Model Comparison

DeepSeek-V3.1 vs Mistral Large 3 (675B Base)

DeepSeek-V3.1 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, LiveCodeBench, SimpleQA), while Mistral Large 3 (675B Base) is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Wed Apr 15 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
Wed Apr 15 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Mistral AI
Mistral Large 3 (675B Base)
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

4.0B diff

Mistral Large 3 (675B Base) has 4.0B more parameters than DeepSeek-V3.1, making it 0.6% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Mistral AI
Mistral Large 3 (675B Base)
675.0Bparameters
671.0B
DeepSeek-V3.1
675.0B
Mistral Large 3 (675B Base)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Mistral AI
Mistral Large 3 (675B Base)
Input- tokens
Output- tokens
Wed Apr 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Mistral Large 3 (675B Base) supports multimodal inputs, whereas DeepSeek-V3.1 does not.

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

DeepSeek-V3.1

Text
Images
Audio
Video

Mistral Large 3 (675B Base)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Mistral Large 3 (675B Base) uses Apache 2.0.

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

DeepSeek-V3.1

MIT

Open weights

Mistral Large 3 (675B Base)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Mistral Large 3 (675B Base) was released on 2025-12-04.

Mistral Large 3 (675B Base) is 11 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Mistral Large 3 (675B Base)

Dec 4, 2025

4 months ago

10mo 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 (163,840 tokens)
Higher GPQA score (74.9% vs 43.9%)
Higher LiveCodeBench score (56.4% vs 34.4%)
Higher SimpleQA score (93.4% vs 23.8%)
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Mistral AI
Mistral Large 3 (675B Base)

FAQ

Common questions about DeepSeek-V3.1 vs Mistral Large 3 (675B Base)

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Mistral Large 3 (675B Base) is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Mistral Large 3 (675B Base) scores MMMLU: 85.5%, AMC_2022_23: 52.0%, GPQA: 43.9%, LiveCodeBench: 34.4%, SimpleQA: 23.8%.
DeepSeek-V3.1 supports 164K tokens and Mistral Large 3 (675B Base) 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-V3.1 is developed by DeepSeek and Mistral Large 3 (675B Base) is developed by Mistral AI.