Model Comparison

DeepSeek-V3.2 (Thinking) vs Mistral Large 3 (675B Instruct 2512 Eagle)

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 2 benchmarks (GPQA, LiveCodeBench), while Mistral Large 3 (675B Instruct 2512 Eagle) is better at 0 benchmarks.

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Thu Apr 16 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
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Mistral AI
Mistral Large 3 (675B Instruct 2512 Eagle)
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

10.0B diff

DeepSeek-V3.2 (Thinking) has 10.0B more parameters than Mistral Large 3 (675B Instruct 2512 Eagle), making it 1.5% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Mistral AI
Mistral Large 3 (675B Instruct 2512 Eagle)
675.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
675.0B
Mistral Large 3 (675B Instruct 2512 Eagle)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Mistral AI
Mistral Large 3 (675B Instruct 2512 Eagle)
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Mistral Large 3 (675B Instruct 2512 Eagle) supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Mistral Large 3 (675B Instruct 2512 Eagle) can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2 (Thinking)

Text
Images
Audio
Video

Mistral Large 3 (675B Instruct 2512 Eagle)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Mistral Large 3 (675B Instruct 2512 Eagle) uses Apache 2.0.

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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Mistral Large 3 (675B Instruct 2512 Eagle)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Mistral Large 3 (675B Instruct 2512 Eagle) was released on 2025-12-04.

Mistral Large 3 (675B Instruct 2512 Eagle) is 0 month newer than DeepSeek-V3.2 (Thinking).

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

4 months ago

Mistral Large 3 (675B Instruct 2512 Eagle)

Dec 4, 2025

4 months ago

3d newer

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
Larger context window (131,072 tokens)
Higher GPQA score (82.4% vs 43.9%)
Higher LiveCodeBench score (83.3% vs 34.4%)
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Mistral AI
Mistral Large 3 (675B Instruct 2512 Eagle)

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Mistral Large 3 (675B Instruct 2512 Eagle)

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Mistral Large 3 (675B Instruct 2512 Eagle) is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. Mistral Large 3 (675B Instruct 2512 Eagle) scores MMMLU: 85.5%, AMC_2022_23: 52.0%, GPQA: 43.9%, LiveCodeBench: 34.4%, SimpleQA: 23.8%.
DeepSeek-V3.2 (Thinking) supports 131K tokens and Mistral Large 3 (675B Instruct 2512 Eagle) 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-V3.2 (Thinking) is developed by DeepSeek and Mistral Large 3 (675B Instruct 2512 Eagle) is developed by Mistral AI.