> ## 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. # LLMs vs Expert Models > Eden AI V3 provides two main endpoints. Both share the same base URL (https://api. 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; }; Eden AI V3 provides two main endpoints. Both share the same base URL (`https://api.edenai.run/v3`) and authentication (`Authorization: Bearer YOUR_API_KEY`), but they serve different purposes. ## At a Glance | | LLM Endpoint | Universal AI Endpoint | | ------------------------- | ------------------------------------------- | -------------------------------------------------------- | | **URL** | `POST /v3/chat/completions` | `POST /v3/universal-ai` | | **Model Format** | `provider/model` | `feature/subfeature/provider[/model]` | | **Use Cases** | Chat, text generation, vision, tool calling | OCR, text analysis, image processing, translation, audio | | **Response Format** | OpenAI-compatible (choices, usage) | Unified (status, cost, output) | | **Streaming** | Yes (SSE) | No | | **OpenAI SDK Compatible** | Yes | No | ## LLM Endpoint The LLM endpoint follows the **OpenAI chat completions format**, making it a drop-in replacement for any OpenAI-compatible integration. **Best for:** * Conversational AI and chatbots * Text generation and completion * Vision and multimodal analysis (analyzing images with LLMs) * Tool/function calling * Any workflow that uses the OpenAI SDK **Model format:** `provider/model` Examples: `openai/gpt-4`, `anthropic/claude-sonnet-4-5`, `google/gemini-2.5-flash` ```python Python theme={null} import requests url = "https://api.edenai.run/v3/chat/completions" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } payload = { "model": "openai/gpt-4", "messages": [ {"role": "user", "content": "Summarize the benefits of cloud computing."} ] } response = requests.post(url, headers=headers, json=payload) result = response.json() print(result["choices"][0]["message"]["content"]) ``` ```bash cURL theme={null} curl -X POST https://api.edenai.run/v3/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "openai/gpt-4", "messages": [ {"role": "user", "content": "Summarize the benefits of cloud computing."} ] }' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.edenai.run/v3/chat/completions', { method: 'POST', headers: { 'Authorization': 'Bearer YOUR_API_KEY', 'Content-Type': 'application/json' }, body: JSON.stringify({ model: 'openai/gpt-4', messages: [{role: 'user', content: 'Summarize the benefits of cloud computing.'}] }) }); const result = await response.json(); console.log(result.choices[0].message.content); ``` ## Universal AI Endpoint The Universal AI endpoint handles **specialized AI tasks** through a single URL. You specify the task, provider, and optionally the model in the model string. **Best for:** * Text analysis (moderation, AI detection, NER, sentiment) * OCR and document parsing (invoices, IDs, resumes) * Image processing (generation, object detection, face detection) * Translation * Audio (speech-to-text, text-to-speech) **Model format:** `feature/subfeature/provider[/model]` Examples: `text/moderation/openai`, `ocr/financial_parser/google`, `image/generation/openai/dall-e-3` ```python Python theme={null} import requests url = "https://api.edenai.run/v3/universal-ai" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } payload = { "model": "text/moderation/openai", "input": { "text": "Content to analyze for moderation" } } response = requests.post(url, headers=headers, json=payload) result = response.json() print(f"Status: {result['status']}, Cost: ${result['cost']}") ``` ```bash cURL theme={null} curl -X POST https://api.edenai.run/v3/universal-ai \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "text/moderation/openai", "input": { "text": "Content to analyze for moderation" } }' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.edenai.run/v3/universal-ai', { method: 'POST', headers: { 'Authorization': 'Bearer YOUR_API_KEY', 'Content-Type': 'application/json' }, body: JSON.stringify({ model: 'text/moderation/openai', input: { text: 'Content to analyze for moderation' } }) }); const result = await response.json(); console.log(`Status: ${result.status}, Cost: $${result.cost}`); ``` ## When to Use Which * You need conversational AI or text generation * You want OpenAI SDK compatibility * You need streaming responses * You are doing vision analysis with an LLM * You need tool/function calling * You need OCR or document parsing * You need text moderation or analysis * You want image generation or detection * You need translation * You need speech-to-text or text-to-speech Not sure which endpoint handles your task? Use the [listing models endpoint](/v3/llms/listing-models) to discover all available features and providers. ## Response Format Comparison **LLM response** (OpenAI-compatible): ```json theme={null} { "id": "chatcmpl-abc123", "object": "chat.completion", "model": "openai/gpt-4", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Cloud computing offers..." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 12, "completion_tokens": 85, "total_tokens": 97 } } ``` **Universal AI response**: ```json theme={null} { "status": "success", "cost": 0.0001, "provider": "openai", "feature": "text", "subfeature": "moderation", "output": { // Feature-specific results } } ``` ## Next Steps Full guide to the LLM endpoint. Full guide to the Expert Models endpoint.