Model Comparison

DeepSeek-R1 vs Magistral Small 2506

Comparing DeepSeek-R1 and Magistral Small 2506 across benchmarks, pricing, and capabilities.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Magistral Small 2506 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Fri Apr 17 2026 • llm-stats.com
DeepSeek
DeepSeek-R1
Input tokens$0.55
Output tokens$2.19
Best providerDeepSeek
Mistral AI
Magistral Small 2506
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

647.0B diff

DeepSeek-R1 has 647.0B more parameters than Magistral Small 2506, making it 2695.8% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
Mistral AI
Magistral Small 2506
24.0Bparameters
671.0B
DeepSeek-R1
24.0B
Magistral Small 2506

Context Window

Maximum input and output token capacity

Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Mistral AI
Magistral Small 2506
Input- tokens
Output- tokens
Fri Apr 17 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1 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-R1

MIT

Open weights

Magistral Small 2506

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while Magistral Small 2506 was released on 2025-06-10.

Magistral Small 2506 is 5 months newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.2 years ago

Magistral Small 2506

Jun 10, 2025

10 months ago

4mo newer

Knowledge Cutoff

When training data ends

Magistral Small 2506 has a documented knowledge cutoff of 2025-06-01, while DeepSeek-R1'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-R1's cutoff date.

DeepSeek-R1

—

Magistral Small 2506

Jun 2025

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Mistral AI

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
Mistral AI
Magistral Small 2506

FAQ

Common questions about DeepSeek-R1 vs Magistral Small 2506

DeepSeek-R1 (DeepSeek) and Magistral Small 2506 (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Magistral Small 2506 scores AIME 2024: 70.7%, GPQA: 68.2%, AIME 2025: 62.8%, LiveCodeBench: 51.3%.
DeepSeek-R1 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.
Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1 is developed by DeepSeek and Magistral Small 2506 is developed by Mistral AI.