Mistral Large 3 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 Mistral Large 3 and Qwen 2.5 72B.
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.
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 | Mistral Large 3 | Qwen 2.5 72B |
|---|---|---|
| Provider | Mistral | Alibaba |
| Context Window | 262,144 tokens | 131,072 tokens |
| Agent Suitability | 91/100 | 88/100 |
| Time to First Token (TTFT) | 250 ms | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-24 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Mistral Large 3
$0.50
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Mistral Large 3
$1.50
Qwen 2.5 72B
$0.80
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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
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