Codestral 2508 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 Codestral 2508 and Mixtral 8x22B.
Codestral 2508
Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/news/codestral-25-08)
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 | Codestral 2508 | Mixtral 8x22B |
|---|---|---|
| Provider | Mistral | Mistral |
| Context Window | 256,000 tokens | 65,536 tokens |
| Agent Suitability | N/A | 87/100 |
| Time to First Token (TTFT) | N/A | 320 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-08-01 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
Codestral 2508
$0.30
Mixtral 8x22B
$0.50
Output Price per Million Tokens
Codestral 2508
$0.90
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.
Codestral 2508 Quirks & Gotchas
No developer gotchas reported.
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