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DeepSeek-V4.1-Flash vs Ministral 3 (14B Base 2512)

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 3.6.

DeepSeek · Mistral AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 3.6, ranking #13 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 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-V4.1-Flash
DeepSeek
Ministral 3 (14B Base 2512)
Mistral AI
Core performance indexes
LLM Stats Score
51.8
#13
3.6
#308
Reasoning
48.9
#18
3.5
#300
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash
Ministral 3 (14B Base 2512)
Math
35.2#43
8.8#256
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 6 for Ministral 3 (14B Base 2512)

No common benchmarks found

DeepSeek-V4.1-Flash and Ministral 3 (14B Base 2512)don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

749.2B diff

DeepSeek-V4.1-Flash has 749.2B more parameters than Ministral 3 (14B Base 2512), making it 5351.5% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Mistral AI
Ministral 3 (14B Base 2512)
14.0Bparameters
763.2B
DeepSeek-V4.1-Flash
14.0B
Ministral 3 (14B Base 2512)

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Mistral AI
Ministral 3 (14B Base 2512)
Input- tokens
Output- tokens
Tue Sep 15 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Ministral 3 (14B Base 2512) support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Ministral 3 (14B Base 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash 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-V4.1-Flash

MIT

Open weights

Ministral 3 (14B Base 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Ministral 3 (14B Base 2512) was released on 2025-12-04.

DeepSeek-V4.1-Flash is 9 months newer than Ministral 3 (14B Base 2512).

DeepSeek-V4.1-Flash

Sep 10, 2026

5 days ago

9mo newer
Ministral 3 (14B Base 2512)

Dec 4, 2025

9 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-V4.1-Flash and Ministral 3 (14B Base 2512) side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Ministral 3 (14B Base 2512)
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Ministral 3 (14B Base 2512).

Which is better, DeepSeek-V4.1-Flash or Ministral 3 (14B Base 2512)?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 3.6. DeepSeek-V4.1-Flash is made by DeepSeek and Ministral 3 (14B Base 2512) is made by Mistral AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4.1-Flash compare to Ministral 3 (14B Base 2512) in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. 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-V4.1-Flash and Ministral 3 (14B Base 2512)?

DeepSeek-V4.1-Flash 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-V4.1-Flash and Ministral 3 (14B Base 2512)?

Key differences include LLM Stats Score (51.8 vs 3.6), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Ministral 3 (14B Base 2512)?

DeepSeek-V4.1-Flash is developed by DeepSeek and Ministral 3 (14B Base 2512) is developed by Mistral AI.

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