GLM-5.3 vs MiMo-V2.6-Flash
GLM-5.3 leads the LLM Stats Score 52.1 to 45.7. MiMo-V2.6-Flash is 10.9x cheaper per token.
Zhipu AI · Xiaomi · Updated for 2026
Which is better?
GLM-5.3 leads the overall LLM Stats Score 52.1 to 45.7, ranking #10 overall.
In the 8 individual benchmarks reported for both models, GLM-5.3 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, MiMo-V2.6-Flash is roughly 10.9x 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-5.3
- overall performance matters — it scores 52.1 and ranks #10 on LLM Stats
- you value its reported benchmark strengths — it wins 5 of 8 exact shared results
Choose MiMo-V2.6-Flash
- cost matters — it's about 10.9x 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
17 reported for GLM-5.3 · 16 for MiMo-V2.6-Flash
GLM-5.3 outperforms in 5 benchmarks (Agents' Last Exam, ExploitBench, ExploitGym, Terminal-Bench 2.1, Terminal-Bench 4.0), while MiMo-V2.6-Flash is better at 3 benchmarks (CyberGym, DeepSWE 1.1, Program Bench).
GLM-5.3 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-5.3 ($1.20/1M tokens) is 8.6x more expensive than MiMo-V2.6-Flash ($0.14/1M tokens).
For output processing, GLM-5.3 ($4.00/1M tokens) is 14.3x more expensive than MiMo-V2.6-Flash ($0.28/1M tokens).
In conclusion, GLM-5.3 is more expensive than MiMo-V2.6-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3 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-5.3 specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Flash supports multimodal inputs, whereas GLM-5.3 does not.
MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3
MiMo-V2.6-Flash
License
Usage and distribution terms
GLM-5.3 is licensed under GLM-5.3 License, while MiMo-V2.6-Flash uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
GLM-5.3 License
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GLM-5.3 was released on 2026-08-14, while MiMo-V2.6-Flash was released on 2026-09-22.
MiMo-V2.6-Flash is 1 month newer than GLM-5.3.
Aug 14, 2026
1 months ago
Sep 22, 2026
0 days ago
1mo 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-5.3 is available from DeepInfra, FriendliAI, Novita, ZAI. MiMo-V2.6-Flash is available from Xiaomi.
GLM-5.3
MiMo-V2.6-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3 and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3 vs MiMo-V2.6-Flash.