Gemini 3.1 Pro vs MiniMax M2
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 MiniMax M2.
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.
MiniMax M2
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...
Technical Specifications
| Specification | Gemini 3.1 Pro | MiniMax M2 |
|---|---|---|
| Provider | MiniMax | |
| Context Window | 2,000,000 tokens | 204,800 tokens |
| Agent Suitability | 93/100 | N/A |
| Time to First Token (TTFT) | 420 ms | N/A |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-20 | 2025-10-23 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Pro
$2.00
MiniMax M2
$0.26
Output Price per Million Tokens
Gemini 3.1 Pro
$12.00
MiniMax M2
$1.02
Want to test both models live?
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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
MiniMax M2 Quirks & Gotchas
No developer gotchas reported.