when you need a fixed, reproducible model. Either form works as the `model` parameter. Alias entries report the concrete model they currently resolve to in the `alias_of` field, as in the response example above. The catalog lists both forms when a provider exposes them, so you may see a stable alias (e.g. `anthropic/claude-opus-4-5`) and one or more dated snapshots (e.g. `anthropic/claude-opus-4-5-20251101`) side by side. Use the stable alias to always get the latest version, or a dated snapshot to pin to a specific release. ## Capabilities The `capabilities` object describes what each model supports: | Field | Description | | ------------------------------------ | ----------------------------------------------------------------------------------- | | `input_modalities` | Input types the model accepts: `text`, `image`, `audio`, `video`, `file` | | `output_modalities` | Output types the model produces | | `supports_reasoning` | Model supports extended thinking / reasoning mode | | `supports_web_search` | Model can perform live web searches via [`web_search_options`](/v3/llms/web-search) | | `supports_function_calling` | Model supports function/tool calling | | `supports_tool_choice` | Model supports the `tool_choice` parameter | | `supports_prompt_caching` | Model supports prompt caching | | `supports_response_schema` | Model supports structured output with a response schema | | `supports_system_messages` | Model accepts system messages | | `supports_computer_use` | Model supports computer-use tooling | | `supports_parallel_function_calling` | Model can call multiple tools in one turn | | `supports_assistant_prefill` | Model accepts a pre-filled assistant message | | `supports_native_streaming` | Model supports native streaming responses | | `supports_embedding_image_input` | Model accepts image input for embeddings | Some models expose extra provider-specific keys (e.g. `supports_pdf_input`, `supports_audio_input`), and a few return `"capabilities": null`. Treat a missing key or a `null` object as "not supported". You can also browse all features and providers visually in the [Eden AI model catalog](https://app.edenai.run/models). Need EU data residency? Hit `https://api.eu.edenai.run/v3/models` instead and the list is automatically filtered to EU-eligible models. See [EU Endpoint](/v3/data-governance/eu-endpoint). Looking for OCR, image, or audio models? See [List Expert Models](/v3/expert-models/listing-models) for the full catalog of expert model features.