MiMo-V2.6-Pro vs Mistral Large 4
MiMo-V2.6-Pro and Mistral Large 4 are closely matched at 48.9 and 45.8 on the LLM Stats Score. MiMo-V2.6-Pro is 1.9x cheaper per token.
Xiaomi · Mistral AI · Updated for 2026
Which is better?
MiMo-V2.6-Pro and Mistral Large 4 are closely matched on the overall LLM Stats Score at 48.9 and 45.8.
In the 3 individual benchmarks reported for both models, MiMo-V2.6-Pro wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, MiMo-V2.6-Pro is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Pro also accepts a larger context window (1,048,576 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 MiMo-V2.6-Pro
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 1.9x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- 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
18 reported for MiMo-V2.6-Pro · 18 for Mistral Large 4
MiMo-V2.6-Pro outperforms in 3 benchmarks (CyberGym, DeepSWE 1.1, Terminal-Bench 4.0), while Mistral Large 4 is better at 0 benchmarks.
MiMo-V2.6-Pro 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, MiMo-V2.6-Pro ($0.43/1M tokens) is 1.6x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, MiMo-V2.6-Pro ($0.87/1M tokens) is 2.4x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than MiMo-V2.6-Pro.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 30.0B more parameters than MiMo-V2.6-Pro, making it 2.9% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to Mistral Large 4's 1,000,000 tokens.
Input capabilities
Documented input modalities across available providers
Both MiMo-V2.6-Pro and Mistral Large 4 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MiMo-V2.6-Pro
Mistral Large 4
License
Usage and distribution terms
MiMo-V2.6-Pro 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
MiMo-V2.6-Pro was released on 2026-09-22, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 0 month newer than MiMo-V2.6-Pro.
Sep 22, 2026
3 weeks ago
Oct 6, 2026
1 weeks ago
2w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
MiMo-V2.6-Pro is available from Xiaomi. Mistral Large 4 is available from Mistral AI.
MiMo-V2.6-Pro
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Pro and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Pro vs Mistral Large 4.