Model Comparison

DeepSeek-V3.2-Speciale vs Magistral Small 2506

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-V3.2-Speciale outperforms in 1 benchmarks (AIME 2025), while Magistral Small 2506 is better at 0 benchmarks.

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.

Sun May 24 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

661.0B diff

DeepSeek-V3.2-Speciale has 661.0B more parameters than Magistral Small 2506, making it 2754.2% larger.

DeepSeek
DeepSeek-V3.2-Speciale
685.0Bparameters
Mistral AI
Magistral Small 2506
24.0Bparameters
685.0B
DeepSeek-V3.2-Speciale
24.0B
Magistral Small 2506

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2-Speciale specifies input context (131,072 tokens). Only DeepSeek-V3.2-Speciale specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3.2-Speciale
Input131,072 tokens
Output131,072 tokens
Mistral AI
Magistral Small 2506
Input- tokens
Output- tokens
Sun May 24 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2-Speciale 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.

DeepSeek-V3.2-Speciale

MIT

Open weights

Magistral Small 2506

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Speciale was released on 2025-12-01, while Magistral Small 2506 was released on 2025-06-10.

DeepSeek-V3.2-Speciale is 6 months newer than Magistral Small 2506.

DeepSeek-V3.2-Speciale

Dec 1, 2025

5 months ago

5mo newer
Magistral Small 2506

Jun 10, 2025

11 months ago

Knowledge Cutoff

When training data ends

Magistral Small 2506 has a documented knowledge cutoff of 2025-06-01, while DeepSeek-V3.2-Speciale'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-Speciale's cutoff date.

DeepSeek-V3.2-Speciale

—

Magistral Small 2506

Jun 2025

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher AIME 2025 score (96.0% vs 62.8%)
Mistral AI

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2-Speciale
Mistral AI
Magistral Small 2506

FAQ

Common questions about DeepSeek-V3.2-Speciale vs Magistral Small 2506.

Which is better, DeepSeek-V3.2-Speciale or Magistral Small 2506?

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks. DeepSeek-V3.2-Speciale is made by DeepSeek and Magistral Small 2506 is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.2-Speciale compare to Magistral Small 2506 in benchmarks?

DeepSeek-V3.2-Speciale scores HMMT 2025: 99.2%, AIME 2025: 96.0%, CodeForces: 90.0%, t2-bench: 80.3%, SWE-Bench Verified: 73.1%. Magistral Small 2506 scores AIME 2024: 70.7%, GPQA: 68.2%, AIME 2025: 62.8%, LiveCodeBench: 51.3%.

What are the context window sizes for DeepSeek-V3.2-Speciale and Magistral Small 2506?

DeepSeek-V3.2-Speciale supports 131K tokens and Magistral Small 2506 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2-Speciale and Magistral Small 2506?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2-Speciale and Magistral Small 2506?

DeepSeek-V3.2-Speciale is developed by DeepSeek and Magistral Small 2506 is developed by Mistral AI.