Gemini 3.1 Pro vs Mixtral 8x22B
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 Mixtral 8x22B.
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.
Mixtral 8x22B
Mixtral 8x22B is Mistral AI's open-weight Mixture-of-Experts model, activating only 39B of its 141B total parameters per token to deliver frontier-level performance at inference costs comparable to a much smaller dense model. Released under the Apache 2.0 license, Mixtral 8x22B is one of the most capable fully open-weight models available, with strong multilingual performance, robust coding ability, and efficient fine-tuning via LoRA. It is widely deployed across self-hosted infrastructure, including Ollama, vLLM, and Hugging Face TGI.
Technical Specifications
| Specification | Gemini 3.1 Pro | Mixtral 8x22B |
|---|---|---|
| Provider | Mistral | |
| Context Window | 2,000,000 tokens | 65,536 tokens |
| Agent Suitability | 93/100 | 87/100 |
| Time to First Token (TTFT) | 420 ms | 320 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-20 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Pro
$2.00
Mixtral 8x22B
$0.50
Output Price per Million Tokens
Gemini 3.1 Pro
$12.00
Mixtral 8x22B
$1.00
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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
Mixtral 8x22B Quirks & Gotchas
- ▸MoE architecture — efficient inference for its capability tier
- ▸Requires ~90GB VRAM at FP16 — 4-bit quantization recommended for single-GPU deployment