Gemini 3.1 Pro vs Llama 4 Maverick
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 3.1 Pro and Llama 4 Maverick.
Gemini 3.1 Pro
Google's premiere multi-modal model featuring a massive 2 million token context window. Engineered for deep code analysis, video indexing, and long-context reasoning.
Llama 4 Maverick
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Technical Specifications
| Specification | Gemini 3.1 Pro | Llama 4 Maverick |
|---|---|---|
| Provider | Meta | |
| Context Window | 2,000,000 tokens | 1,048,576 tokens |
| Agent Suitability | 93/100 | N/A |
| Time to First Token (TTFT) | 420 ms | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-04-20 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Pro
$2.00
Llama 4 Maverick
$0.15
Output Price per Million Tokens
Gemini 3.1 Pro
$12.00
Llama 4 Maverick
$0.60
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Gemini 3.1 Pro Quirks & Gotchas
- ▸Best model for massive context — 2M token window is class-leading
- ▸Tool calling requires explicit schema definition in Google AI Studio
Llama 4 Maverick Quirks & Gotchas
No developer gotchas reported.