Model Comparison

DeepSeek-R1 vs Magistral Medium

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

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Magistral Medium 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 Medium
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 Medium, making it 2695.8% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
Mistral AI
Magistral Medium
24.0Bparameters
671.0B
DeepSeek-R1
24.0B
Magistral Medium

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 Medium
Input- tokens
Output- tokens
Fri Apr 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Magistral Medium supports multimodal inputs, whereas DeepSeek-R1 does not.

Magistral Medium can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-R1

Text
Images
Audio
Video

Magistral Medium

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1 is licensed under MIT, while Magistral Medium 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 Medium

Apache 2.0

Open weights

Release Timeline

When each model was launched

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

Magistral Medium is 5 months newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.2 years ago

Magistral Medium

Jun 10, 2025

10 months ago

4mo newer

Knowledge Cutoff

When training data ends

Magistral Medium has a documented knowledge cutoff of 2025-06-01, while DeepSeek-R1's cutoff date is not specified.

We can confirm Magistral Medium's training data extends to 2025-06-01, but cannot make a direct comparison without DeepSeek-R1's cutoff date.

DeepSeek-R1

—

Magistral Medium

Jun 2025

Outputs Comparison

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Key Takeaways

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

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
Mistral AI
Magistral Medium

FAQ

Common questions about DeepSeek-R1 vs Magistral Medium

DeepSeek-R1 (DeepSeek) and Magistral Medium (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 Medium scores AIME 2024: 73.6%, GPQA: 70.8%, AIME 2025: 64.9%, LiveCodeBench: 50.3%, Aider-Polyglot: 47.1%.
DeepSeek-R1 supports 131K tokens and Magistral Medium 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 multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1 is developed by DeepSeek and Magistral Medium is developed by Mistral AI.