LLMs vs Expert Models

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

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"])

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."}
    ]
  }'

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

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']}")

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"
    }
  }'

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

Use the LLM Endpoint when...

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

Use Universal AI when...

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 to discover all available features and providers.

Response Format Comparison

LLM response (OpenAI-compatible):

{
  "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:

{
  "status": "success",
  "cost": 0.0001,
  "provider": "openai",
  "feature": "text",
  "subfeature": "moderation",
  "output": {
    // Feature-specific results
  }
}

V3 Documentation

Quick Start

Overview

LLMs

Expert Models

General

Organization

Data Governance

Integrations

At a Glance

LLM Endpoint

Universal AI Endpoint

When to Use Which

Use the LLM Endpoint when...

Use Universal AI when...

Response Format Comparison

Next Steps

Chat Completions

First Expert Model Call

​At a Glance

​LLM Endpoint

​Universal AI Endpoint

​When to Use Which

Use the LLM Endpoint when...

Use Universal AI when...

​Response Format Comparison

​Next Steps

Chat Completions

First Expert Model Call

At a Glance

LLM Endpoint

Universal AI Endpoint

When to Use Which

Response Format Comparison

Next Steps