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MiniStral 3 (14B Instruct 2512) vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

MiniStral 3 (14B Instruct 2512) outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 1 benchmark (MM-MT-Bench). Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Based on current benchmark, pricing, and model metadata for 2026.

Choose MiniStral 3 (14B Instruct 2512)

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

Choose Qwen3 VL 32B Thinking

  • you want the strongest raw capability — it leads on 1 of 1 shared benchmarks

At a glance

The differences that matter most.

Benchmark wins
0 of 1
1 of 1
Input price
— / M
— / M
Output price
— / M
— / M
Context window
Released
Dec 2025
Sep 2025
License
Apache 2.0
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

MiniStral 3 (14B Instruct 2512) outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 1 benchmark (MM-MT-Bench).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Tue Aug 25 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

19.0B diff

Qwen3 VL 32B Thinking has 19.0B more parameters than MiniStral 3 (14B Instruct 2512), making it 135.7% larger.

Mistral AI
MiniStral 3 (14B Instruct 2512)
14.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
14.0B
MiniStral 3 (14B Instruct 2512)
33.0B
Qwen3 VL 32B Thinking

Input Capabilities

Supported data types and modalities

Both MiniStral 3 (14B Instruct 2512) and Qwen3 VL 32B Thinking support multimodal inputs.

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

MiniStral 3 (14B Instruct 2512)

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.

MiniStral 3 (14B Instruct 2512)

Apache 2.0

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

MiniStral 3 (14B Instruct 2512) was released on 2025-12-04, while Qwen3 VL 32B Thinking was released on 2025-09-22.

MiniStral 3 (14B Instruct 2512) is 2 months newer than Qwen3 VL 32B Thinking.

MiniStral 3 (14B Instruct 2512)

Dec 4, 2025

8 months ago

2mo newer
Qwen3 VL 32B Thinking

Sep 22, 2025

11 months 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

Judge for yourself.

Run your own prompts against MiniStral 3 (14B Instruct 2512) and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

MiniStral 3 (14B Instruct 2512)
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

Common questions about MiniStral 3 (14B Instruct 2512) vs Qwen3 VL 32B Thinking.

Which is better, MiniStral 3 (14B Instruct 2512) or Qwen3 VL 32B Thinking?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. MiniStral 3 (14B Instruct 2512) 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 MiniStral 3 (14B Instruct 2512) compare to Qwen3 VL 32B Thinking in benchmarks?

MiniStral 3 (14B Instruct 2512) scores MATH: 90.4%, Wild Bench: 68.5%, Arena Hard: 55.1%, MM-MT-Bench: 8.5%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

Who makes MiniStral 3 (14B Instruct 2512) and Qwen3 VL 32B Thinking?

MiniStral 3 (14B Instruct 2512) is developed by Mistral AI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.