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DeepSeek-V4-Pro-0813 vs Ministral 3 (14B Base 2512)

Comparing DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512) across benchmarks, pricing, and capabilities.

DeepSeek · Mistral AI · Updated for 2026

Which is better?

DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512) trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Pro-0813

  • you want the most recent training data — it shipped Aug 2026

Choose Ministral 3 (14B Base 2512)

  • you are already invested in the Mistral AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4-Pro-0813
DeepSeek
Ministral 3 (14B Base 2512)
Mistral AI
Benchmark wins
—
—
Input price
$0.43 / M
— / M
Output price
$0.87 / M
— / M
Context window
1,048,576
—
Released
Aug 2026
Dec 2025
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512)don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

1586.0B diff

DeepSeek-V4-Pro-0813 has 1586.0B more parameters than Ministral 3 (14B Base 2512), making it 11328.6% larger.

DeepSeek
DeepSeek-V4-Pro-0813
1.6Tparameters
Mistral AI
Ministral 3 (14B Base 2512)
14.0Bparameters
1600.0B
DeepSeek-V4-Pro-0813
14.0B
Ministral 3 (14B Base 2512)

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-0813 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-0813 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4-Pro-0813
Input1,048,576 tokens
Output393,216 tokens
Mistral AI
Ministral 3 (14B Base 2512)
Input- tokens
Output- tokens
Mon Aug 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Ministral 3 (14B Base 2512) supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.

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

DeepSeek-V4-Pro-0813

Text
Images
Audio
Video

Ministral 3 (14B Base 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Pro-0813 is licensed under MIT, while Ministral 3 (14B Base 2512) uses Apache 2.0.

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

DeepSeek-V4-Pro-0813

MIT

Open weights

Ministral 3 (14B Base 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Ministral 3 (14B Base 2512) was released on 2025-12-04.

DeepSeek-V4-Pro-0813 is 8 months newer than Ministral 3 (14B Base 2512).

DeepSeek-V4-Pro-0813

Aug 13, 2026

1 weeks ago

8mo newer
Ministral 3 (14B Base 2512)

Dec 4, 2025

8 months ago

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

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512) side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-0813
✓ Preferred
Ministral 3 (14B Base 2512)
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-0813 vs Ministral 3 (14B Base 2512).

Which is better, DeepSeek-V4-Pro-0813 or Ministral 3 (14B Base 2512)?

DeepSeek-V4-Pro-0813 (DeepSeek) and Ministral 3 (14B Base 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.

How does DeepSeek-V4-Pro-0813 compare to Ministral 3 (14B Base 2512) in benchmarks?

DeepSeek-V4-Pro-0813 scores Terminal-Bench 2.1: 87.9%, CyberGym: 83.3%, Toolathlon: 74.1%, DSBench-FullStack: 71.1%, DSBench-Hard: 67.2%. Ministral 3 (14B Base 2512) scores MMLU-Redux: 82.0%, MMLU: 79.4%, TriviaQA: 74.9%, Multilingual MMLU: 74.2%, MATH (CoT): 67.6%.

What are the context window sizes for DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512)?

DeepSeek-V4-Pro-0813 supports 1.0M tokens and Ministral 3 (14B Base 2512) supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512)?

Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Pro-0813 and Ministral 3 (14B Base 2512)?

DeepSeek-V4-Pro-0813 is developed by DeepSeek and Ministral 3 (14B Base 2512) is developed by Mistral AI.

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