Model Comparison

Devstral Small 1.1 vs Qwen2 7B InstructWhich is better in 2026?

Comparing Devstral Small 1.1 and Qwen2 7B Instruct across benchmarks, pricing, and capabilities.

Verdict: Devstral Small 1.1 vs Qwen2 7B Instruct — which is better?

Devstral Small 1.1 (by Mistral AI) and Qwen2 7B Instruct (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose Devstral Small 1.1 if…

  • you want the most recent training data — it shipped Jul 2025

Choose Qwen2 7B Instruct if…

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Devstral Small 1.1 and Qwen2 7B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

16.4B diff

Devstral Small 1.1 has 16.4B more parameters than Qwen2 7B Instruct, making it 215.0% larger.

Mistral AI
Devstral Small 1.1
24.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2 7B Instruct
7.6Bparameters
24.0B
Devstral Small 1.1
7.6B
Qwen2 7B Instruct

Context Window

Maximum input and output token capacity

Only Devstral Small 1.1 specifies input context (128,000 tokens). Only Devstral Small 1.1 specifies output context (128,000 tokens).

Mistral AI
Devstral Small 1.1
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen2 7B Instruct
Input- tokens
Output- tokens
Sun Jul 26 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Devstral Small 1.1

Apache 2.0

Open weights

Qwen2 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Devstral Small 1.1 was released on 2025-07-11, while Qwen2 7B Instruct was released on 2024-07-23.

Devstral Small 1.1 is 12 months newer than Qwen2 7B Instruct.

Devstral Small 1.1

Jul 11, 2025

1.0 years ago

11mo newer
Qwen2 7B Instruct

Jul 23, 2024

2.0 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Mistral AI
Larger context window (128,000 tokens)
Alibaba Cloud / Qwen Team

Qwen2 7B Instruct

View details

Alibaba Cloud / Qwen Team

No standout differentiators in the data we have for this pair.

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Devstral Small 1.1 and Qwen2 7B Instruct side-by-side, then vote on the output you prefer.

Devstral Small 1.1
✓ Preferred
Qwen2 7B Instruct
Open in Playground
AI Model Comparison Table
Feature
Mistral AI
Devstral Small 1.1
Alibaba Cloud / Qwen Team
Qwen2 7B Instruct

FAQ

Common questions about Devstral Small 1.1 vs Qwen2 7B Instruct.

Which is better, Devstral Small 1.1 or Qwen2 7B Instruct?

Devstral Small 1.1 (Mistral AI) and Qwen2 7B Instruct (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Devstral Small 1.1 compare to Qwen2 7B Instruct in benchmarks?

Devstral Small 1.1 scores SWE-Bench Verified: 53.6%. Qwen2 7B Instruct scores MT-Bench: 84.1%, GSM8k: 82.3%, HumanEval: 79.9%, C-Eval: 77.2%, AlignBench: 72.1%.

What are the context window sizes for Devstral Small 1.1 and Qwen2 7B Instruct?

Devstral Small 1.1 supports 128K tokens and Qwen2 7B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes Devstral Small 1.1 and Qwen2 7B Instruct?

Devstral Small 1.1 is developed by Mistral AI and Qwen2 7B Instruct is developed by Alibaba Cloud / Qwen Team.

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