Model Comparison

DeepSeek-R1-0528 vs MiniStral 3 (14B Instruct 2512)

Comparing DeepSeek-R1-0528 and MiniStral 3 (14B Instruct 2512) across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1-0528 and MiniStral 3 (14B 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
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
Mistral AI
MiniStral 3 (14B 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

657.0B diff

DeepSeek-R1-0528 has 657.0B more parameters than MiniStral 3 (14B Instruct 2512), making it 4692.9% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Mistral AI
MiniStral 3 (14B Instruct 2512)
14.0Bparameters
671.0B
DeepSeek-R1-0528
14.0B
MiniStral 3 (14B Instruct 2512)

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Mistral AI
MiniStral 3 (14B Instruct 2512)
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

MiniStral 3 (14B Instruct 2512) supports multimodal inputs, whereas DeepSeek-R1-0528 does not.

MiniStral 3 (14B Instruct 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-R1-0528

Text
Images
Audio
Video

MiniStral 3 (14B Instruct 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while MiniStral 3 (14B Instruct 2512) uses Apache 2.0.

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

DeepSeek-R1-0528

MIT

Open weights

MiniStral 3 (14B Instruct 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while MiniStral 3 (14B Instruct 2512) was released on 2025-12-04.

MiniStral 3 (14B Instruct 2512) is 6 months newer than DeepSeek-R1-0528.

DeepSeek-R1-0528

May 28, 2025

10 months ago

MiniStral 3 (14B Instruct 2512)

Dec 4, 2025

4 months ago

6mo 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)
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Mistral AI
MiniStral 3 (14B Instruct 2512)

FAQ

Common questions about DeepSeek-R1-0528 vs MiniStral 3 (14B Instruct 2512)

DeepSeek-R1-0528 (DeepSeek) and MiniStral 3 (14B 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-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. MiniStral 3 (14B Instruct 2512) scores MATH: 90.4%, Wild Bench: 68.5%, Arena Hard: 55.1%, MM-MT-Bench: 8.5%.
DeepSeek-R1-0528 supports 131K tokens and MiniStral 3 (14B 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-R1-0528 is developed by DeepSeek and MiniStral 3 (14B Instruct 2512) is developed by Mistral AI.