Model Comparison

DeepSeek-V3.2-Exp vs Magistral Small 2506

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

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

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.

Tue Apr 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 21 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2-Exp
Input tokens$0.27
Output tokens$0.41
Best providerNovita
Mistral AI
Magistral Small 2506
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

661.0B diff

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

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

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).

DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Mistral AI
Magistral Small 2506
Input- tokens
Output- tokens
Tue Apr 21 2026 • llm-stats.com

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.

DeepSeek-V3.2-Exp

MIT

Open weights

Magistral Small 2506

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.

DeepSeek-V3.2-Exp

Sep 29, 2025

6 months ago

3mo newer
Magistral Small 2506

Jun 10, 2025

10 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.

DeepSeek-V3.2-Exp

—

Magistral Small 2506

Jun 2025

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Higher AIME 2025 score (89.3% vs 62.8%)
Higher GPQA score (79.9% vs 68.2%)
Higher LiveCodeBench score (74.1% vs 51.3%)
Mistral AI

Detailed Comparison

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

FAQ

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

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp 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.
DeepSeek-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%. Magistral Small 2506 scores AIME 2024: 70.7%, GPQA: 68.2%, AIME 2025: 62.8%, LiveCodeBench: 51.3%.
DeepSeek-V3.2-Exp supports 164K 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.
Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.2-Exp is developed by DeepSeek and Magistral Small 2506 is developed by Mistral AI.