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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

Loading models from MegaLLM API...

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