GLM-5.3 vs MiniMax M3
GLM-5.3 leads the LLM Stats Score 53.6 to 41.4. MiniMax M3 is 4.1x cheaper per token.
Zhipu AI · MiniMax · Updated for 2026
Which is better?
GLM-5.3 leads the overall LLM Stats Score 53.6 to 41.4, ranking #9 overall.
In the 3 individual benchmarks reported for both models, GLM-5.3 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, MiniMax M3 is roughly 4.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3 also accepts a larger context window (1,000,000 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-5.3
- overall performance matters — it scores 53.6 and ranks #9 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Aug 2026
Choose MiniMax M3
- cost matters — it's about 4.1x cheaper per token
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 · 35 for MiniMax M3
GLM-5.3 outperforms in 3 benchmarks (NL2Repo, PostTrainBench, Terminal-Bench 2.1), while MiniMax M3 is better at 0 benchmarks.
GLM-5.3 significantly outperforms across most 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 ($1.40/1M tokens) is 4.7x more expensive than MiniMax M3 ($0.30/1M tokens).
For output processing, GLM-5.3 ($4.40/1M tokens) is 3.7x more expensive than MiniMax M3 ($1.20/1M tokens).
In conclusion, GLM-5.3 is more expensive than MiniMax M3.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3 has 325.0B more parameters than MiniMax M3, making it 75.9% larger.
Context Window
Maximum input and output token capacity
GLM-5.3 accepts 1,000,000 input tokens compared to MiniMax M3's 512,000 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
MiniMax M3 supports multimodal inputs, whereas GLM-5.3 does not.
MiniMax M3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3
MiniMax M3
License
Usage and distribution terms
GLM-5.3 is licensed under GLM-5.3 License, while MiniMax M3 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 MiniMax M3 was released on 2026-06-01.
GLM-5.3 is 2 months newer than MiniMax M3.
Aug 14, 2026
3 weeks ago
2mo newerJun 1, 2026
3 months ago
Knowledge 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 FriendliAI, Novita, ZAI. MiniMax M3 is available from Fireworks, MiniMax, Novita, Together.
GLM-5.3
MiniMax M3
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3 and MiniMax M3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3 vs MiniMax M3.