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

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.

Sun Mar 15 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 Mar 15 2026 • llm-stats.com
Mistral AI
Ministral 3 (14B Base 2512)
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input tokens$0.30
Output tokens$3.00
Best providerFireworks
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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.

Mistral AI
Ministral 3 (14B Base 2512)
14.0Bparameters
Alibaba Cloud / Qwen Team
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).

Mistral AI
Ministral 3 (14B Base 2512)
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input262,144 tokens
Output131,072 tokens
Sun Mar 15 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

3 months ago

4mo newer
Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

7 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

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Key Takeaways

Supports multimodal inputs
Larger context window (262,144 tokens)
Higher MMLU-Redux score (93.8% vs 82.0%)

Detailed Comparison