DeepSeek R1 Distill Llama 8B vs Ministral 3 (8B Reasoning 2512) Comparison
Comparing DeepSeek R1 Distill Llama 8B and Ministral 3 (8B 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 (8B Reasoning 2512) is better at 3 benchmarks (AIME 2024, GPQA, LiveCodeBench).
Ministral 3 (8B 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 Llama 8B has 0.0B more parameters than Ministral 3 (8B Reasoning 2512), making it 0.4% larger.
Context Window
Maximum input and output token capacity
Only Ministral 3 (8B Reasoning 2512) specifies input context (262,100 tokens). Only Ministral 3 (8B Reasoning 2512) specifies output context (262,100 tokens).
Input Capabilities
Supported data types and modalities
Ministral 3 (8B Reasoning 2512) supports multimodal inputs, whereas DeepSeek R1 Distill Llama 8B does not.
Ministral 3 (8B 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 (8B Reasoning 2512)
License
Usage and distribution terms
DeepSeek R1 Distill Llama 8B is licensed under MIT, while Ministral 3 (8B 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 (8B Reasoning 2512) was released on 2025-12-04.
Ministral 3 (8B 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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