Model Comparison

Mistral Small 3.2 24B Instruct vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks.

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Mistral Small 3.2 24B Instruct outperforms in 1 benchmarks (AI2D), while Qwen3 VL 4B Thinking is better at 3 benchmarks (GPQA, MMLU, MMLU-Pro).

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks.

Sun Apr 19 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Sun Apr 19 2026 • llm-stats.com
Mistral AI
Mistral Small 3.2 24B Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input tokens$0.10
Output tokens$1.00
Best providerDeepinfra
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Model Size

Parameter count comparison

19.6B diff

Mistral Small 3.2 24B Instruct has 19.6B more parameters than Qwen3 VL 4B Thinking, making it 490.0% larger.

Mistral AI
Mistral Small 3.2 24B Instruct
23.6Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
23.6B
Mistral Small 3.2 24B Instruct
4.0B
Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 4B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 4B Thinking specifies output context (262,144 tokens).

Mistral AI
Mistral Small 3.2 24B Instruct
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Sun Apr 19 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Mistral Small 3.2 24B Instruct and Qwen3 VL 4B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Mistral Small 3.2 24B Instruct

Text
Images
Audio
Video

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

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

Mistral Small 3.2 24B Instruct

Apache 2.0

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Mistral Small 3.2 24B Instruct was released on 2025-06-20, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 3 months newer than Mistral Small 3.2 24B Instruct.

Mistral Small 3.2 24B Instruct

Jun 20, 2025

10 months ago

Qwen3 VL 4B Thinking

Sep 22, 2025

6 months ago

3mo newer

Knowledge Cutoff

When training data ends

Mistral Small 3.2 24B Instruct has a documented knowledge cutoff of 2023-10-01, while Qwen3 VL 4B Thinking's cutoff date is not specified.

We can confirm Mistral Small 3.2 24B Instruct's training data extends to 2023-10-01, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.

Mistral Small 3.2 24B Instruct

Oct 2023

Qwen3 VL 4B Thinking

Outputs Comparison

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Key Takeaways

Higher AI2D score (92.9% vs 84.9%)
Alibaba Cloud / Qwen Team

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Higher GPQA score (64.1% vs 46.1%)
Higher MMLU score (81.5% vs 80.5%)
Higher MMLU-Pro score (73.6% vs 69.1%)

Detailed Comparison

FAQ

Common questions about Mistral Small 3.2 24B Instruct vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks. Mistral Small 3.2 24B Instruct is made by Mistral AI and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Mistral Small 3.2 24B Instruct scores DocVQA: 94.9%, AI2D: 92.9%, HumanEval Plus: 92.9%, ChartQA: 87.4%, IF: 84.8%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.
Mistral Small 3.2 24B Instruct supports an unknown number of tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Mistral Small 3.2 24B Instruct is developed by Mistral AI and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.