MiMo-V2.5 vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.2 to 35.9. MiMo-V2.5 is 4.9x cheaper per token.
Xiaomi · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to 35.9, ranking #34 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, MiMo-V2.5 is roughly 4.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.5 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-V2.5
- cost matters — it's about 4.9x 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
- overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
- your work emphasizes reasoning and agents — 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
14 reported for MiMo-V2.5 · 18 for Mistral Large 4
MiMo-V2.5 outperforms in 0 benchmarks, while Mistral Large 4 is better at 1 benchmark (Finance Agent v2).
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, MiMo-V2.5 ($0.17/1M tokens) is 4.0x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, MiMo-V2.5 ($0.34/1M tokens) is 6.2x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than MiMo-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 739.2B more parameters than MiMo-V2.5, making it 237.9% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.5 accepts 1,048,576 input tokens compared to Mistral Large 4's 1,000,000 tokens. Only MiMo-V2.5 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Both MiMo-V2.5 and Mistral Large 4 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MiMo-V2.5
Mistral Large 4
License
Usage and distribution terms
MiMo-V2.5 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-V2.5 was released on 2026-04-22, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 6 months newer than MiMo-V2.5.
Apr 22, 2026
5 months ago
Oct 6, 2026
1 days ago
5mo 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-V2.5 is available from Novita, DeepInfra. Mistral Large 4 is available from Mistral AI.
MiMo-V2.5
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.5 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.5 vs Mistral Large 4.