GLM-5.3 vs MiMo-V2.6-Pro
GLM-5.3 and MiMo-V2.6-Pro are closely matched at 52.1 and 50.0 on the LLM Stats Score. MiMo-V2.6-Pro is 3.5x cheaper per token.
Zhipu AI · Xiaomi · Updated for 2026
Which is better?
GLM-5.3 and MiMo-V2.6-Pro are closely matched on the overall LLM Stats Score at 52.1 and 50.0.
In the 8 individual benchmarks reported for both models, MiMo-V2.6-Pro wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, MiMo-V2.6-Pro is roughly 3.5x 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
- you want predictable pricing at $1.20/M input and $4.00/M output
Choose MiMo-V2.6-Pro
- you value its reported benchmark strengths — it wins 6 of 8 exact shared results
- cost matters — it's about 3.5x 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 · 18 for MiMo-V2.6-Pro
GLM-5.3 outperforms in 2 benchmarks (ExploitBench, Terminal-Bench 4.0), while MiMo-V2.6-Pro is better at 6 benchmarks (Agents' Last Exam, CyberGym, DeepSWE 1.1, ExploitGym, Program Bench, Terminal-Bench 2.1).
MiMo-V2.6-Pro 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 2.8x more expensive than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, GLM-5.3 ($4.00/1M tokens) is 4.6x more expensive than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, GLM-5.3 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 267.0B more parameters than GLM-5.3, making it 35.5% 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-Pro supports multimodal inputs, whereas GLM-5.3 does not.
MiMo-V2.6-Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3
MiMo-V2.6-Pro
License
Usage and distribution terms
GLM-5.3 is licensed under GLM-5.3 License, while MiMo-V2.6-Pro 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-Pro was released on 2026-09-22.
MiMo-V2.6-Pro 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-Pro is available from Xiaomi.
GLM-5.3
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3 and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3 vs MiMo-V2.6-Pro.