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 Tier | Recommended Model IDs | Use Cases |
---|---|---|
Economy | gpt-4o-mini , gemini-2.0-flash-001 | Prototyping, simple tasks |
Standard | gpt-5-mini , claude-3.5-sonnet | Production apps, chatbots |
Premium | gpt-5 , claude-sonnet-4 | Advanced reasoning, analysis |
Enterprise | claude-opus-4-1-20250805 , gpt-4.1 | Critical 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 Level | Tokens/Month | gpt-5-mini | claude-3.5-sonnet | gemini-2.0-flash-001 |
---|---|---|---|---|
Hobby | 1M | $2.25 | $18 | $0.75 |
Startup | 10M | $22.50 | $180 | $7.50 |
Business | 100M | $225 | $1,800 | $75 |
Enterprise | 1B+ | Custom | Custom | Custom |
Important: Model IDs are case-sensitive. Always use the exact model ID as shown in the tables above.
Next Steps
- Learn about Automatic Fallbacks for high availability
- Check Provider-Specific Features for advanced capabilities
- View Use Cases for different scenarios