GLM-5 vs MiMo-V2.6-Flash
MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 37.1. MiMo-V2.6-Flash is 8.9x cheaper per token.
Zhipu AI · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 37.1, ranking #29 overall.
On price, MiMo-V2.6-Flash is roughly 8.9x 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 GLM-5
- you want predictable pricing at $1.00/M input and $3.20/M output
Choose MiMo-V2.6-Flash
- overall performance matters — it scores 45.6 and ranks #29 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- cost matters — it's about 8.9x 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
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
5 reported for GLM-5 · 16 for MiMo-V2.6-Flash
GLM-5 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, GLM-5 ($1.00/1M tokens) is 7.1x more expensive than MiMo-V2.6-Flash ($0.14/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 11.4x more expensive than MiMo-V2.6-Flash ($0.28/1M tokens).
In conclusion, GLM-5 is more expensive than MiMo-V2.6-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 435.0B more parameters than MiMo-V2.6-Flash, making it 140.8% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Flash accepts 1,048,576 input tokens compared to GLM-5's 200,000 tokens. Only GLM-5 specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Flash supports multimodal inputs, whereas GLM-5 does not.
MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
MiMo-V2.6-Flash
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while MiMo-V2.6-Flash was released on 2026-09-22.
MiMo-V2.6-Flash is 7 months newer than GLM-5.
Feb 11, 2026
7 months ago
Sep 22, 2026
0 days ago
7mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5 is available from FriendliAI, ZAI. MiMo-V2.6-Flash is available from Xiaomi.
GLM-5
MiMo-V2.6-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5 and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5 vs MiMo-V2.6-Flash.