GLM-5.3-Flash vs Qwen3.8-Flash-Next
GLM-5.3-Flash and Qwen3.8-Flash-Next are closely matched at 50.2 and 49.1 on the LLM Stats Score.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3-Flash and Qwen3.8-Flash-Next are closely matched on the overall LLM Stats Score at 50.2 and 49.1.
In the 6 individual benchmarks reported for both models, GLM-5.3-Flash wins 4; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- you value its reported benchmark strengths — it wins 4 of 6 exact shared results
Choose Qwen3.8-Flash-Next
- you are already invested in the Alibaba Cloud / Qwen Team ecosystem
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for GLM-5.3-Flash · 22 for Qwen3.8-Flash-Next
GLM-5.3-Flash outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Toolathlon), while Qwen3.8-Flash-Next is better at 2 benchmarks (Agents' Last Exam, CharXiv-R).
GLM-5.3-Flash shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
GLM-5.3-Flash has 195.0B more parameters than Qwen3.8-Flash-Next, making it 156.0% larger.
Context Window
Maximum input and output token capacity
Only GLM-5.3-Flash specifies input context (1,048,576 tokens). Only GLM-5.3-Flash specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Qwen3.8-Flash-Next support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Qwen3.8-Flash-Next
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Qwen3.8-Flash-Next uses Qwen Community License 1.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen Community License 1.0
Open weights
Release Timeline
When each model was launched
Both models were released on 2026-08-26.
They likely represent similar generations of model development.
Aug 26, 2026
3 weeks ago
Aug 26, 2026
3 weeks ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Qwen3.8-Flash-Next.