GLM-5.3 vs GLM-5.3-Flash
GLM-5.3 and GLM-5.3-Flash are closely matched at 53.6 and 50.7 on the LLM Stats Score. GLM-5.3-Flash is 9.1x cheaper per token.
Zhipu AI · Zhipu AI · Updated for 2026
Which is better?
GLM-5.3 and GLM-5.3-Flash are closely matched on the overall LLM Stats Score at 53.6 and 50.7.
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, GLM-5.3-Flash is roughly 9.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-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-5.3
- you value its reported benchmark strengths — it wins 5 of 8 exact shared results
Choose GLM-5.3-Flash
- cost matters — it's about 9.1x 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 Aug 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 · 15 for GLM-5.3-Flash
GLM-5.3 outperforms in 5 benchmarks (Agents' Last Exam, DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while GLM-5.3-Flash is better at 3 benchmarks (AutomationBench, GDPval-AA, Toolathlon).
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 ($1.40/1M tokens) is 9.3x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, GLM-5.3 ($4.40/1M tokens) is 8.8x more expensive than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, GLM-5.3 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3 has 433.0B more parameters than GLM-5.3-Flash, making it 135.3% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to GLM-5.3's 1,000,000 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-5.3-Flash supports multimodal inputs, whereas GLM-5.3 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3
GLM-5.3-Flash
License
Usage and distribution terms
GLM-5.3 is licensed under GLM-5.3 License, while GLM-5.3-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 GLM-5.3-Flash was released on 2026-08-26.
GLM-5.3-Flash is 0 month newer than GLM-5.3.
Aug 14, 2026
3 weeks ago
Aug 26, 2026
1 weeks ago
1w 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 FriendliAI, Novita, ZAI. GLM-5.3-Flash is available from DeepInfra, FriendliAI, Novita, ZAI.
GLM-5.3
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3 vs GLM-5.3-Flash.