Model Comparison

Mistral Large 3 vs Qwen3 VL 32B Thinking

Both models are evenly matched across the benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Mistral Large 3 outperforms in 1 benchmarks (MM-MT-Bench), while Qwen3 VL 32B Thinking is better at 1 benchmark (MMLU-Redux).

Both models are evenly matched across the benchmarks.

Tue May 12 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

642.0B diff

Mistral Large 3 has 642.0B more parameters than Qwen3 VL 32B Thinking, making it 1945.5% larger.

Mistral Large 3

675.0Bparameters

Qwen3 VL 32B Thinking

33.0Bparameters

675.0B

Mistral Large 3

33.0B

Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only Mistral Large 3 specifies input context (128,000 tokens). Only Mistral Large 3 specifies output context (8,192 tokens).

Mistral Large 3

Input128,000 tokens

Output8,192 tokens

Qwen3 VL 32B Thinking

Input- tokens

Output- tokens

Tue May 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Mistral Large 3 and Qwen3 VL 32B Thinking support multimodal inputs.

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

Mistral Large 3

Text

Images

Audio

Video

Qwen3 VL 32B 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 Large 3

Apache 2.0

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Mistral Large 3 was released on 2025-09-01, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 1 month newer than Mistral Large 3.

Mistral Large 3

Sep 1, 2025

8 months ago

Qwen3 VL 32B Thinking

Sep 22, 2025

7 months ago

3w newer

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

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Key Takeaways

Mistral Large 3

View details

Mistral AI

Larger context window (128,000 tokens)

Higher MM-MT-Bench score (84.9% vs 83.0%)

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Higher MMLU-Redux score (91.9% vs 82.0%)

Detailed Comparison

AI Model Comparison Table
Feature	Mistral Large 3	Qwen3 VL 32B Thinking

FAQ

Common questions about Mistral Large 3 vs Qwen3 VL 32B Thinking.

Which is better, Mistral Large 3 or Qwen3 VL 32B Thinking?

Both models are evenly matched across the benchmarks. Mistral Large 3 is made by Mistral AI and Qwen3 VL 32B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Mistral Large 3 compare to Qwen3 VL 32B Thinking in benchmarks?

Mistral Large 3 scores MATH: 90.4%, MM-MT-Bench: 84.9%, MMLU-Redux: 82.0%, TriviaQA: 74.9%, MMMLU: 74.2%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for Mistral Large 3 and Qwen3 VL 32B Thinking?

Mistral Large 3 supports 128K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes Mistral Large 3 and Qwen3 VL 32B Thinking?

Mistral Large 3 is developed by Mistral AI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.