DeepSeek R1 Distill Llama 8B vs Ministral 3 (14B Reasoning 2512) Comparison
Comparing DeepSeek R1 Distill Llama 8B and Ministral 3 (14B Reasoning 2512) across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek R1 Distill Llama 8B 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
Ministral 3 (14B Reasoning 2512) has 6.0B more parameters than DeepSeek R1 Distill Llama 8B, making it 74.3% larger.
Context Window
Maximum input and output token capacity
Only Ministral 3 (14B Reasoning 2512) specifies input context (262,100 tokens). Only Ministral 3 (14B Reasoning 2512) specifies output context (262,100 tokens).
Input Capabilities
Supported data types and modalities
Ministral 3 (14B Reasoning 2512) supports multimodal inputs, whereas DeepSeek R1 Distill Llama 8B 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 Llama 8B
Ministral 3 (14B Reasoning 2512)
License
Usage and distribution terms
DeepSeek R1 Distill Llama 8B 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 Llama 8B 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 Llama 8B.
Jan 20, 2025
1.1 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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