Model Comparison

DeepSeek R1 Zero vs Mistral Large 2

Comparing DeepSeek R1 Zero and Mistral Large 2 across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek R1 Zero and Mistral Large 2 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
Thu Apr 02 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Zero
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Mistral AI
Mistral Large 2
Input tokens$2.00
Output tokens$6.00
Best providerGoogle
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Model Size

Parameter count comparison

548.0B diff

DeepSeek R1 Zero has 548.0B more parameters than Mistral Large 2, making it 445.5% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Mistral AI
Mistral Large 2
123.0Bparameters
671.0B
DeepSeek R1 Zero
123.0B
Mistral Large 2

Context Window

Maximum input and output token capacity

Only Mistral Large 2 specifies input context (128,000 tokens). Only Mistral Large 2 specifies output context (128,000 tokens).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Mistral AI
Mistral Large 2
Input128,000 tokens
Output128,000 tokens
Thu Apr 02 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Mistral Large 2 uses Mistral Research License.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek R1 Zero

MIT

Open weights

Mistral Large 2

Mistral Research License

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Mistral Large 2 was released on 2024-07-24.

DeepSeek R1 Zero is 6 months newer than Mistral Large 2.

DeepSeek R1 Zero

Jan 20, 2025

1.2 years ago

6mo newer
Mistral Large 2

Jul 24, 2024

1.7 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

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Key Takeaways

DeepSeek
Mistral AI
Larger context window (128,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Zero
Mistral AI
Mistral Large 2

FAQ

Common questions about DeepSeek R1 Zero vs Mistral Large 2

DeepSeek R1 Zero (DeepSeek) and Mistral Large 2 (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Mistral Large 2 scores GSM8k: 93.0%, HumanEval: 92.0%, MT-Bench: 86.3%, MMLU: 84.0%, MMLU French: 82.8%.
DeepSeek R1 Zero supports an unknown number of tokens and Mistral Large 2 supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (MIT vs Mistral Research License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Zero is developed by DeepSeek and Mistral Large 2 is developed by Mistral AI.