Gemma 4 31B vs MiMo-V2.6-Flash
MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 33.1. Gemma 4 31B is 1.1x cheaper per token.
Google · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 33.1, ranking #29 overall.
On price, Gemma 4 31B is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Flash 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 Gemma 4 31B
- cost matters — it's about 1.1x cheaper per token
Choose MiMo-V2.6-Flash
- overall performance matters — it scores 45.6 and ranks #29 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Individual benchmarks
12 reported for Gemma 4 31B · 16 for MiMo-V2.6-Flash
Gemma 4 31B and MiMo-V2.6-Flashdon'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, Gemma 4 31B ($0.09/1M tokens) is 1.6x cheaper than MiMo-V2.6-Flash ($0.14/1M tokens).
For output processing, Gemma 4 31B ($0.34/1M tokens) is 1.2x more expensive than MiMo-V2.6-Flash ($0.28/1M tokens).
In conclusion, MiMo-V2.6-Flash is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Flash has 278.3B more parameters than Gemma 4 31B, making it 906.5% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Flash accepts 1,048,576 input tokens compared to Gemma 4 31B's 262,144 tokens. Only Gemma 4 31B specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both Gemma 4 31B and MiMo-V2.6-Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemma 4 31B
MiMo-V2.6-Flash
License
Usage and distribution terms
Gemma 4 31B is licensed under Apache 2.0, while MiMo-V2.6-Flash uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Gemma 4 31B was released on 2026-04-02, while MiMo-V2.6-Flash was released on 2026-09-22.
MiMo-V2.6-Flash is 6 months newer than Gemma 4 31B.
Apr 2, 2026
5 months ago
Sep 22, 2026
0 days ago
5mo newerKnowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while MiMo-V2.6-Flash's cutoff date is not specified.
We can confirm Gemma 4 31B's training data extends to 2025-01-01, but cannot make a direct comparison without MiMo-V2.6-Flash's cutoff date.
Jan 2025
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Provider Availability
Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together. MiMo-V2.6-Flash is available from Xiaomi.
Gemma 4 31B
MiMo-V2.6-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 4 31B and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 4 31B vs MiMo-V2.6-Flash.