Mistral Large 3 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 Mistral Large 3 and Mixtral 8x22B.
Mistral Large 3
Mistral's flagship commercial model, boasting multilingual support and advanced coding and math skills. Designed for complex reasoning and enterprise tasks that require high compliance.
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 | Mistral Large 3 | Mixtral 8x22B |
|---|---|---|
| Provider | Mistral | Mistral |
| Context Window | 262,144 tokens | 65,536 tokens |
| Agent Suitability | 91/100 | 87/100 |
| Time to First Token (TTFT) | 250 ms | 320 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-24 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
Mistral Large 3
$0.50
Mixtral 8x22B
$0.50
Output Price per Million Tokens
Mistral Large 3
$1.50
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.
Mistral Large 3 Quirks & Gotchas
- ▸Top-tier structured JSON output — best multilingual function calling
- ▸Le Chat offers free consumer tier with same underlying model
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