Mixtral 8x22B vs Qwen2.5 Coder 32B Instruct
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 Mixtral 8x22B and Qwen2.5 Coder 32B Instruct.
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.
Qwen2.5 Coder 32B Instruct
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...
Technical Specifications
| Specification | Mixtral 8x22B | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Provider | Mistral | Alibaba |
| Context Window | 65,536 tokens | 128,000 tokens |
| Agent Suitability | 87/100 | N/A |
| Time to First Token (TTFT) | 320 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-12-11 | 2024-11-11 |
API Pricing Comparison
Input Price per Million Tokens
Mixtral 8x22B
$0.50
Qwen2.5 Coder 32B Instruct
$0.66
Output Price per Million Tokens
Mixtral 8x22B
$1.00
Qwen2.5 Coder 32B Instruct
$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.
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
Qwen2.5 Coder 32B Instruct Quirks & Gotchas
No developer gotchas reported.