Mixtral 8x22B Instruct vs Qwen 2.5 72B
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 Instruct and Qwen 2.5 72B.
Mixtral 8x22B Instruct
Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding,...
Qwen 2.5 72B
Qwen 2.5 72B is Alibaba Cloud's flagship open-weight large language model from the Qwen 2.5 generation, delivering GPT-4-class performance across general reasoning, coding, mathematics, and multilingual tasks with strong Chinese-language superiority. It supports a 131,072-token context window and is available under a permissive Apache 2.0 license for both research and commercial use, making it one of the most popular open-weight alternatives to Llama for bilingual applications.
Technical Specifications
| Specification | Mixtral 8x22B Instruct | Qwen 2.5 72B |
|---|---|---|
| Provider | Mistral | Alibaba |
| Context Window | 65,536 tokens | 131,072 tokens |
| Agent Suitability | N/A | 88/100 |
| Time to First Token (TTFT) | N/A | 280 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-04-17 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Mixtral 8x22B Instruct
$2.00
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Mixtral 8x22B Instruct
$6.00
Qwen 2.5 72B
$0.80
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
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 Instruct Quirks & Gotchas
No developer gotchas reported.
Qwen 2.5 72B Quirks & Gotchas
- ▸Strong bilingual (ZH/EN) performance — best open model for Chinese-language tasks
- ▸Self-hostable via vLLM or Ollama with 4-bit quantization