MiniMax M2.1 vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 45.8 to 31.3. MiniMax M2.1 is 2.0x cheaper per token.
MiniMax · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 45.8 to 31.3, ranking #39 overall.
In the 1 individual benchmarks reported for both models, Mistral Large 4 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, MiniMax M2.1 is roughly 2.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose MiniMax M2.1
- cost matters — it's about 2.0x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- overall performance matters — it scores 45.8 and ranks #39 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
25 reported for MiniMax M2.1 · 18 for Mistral Large 4
MiniMax M2.1 outperforms in 0 benchmarks, while Mistral Large 4 is better at 1 benchmark (SciCode).
Mistral Large 4 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, MiniMax M2.1 ($0.30/1M tokens) is 2.3x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, MiniMax M2.1 ($1.20/1M tokens) is 1.7x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than MiniMax M2.1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 820.0B more parameters than MiniMax M2.1, making it 356.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,000,000 tokens. Only MiniMax M2.1 specifies output context (1,000,000 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas MiniMax M2.1 does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiniMax M2.1
Mistral Large 4
License
Usage and distribution terms
MiniMax M2.1 is licensed under MIT, while Mistral Large 4 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
MiniMax M2.1 was released on 2025-12-23, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 10 months newer than MiniMax M2.1.
Dec 23, 2025
10 months ago
Oct 6, 2026
2 weeks ago
9mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
MiniMax M2.1 is available from MiniMax. Mistral Large 4 is available from Mistral AI.
MiniMax M2.1
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against MiniMax M2.1 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiniMax M2.1 vs Mistral Large 4.