Model Comparison

DeepSeek-V3 vs Ministral 3 (3B Base 2512)

DeepSeek-V3 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3 outperforms in 2 benchmarks (MMLU, MMLU-Redux), while Ministral 3 (3B Base 2512) is better at 0 benchmarks.

DeepSeek-V3 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
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Mistral AI
Ministral 3 (3B Base 2512)
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

668.0B diff

DeepSeek-V3 has 668.0B more parameters than Ministral 3 (3B Base 2512), making it 22266.7% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Mistral AI
Ministral 3 (3B Base 2512)
3.0Bparameters
671.0B
DeepSeek-V3
3.0B
Ministral 3 (3B Base 2512)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Mistral AI
Ministral 3 (3B Base 2512)
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Ministral 3 (3B Base 2512) supports multimodal inputs, whereas DeepSeek-V3 does not.

Ministral 3 (3B Base 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3

Text
Images
Audio
Video

Ministral 3 (3B Base 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Ministral 3 (3B Base 2512) uses Apache 2.0.

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

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Ministral 3 (3B Base 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Ministral 3 (3B Base 2512) was released on 2025-12-04.

Ministral 3 (3B Base 2512) is 11 months newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.3 years ago

Ministral 3 (3B Base 2512)

Dec 4, 2025

4 months ago

11mo 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 MMLU score (88.5% vs 70.7%)
Higher MMLU-Redux score (89.1% vs 73.5%)
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Mistral AI
Ministral 3 (3B Base 2512)

FAQ

Common questions about DeepSeek-V3 vs Ministral 3 (3B Base 2512)

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Ministral 3 (3B Base 2512) is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Ministral 3 (3B Base 2512) scores MMLU-Redux: 73.5%, MMLU: 70.7%, Multilingual MMLU: 65.2%, MATH (CoT): 60.1%, TriviaQA: 59.2%.
DeepSeek-V3 supports 131K tokens and Ministral 3 (3B Base 2512) 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 + Model License (Commercial use allowed) vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Ministral 3 (3B Base 2512) is developed by Mistral AI.