Model Comparison

Ministral 3 (14B Base 2512) vs Qwen3-235B-A22B-Thinking-2507

Qwen3-235B-A22B-Thinking-2507 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Ministral 3 (14B Base 2512) outperforms in 0 benchmarks, while Qwen3-235B-A22B-Thinking-2507 is better at 1 benchmark (MMLU-Redux).

Qwen3-235B-A22B-Thinking-2507 significantly outperforms across most benchmarks.

Thu Apr 16 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

Thu Apr 16 2026 • llm-stats.com

Ministral 3 (14B Base 2512)

Input tokens$0.00

Output tokens$0.00

Best providerUnknown Organization

Qwen3-235B-A22B-Thinking-2507

Input tokens$0.30

Output tokens$3.00

Best providerFireworks

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

221.0B diff

Qwen3-235B-A22B-Thinking-2507 has 221.0B more parameters than Ministral 3 (14B Base 2512), making it 1578.6% larger.

Ministral 3 (14B Base 2512)

14.0Bparameters

Qwen3-235B-A22B-Thinking-2507

235.0Bparameters

14.0B

Ministral 3 (14B Base 2512)

235.0B

Qwen3-235B-A22B-Thinking-2507

Context Window

Maximum input and output token capacity

Only Qwen3-235B-A22B-Thinking-2507 specifies input context (262,144 tokens). Only Qwen3-235B-A22B-Thinking-2507 specifies output context (131,072 tokens).

Ministral 3 (14B Base 2512)

Input- tokens

Output- tokens

Qwen3-235B-A22B-Thinking-2507

Input262,144 tokens

Output131,072 tokens

Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Ministral 3 (14B Base 2512) supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.

Ministral 3 (14B Base 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

Ministral 3 (14B Base 2512)

Text

Images

Audio

Video

Qwen3-235B-A22B-Thinking-2507

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 Base 2512)

Apache 2.0

Open weights

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

Ministral 3 (14B Base 2512) was released on 2025-12-04, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

Ministral 3 (14B Base 2512) is 4 months newer than Qwen3-235B-A22B-Thinking-2507.

Ministral 3 (14B Base 2512)

Dec 4, 2025

4 months ago

4mo newer

Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

8 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→

Key Takeaways

Ministral 3 (14B Base 2512)

View details

Mistral AI

Supports multimodal inputs

Qwen3-235B-A22B-Thinking-2507

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Higher MMLU-Redux score (93.8% vs 82.0%)

Detailed Comparison

AI Model Comparison Table
Feature	Ministral 3 (14B Base 2512)	Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about Ministral 3 (14B Base 2512) vs Qwen3-235B-A22B-Thinking-2507

Qwen3-235B-A22B-Thinking-2507 significantly outperforms across most benchmarks. Ministral 3 (14B Base 2512) is made by Mistral AI and Qwen3-235B-A22B-Thinking-2507 is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

Ministral 3 (14B Base 2512) scores MMLU-Redux: 82.0%, MMLU: 79.4%, TriviaQA: 74.9%, Multilingual MMLU: 74.2%, MATH (CoT): 67.6%. Qwen3-235B-A22B-Thinking-2507 scores MMLU-Redux: 93.8%, AIME 2025: 92.3%, WritingBench: 88.3%, IFEval: 87.8%, Creative Writing v3: 86.1%.

Ministral 3 (14B Base 2512) supports an unknown number of tokens and Qwen3-235B-A22B-Thinking-2507 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Key differences include multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Ministral 3 (14B Base 2512) is developed by Mistral AI and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.

Ministral 3 (14B Base 2512) vs Qwen3-235B-A22B-Thinking-2507

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

Input Capabilities

Ministral 3 (14B Base 2512)

Qwen3-235B-A22B-Thinking-2507

License

Release Timeline

Knowledge Cutoff

Outputs Comparison

Key Takeaways

Ministral 3 (14B Base 2512)

Qwen3-235B-A22B-Thinking-2507

Detailed Comparison

FAQ

Which is better, Ministral 3 (14B Base 2512) or Qwen3-235B-A22B-Thinking-2507?

How does Ministral 3 (14B Base 2512) compare to Qwen3-235B-A22B-Thinking-2507 in benchmarks?

What are the context window sizes for Ministral 3 (14B Base 2512) and Qwen3-235B-A22B-Thinking-2507?

What are the main differences between Ministral 3 (14B Base 2512) and Qwen3-235B-A22B-Thinking-2507?

Who makes Ministral 3 (14B Base 2512) and Qwen3-235B-A22B-Thinking-2507?