DeepSeek-V3.2-Exp vs Magistral Small 2506 Comparison
Comparing DeepSeek-V3.2-Exp and Magistral Small 2506 across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Exp outperforms in 3 benchmarks (AIME 2025, GPQA, LiveCodeBench), while Magistral Small 2506 is better at 0 benchmarks.
DeepSeek-V3.2-Exp 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-V3.2-Exp has 661.0B more parameters than Magistral Small 2506, making it 2754.2% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2-Exp specifies input context (163,840 tokens). Only DeepSeek-V3.2-Exp specifies output context (65,536 tokens).
License
Usage and distribution terms
DeepSeek-V3.2-Exp is licensed under MIT, while Magistral Small 2506 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-V3.2-Exp was released on 2025-09-29, while Magistral Small 2506 was released on 2025-06-10.
DeepSeek-V3.2-Exp is 4 months newer than Magistral Small 2506.
Sep 29, 2025
5 months ago
3mo newerJun 10, 2025
9 months ago
Knowledge Cutoff
When training data ends
Magistral Small 2506 has a documented knowledge cutoff of 2025-06-01, while DeepSeek-V3.2-Exp's cutoff date is not specified.
We can confirm Magistral Small 2506's training data extends to 2025-06-01, but cannot make a direct comparison without DeepSeek-V3.2-Exp's cutoff date.
—
Jun 2025
Outputs Comparison
Key Takeaways
DeepSeek-V3.2-Exp
View detailsDeepSeek
Magistral Small 2506
View detailsMistral AI
Detailed Comparison
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