Ministral 3 (8B Reasoning 2512) vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.2 to 16.5. Ministral 3 (8B Reasoning 2512) is 6.9x cheaper per token.
Mistral AI · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to 16.5, ranking #34 overall.
On price, Ministral 3 (8B Reasoning 2512) is roughly 6.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 4 also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Ministral 3 (8B Reasoning 2512)
- cost matters — it's about 6.9x cheaper per token
- 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 and coding — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
4 reported for Ministral 3 (8B Reasoning 2512) · 18 for Mistral Large 4
Ministral 3 (8B Reasoning 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
Pricing Analysis
Price comparison per million tokens
For input processing, Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens) is 4.5x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens) is 13.9x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Ministral 3 (8B Reasoning 2512).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 1042.0B more parameters than Ministral 3 (8B Reasoning 2512), making it 13025.0% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Ministral 3 (8B Reasoning 2512)'s 262,100 tokens. Only Ministral 3 (8B Reasoning 2512) specifies output context (262,100 tokens).
Input capabilities
Documented input modalities across available providers
Both Ministral 3 (8B Reasoning 2512) and Mistral Large 4 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Ministral 3 (8B Reasoning 2512)
Mistral Large 4
License
Usage and distribution terms
Ministral 3 (8B Reasoning 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.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Ministral 3 (8B Reasoning 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 (8B Reasoning 2512).
Dec 4, 2025
10 months ago
Oct 6, 2026
1 weeks ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Ministral 3 (8B Reasoning 2512) is available from Mistral AI. Mistral Large 4 is available from Mistral AI.
Ministral 3 (8B Reasoning 2512)
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Ministral 3 (8B Reasoning 2512) and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ministral 3 (8B Reasoning 2512) vs Mistral Large 4.