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Mistral Small 4 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 Small 4 and Qwen 2.5 72B.

Mistral

Mistral Small 4

Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...

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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 Small 4Qwen 2.5 72B
ProviderMistralAlibaba
Context Window262,144 tokens131,072 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A280 ms
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-03-162025-09-19

API Pricing Comparison

Input Price per Million Tokens

Mistral Small 4

$0.15

Qwen 2.5 72B

$0.40

Output Price per Million Tokens

Mistral Small 4

$0.60

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 Small 4 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