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Mistral Large 2407 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 2407 and Qwen 2.5 72B.

Mistral

Mistral Large 2407

This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....

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Alibaba

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.

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Technical Specifications

SpecificationMistral Large 2407Qwen 2.5 72B
ProviderMistralAlibaba
Context Window131,072 tokens131,072 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A280 ms
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-11-192025-09-19

API Pricing Comparison

Input Price per Million Tokens

Mistral Large 2407

$2.00

Qwen 2.5 72B

$0.40

Output Price per Million Tokens

Mistral Large 2407

$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.

Mistral Large 2407 Quirks & Gotchas

No developer gotchas reported.

Qwen 2.5 72B Quirks & Gotchas

  • โ–ธStrong bilingual (ZH/EN) performance โ€” best open model for Chinese-language tasks
  • โ–ธSelf-hostable via vLLM or Ollama with 4-bit quantization