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MegaLLM

Models Catalog

Complete list of available AI models through MegaLLM API

Models Catalog

Access cutting-edge AI models from leading providers through a single, unified API. All models are accessible using their model ID in your API calls.

Live Models Data

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Model Selection Guide

By Use Case

🚀 Fast Responses

gpt-5-mini, gpt-4o-mini, gemini-2.0-flash-001, gpt-3.5-turbo

🧠 Complex Reasoning

gpt-5, claude-opus-4-1-20250805, gemini-2.5-pro

💰 Cost-Effective

gpt-4o-mini, gemini-2.0-flash-001, xai/grok-code-fast-1

📚 Large Context

gpt-4.1 (1M+), gemini-2.5-pro (1M+), xai/grok-code-fast-1 (256K)

🖼️ Vision Tasks

gpt-5, gpt-4o, claude-sonnet-4, gemini models

💻 Code Generation

xai/grok-code-fast-1, gpt-5, claude-3.7-sonnet

By Budget

Budget TierRecommended Model IDsUse Cases
Economygpt-4o-mini, gemini-2.0-flash-001Prototyping, simple tasks
Standardgpt-5-mini, claude-3.5-sonnetProduction apps, chatbots
Premiumgpt-5, claude-sonnet-4Advanced reasoning, analysis
Enterpriseclaude-opus-4-1-20250805, gpt-4.1Critical applications, research

Using Models in Code

Always use the model ID when making API calls:

from openai import OpenAI

client = OpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-api-key"
)

# Use model ID, not display name
response = client.chat.completions.create(
    model="gpt-5",  # Model ID
    messages=[{"role": "user", "content": "Hello!"}]
)

# Switch to Claude using model ID
response = client.chat.completions.create(
    model="claude-opus-4-1-20250805",  # Model ID
    messages=[{"role": "user", "content": "Hello!"}]
)

# Try Gemini using model ID
response = client.chat.completions.create(
    model="gemini-2.5-pro",  # Model ID
    messages=[{"role": "user", "content": "Hello!"}]
)
// Always use model IDs
const models = ['gpt-5', 'claude-opus-4-1-20250805', 'gemini-2.5-pro'];

for (const modelId of models) {
  const response = await fetch("https://ai.megallm.io/v1/chat/completions", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${API_KEY}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: modelId,  // Using model ID
      messages: [{ role: "user", content: "Hello!" }]
    })
  });

  console.log(`${modelId} response:`, await response.json());
}
# Test multiple models using their IDs
for model in "gpt-5" "claude-opus-4-1-20250805" "gemini-2.5-pro"; do
  echo "Testing $model..."
  curl https://ai.megallm.io/v1/chat/completions \
    -H "Authorization: Bearer $API_KEY" \
    -H "Content-Type: application/json" \
    -d "{
      \"model\": \"$model\",
      \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]
    }"
done

Automatic Fallback

Configure automatic fallback using model IDs:

response = client.chat.completions.create(
    model="gpt-5",
    messages=messages,
    fallback_models=["claude-opus-4-1-20250805", "gemini-2.5-pro"],
    fallback_on_rate_limit=True,
    fallback_on_error=True
)

Pricing Calculator

Estimate your costs across different models:

Usage LevelTokens/Monthgpt-5-miniclaude-3.5-sonnetgemini-2.0-flash-001
Hobby1M$2.25$18$0.75
Startup10M$22.50$180$7.50
Business100M$225$1,800$75
Enterprise1B+CustomCustomCustom

Important: Model IDs are case-sensitive. Always use the exact model ID as shown in the tables above.

Next Steps