DeepSeek R1 Distill Qwen 32B vs Ministral 3 (14B Reasoning 2512) Comparison
Comparing DeepSeek R1 Distill Qwen 32B and Ministral 3 (14B Reasoning 2512) across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek R1 Distill Qwen 32B outperforms in 0 benchmarks, while Ministral 3 (14B Reasoning 2512) is better at 3 benchmarks (AIME 2024, GPQA, LiveCodeBench).
Ministral 3 (14B Reasoning 2512) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek R1 Distill Qwen 32B has 18.8B more parameters than Ministral 3 (14B Reasoning 2512), making it 134.3% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek R1 Distill Qwen 32B specifies input context (128,000 tokens). Only DeepSeek R1 Distill Qwen 32B specifies output context (128,000 tokens).
Input Capabilities
Supported data types and modalities
Ministral 3 (14B Reasoning 2512) supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 32B does not.
Ministral 3 (14B Reasoning 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek R1 Distill Qwen 32B
Ministral 3 (14B Reasoning 2512)
License
Usage and distribution terms
DeepSeek R1 Distill Qwen 32B is licensed under MIT, while Ministral 3 (14B Reasoning 2512) uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while Ministral 3 (14B Reasoning 2512) was released on 2025-12-04.
Ministral 3 (14B Reasoning 2512) is 11 months newer than DeepSeek R1 Distill Qwen 32B.
Jan 20, 2025
1.2 years ago
Dec 4, 2025
3 months ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Detailed Comparison
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