MiMo-V2-Pro vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads the LLM Stats Score 49.9 to 35.6. MiMo-V2.6-Pro is 2.8x cheaper per token.
Xiaomi · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 49.9 to 35.6, ranking #20 overall.
On price, MiMo-V2.6-Pro is roughly 2.8x 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-Pro
- you want predictable pricing at $1.00/M input and $3.00/M output
Choose MiMo-V2.6-Pro
- overall performance matters — it scores 49.9 and ranks #20 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 2.8x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for MiMo-V2-Pro · 18 for MiMo-V2.6-Pro
MiMo-V2-Pro and MiMo-V2.6-Prodon'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, MiMo-V2-Pro ($1.00/1M tokens) is 2.3x more expensive than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, MiMo-V2-Pro ($3.00/1M tokens) is 3.4x more expensive than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, MiMo-V2-Pro is more expensive than MiMo-V2.6-Pro.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 20.0B more parameters than MiMo-V2-Pro, making it 2.0% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to MiMo-V2-Pro's 1,000,000 tokens. Only MiMo-V2-Pro specifies output context (16,384 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Pro supports multimodal inputs, whereas MiMo-V2-Pro does not.
MiMo-V2.6-Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiMo-V2-Pro
MiMo-V2.6-Pro
License
Usage and distribution terms
MiMo-V2-Pro is licensed under a proprietary license, while MiMo-V2.6-Pro uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
MiMo-V2-Pro was released on 2026-03-18, while MiMo-V2.6-Pro was released on 2026-09-22.
MiMo-V2.6-Pro is 6 months newer than MiMo-V2-Pro.
Mar 18, 2026
6 months ago
Sep 22, 2026
1 weeks ago
6mo 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-Pro is available from Xiaomi. MiMo-V2.6-Pro is available from Xiaomi.
MiMo-V2-Pro
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2-Pro and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2-Pro vs MiMo-V2.6-Pro.