GLM-5.2 vs MiMo-V2.6-Flash
GLM-5.2 and MiMo-V2.6-Flash are closely matched at 45.5 and 45.6 on the LLM Stats Score. MiMo-V2.6-Flash is 6.6x cheaper per token.
Zhipu AI · Xiaomi · Updated for 2026
Which is better?
GLM-5.2 and MiMo-V2.6-Flash are closely matched on the overall LLM Stats Score at 45.5 and 45.6.
In the 3 individual benchmarks reported for both models, MiMo-V2.6-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, MiMo-V2.6-Flash is roughly 6.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-5.2
- you want predictable pricing at $0.75/M input and $2.40/M output
Choose MiMo-V2.6-Flash
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- cost matters — it's about 6.6x cheaper per token
- 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
19 reported for GLM-5.2 · 16 for MiMo-V2.6-Flash
GLM-5.2 outperforms in 1 benchmarks (Program Bench), while MiMo-V2.6-Flash is better at 2 benchmarks (DeepSWE 1.1, Terminal-Bench 2.1).
MiMo-V2.6-Flash shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.2 ($0.75/1M tokens) is 5.4x more expensive than MiMo-V2.6-Flash ($0.14/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 8.6x more expensive than MiMo-V2.6-Flash ($0.28/1M tokens).
In conclusion, GLM-5.2 is more expensive than MiMo-V2.6-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.2 has 444.0B more parameters than MiMo-V2.6-Flash, making it 143.7% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Only GLM-5.2 specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Flash supports multimodal inputs, whereas GLM-5.2 does not.
MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
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-5.2 was released on 2026-06-16, while MiMo-V2.6-Flash was released on 2026-09-22.
MiMo-V2.6-Flash is 3 months newer than GLM-5.2.
Jun 16, 2026
3 months ago
Sep 22, 2026
0 days ago
3mo 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-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. MiMo-V2.6-Flash is available from Xiaomi.
GLM-5.2
MiMo-V2.6-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs MiMo-V2.6-Flash.