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Ministral 3 (14B Base 2512) vs Mistral Large 4

Mistral Large 4 leads the LLM Stats Score 46.2 to 3.6.

Mistral AI · Mistral AI · Updated for 2026

Which is better?

Mistral Large 4 leads the overall LLM Stats Score 46.2 to 3.6, ranking #34 overall.

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

Choose Ministral 3 (14B Base 2512)

  • you need open weights you can self-host or fine-tune

Choose Mistral Large 4

  • overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

Ministral 3 (14B Base 2512)
Mistral AI
Mistral Large 4
Mistral AI
Core performance indexes
LLM Stats Score
3.6
#324
46.2
#34
Reasoning
3.5
#316
44.0
#43
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.68 / M
Output price
— / M
$2.09 / M
Context window
—
1,000,000

Individual benchmarks

6 reported for Ministral 3 (14B Base 2512) · 18 for Mistral Large 4

No common benchmarks found

Ministral 3 (14B Base 2512) and Mistral Large 4don'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

1036.0B diff

Mistral Large 4 has 1036.0B more parameters than Ministral 3 (14B Base 2512), making it 7400.0% larger.

Mistral AI
Ministral 3 (14B Base 2512)
14.0Bparameters
Mistral AI
Mistral Large 4
1.1Tparameters
14.0B
Ministral 3 (14B Base 2512)
1050.0B
Mistral Large 4

Context Window

Maximum input and output token capacity

Only Mistral Large 4 specifies input context (1,000,000 tokens).

Mistral AI
Ministral 3 (14B Base 2512)
Input- tokens
Output- tokens
Mistral AI
Mistral Large 4
Input1,000,000 tokens
Output- tokens
Fri Oct 09 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Ministral 3 (14B Base 2512) and Mistral Large 4 support multimodal inputs.

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

Ministral 3 (14B Base 2512)

Text
Images
Audio
Video

Mistral Large 4

Text
Images
Audio
Video

License

Usage and distribution terms

Ministral 3 (14B Base 2512) is licensed under Apache 2.0, while Mistral Large 4 uses a proprietary license.

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

Ministral 3 (14B Base 2512)

Apache 2.0

Open weights

Mistral Large 4

Proprietary

Closed source

Release Timeline

When each model was launched

Ministral 3 (14B Base 2512) was released on 2025-12-04, while Mistral Large 4 was released on 2026-10-06.

Mistral Large 4 is 10 months newer than Ministral 3 (14B Base 2512).

Ministral 3 (14B Base 2512)

Dec 4, 2025

10 months ago

Mistral Large 4

Oct 6, 2026

2 days ago

10mo 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

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Ministral 3 (14B Base 2512) and Mistral Large 4 side-by-side, then vote on the output you prefer.

Ministral 3 (14B Base 2512)
✓ Preferred
Mistral Large 4
Open in Playground

FAQ

Common questions about Ministral 3 (14B Base 2512) vs Mistral Large 4.

Which is better, Ministral 3 (14B Base 2512) or Mistral Large 4?

Mistral Large 4 leads the LLM Stats Score 46.2 to 3.6. Ministral 3 (14B Base 2512) is made by Mistral AI and Mistral Large 4 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 Ministral 3 (14B Base 2512) compare to Mistral Large 4 in benchmarks?

Ministral 3 (14B Base 2512) scores MMLU-Redux: 82.0%, MMLU: 79.4%, TriviaQA: 74.9%, Multilingual MMLU: 74.2%, MATH (CoT): 67.6%. Mistral Large 4 scores B3 AI Security Benchmark: 93.3%, CyBench: 93.0%, SciCode: 91.8%, KORABench: 84.5%, CyberGym: 82.0%.

What are the context window sizes for Ministral 3 (14B Base 2512) and Mistral Large 4?

Ministral 3 (14B Base 2512) supports an unknown number of tokens and Mistral Large 4 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Ministral 3 (14B Base 2512) and Mistral Large 4?

Key differences include LLM Stats Score (3.6 vs 46.2), licensing (Apache 2.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

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