Model Comparison

DeepSeek-V3.2-Exp vs Ministral 3 (8B Instruct 2512)

Comparing DeepSeek-V3.2-Exp and Ministral 3 (8B Instruct 2512) across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2-Exp and Ministral 3 (8B Instruct 2512) 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
Tue Apr 21 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2-Exp
Input tokens$0.27
Output tokens$0.41
Best providerNovita
Mistral AI
Ministral 3 (8B Instruct 2512)
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

677.0B diff

DeepSeek-V3.2-Exp has 677.0B more parameters than Ministral 3 (8B Instruct 2512), making it 8462.5% larger.

DeepSeek
DeepSeek-V3.2-Exp
685.0Bparameters
Mistral AI
Ministral 3 (8B Instruct 2512)
8.0Bparameters
685.0B
DeepSeek-V3.2-Exp
8.0B
Ministral 3 (8B Instruct 2512)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2-Exp specifies input context (163,840 tokens). Only DeepSeek-V3.2-Exp specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Mistral AI
Ministral 3 (8B Instruct 2512)
Input- tokens
Output- tokens
Tue Apr 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Ministral 3 (8B Instruct 2512) supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.

Ministral 3 (8B Instruct 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2-Exp

Text
Images
Audio
Video

Ministral 3 (8B Instruct 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2-Exp is licensed under MIT, while Ministral 3 (8B Instruct 2512) uses Apache 2.0.

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

DeepSeek-V3.2-Exp

MIT

Open weights

Ministral 3 (8B Instruct 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Exp was released on 2025-09-29, while Ministral 3 (8B Instruct 2512) was released on 2025-12-04.

Ministral 3 (8B Instruct 2512) is 2 months newer than DeepSeek-V3.2-Exp.

DeepSeek-V3.2-Exp

Sep 29, 2025

6 months ago

Ministral 3 (8B Instruct 2512)

Dec 4, 2025

4 months ago

2mo 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)
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2-Exp
Mistral AI
Ministral 3 (8B Instruct 2512)

FAQ

Common questions about DeepSeek-V3.2-Exp vs Ministral 3 (8B Instruct 2512)

DeepSeek-V3.2-Exp (DeepSeek) and Ministral 3 (8B Instruct 2512) (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-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%. Ministral 3 (8B Instruct 2512) scores MATH: 87.6%, Wild Bench: 66.8%, Arena Hard: 50.9%, MM-MT-Bench: 8.1%.
DeepSeek-V3.2-Exp supports 164K tokens and Ministral 3 (8B Instruct 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 vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.2-Exp is developed by DeepSeek and Ministral 3 (8B Instruct 2512) is developed by Mistral AI.