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Ministral 3 (8B Base 2512) vs Qwen3 VL 4B Instruct

Qwen3 VL 4B Instruct significantly outperforms across most benchmarks.

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

Which is better?

Ministral 3 (8B Base 2512) outperforms in 0 benchmarks, while Qwen3 VL 4B Instruct is better at 2 benchmarks (MMLU, MMLU-Redux). Qwen3 VL 4B Instruct significantly outperforms across most benchmarks.

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

Choose Ministral 3 (8B Base 2512)

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

Choose Qwen3 VL 4B Instruct

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks

At a glance

The differences that matter most.

Benchmark wins
0 of 2
2 of 2
Input price
— / M
$0.10 / M
Output price
— / M
$0.60 / M
Context window
262,144
Released
Dec 2025
Sep 2025
License
Apache 2.0
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Ministral 3 (8B Base 2512) outperforms in 0 benchmarks, while Qwen3 VL 4B Instruct is better at 2 benchmarks (MMLU, MMLU-Redux).

Qwen3 VL 4B Instruct significantly outperforms across most benchmarks.

Mon Aug 24 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

4.0B diff

Ministral 3 (8B Base 2512) has 4.0B more parameters than Qwen3 VL 4B Instruct, making it 100.0% larger.

Mistral AI
Ministral 3 (8B Base 2512)
8.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Instruct
4.0Bparameters
8.0B
Ministral 3 (8B Base 2512)
4.0B
Qwen3 VL 4B Instruct

Context Window

Maximum input and output token capacity

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

Mistral AI
Ministral 3 (8B Base 2512)
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Instruct
Input262,144 tokens
Output262,144 tokens
Mon Aug 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Ministral 3 (8B Base 2512) and Qwen3 VL 4B Instruct support multimodal inputs.

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

Ministral 3 (8B Base 2512)

Text
Images
Audio
Video

Qwen3 VL 4B Instruct

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 (8B Base 2512)

Apache 2.0

Open weights

Qwen3 VL 4B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Ministral 3 (8B Base 2512) was released on 2025-12-04, while Qwen3 VL 4B Instruct was released on 2025-09-22.

Ministral 3 (8B Base 2512) is 2 months newer than Qwen3 VL 4B Instruct.

Ministral 3 (8B Base 2512)

Dec 4, 2025

8 months ago

2mo newer
Qwen3 VL 4B Instruct

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 (8B Base 2512) and Qwen3 VL 4B Instruct side-by-side, then vote on the output you prefer.

Ministral 3 (8B Base 2512)
✓ Preferred
Qwen3 VL 4B Instruct
Open in Playground

FAQ

Common questions about Ministral 3 (8B Base 2512) vs Qwen3 VL 4B Instruct.

Which is better, Ministral 3 (8B Base 2512) or Qwen3 VL 4B Instruct?

Qwen3 VL 4B Instruct significantly outperforms across most benchmarks. Ministral 3 (8B Base 2512) is made by Mistral AI and Qwen3 VL 4B Instruct 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 (8B Base 2512) compare to Qwen3 VL 4B Instruct in benchmarks?

Ministral 3 (8B Base 2512) scores MMLU-Redux: 79.3%, MMLU: 76.1%, Multilingual MMLU: 70.6%, TriviaQA: 68.1%, MATH (CoT): 62.6%. Qwen3 VL 4B Instruct scores DocVQAtest: 95.3%, ScreenSpot: 94.0%, OCRBench: 88.1%, MMBench-V1.1: 85.1%, AI2D: 84.1%.

What are the context window sizes for Ministral 3 (8B Base 2512) and Qwen3 VL 4B Instruct?

Ministral 3 (8B Base 2512) supports an unknown number of tokens and Qwen3 VL 4B Instruct supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes Ministral 3 (8B Base 2512) and Qwen3 VL 4B Instruct?

Ministral 3 (8B Base 2512) is developed by Mistral AI and Qwen3 VL 4B Instruct is developed by Alibaba Cloud / Qwen Team.