GLM-5.3-Flash vs Qwen3.8 Flash
GLM-5.3-Flash and Qwen3.8 Flash are closely matched at 51.1 and 49.6 on the LLM Stats Score. Qwen3.8 Flash is 1.0x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3-Flash and Qwen3.8 Flash are closely matched on the overall LLM Stats Score at 51.1 and 49.6.
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.
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-Flash
- you value its reported benchmark strengths — it wins 4 of 6 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Qwen3.8 Flash
- you want predictable pricing at $0.15/M input and $0.47/M output
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
GLM-5.3-Flash outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Toolathlon), while Qwen3.8 Flash 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
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) costs the same as Qwen3.8 Flash ($0.15/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 1.1x more expensive than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, GLM-5.3-Flash is more expensive than Qwen3.8 Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 195.0B more parameters than Qwen3.8 Flash, making it 156.0% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Qwen3.8 Flash's 1,000,000 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Qwen3.8 Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Qwen3.8 Flash
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Qwen3.8 Flash uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
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
4 days ago
Aug 26, 2026
4 days 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-Flash is available from DeepInfra, Novita, ZAI. Qwen3.8 Flash is available from Novita.
GLM-5.3-Flash
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Qwen3.8 Flash.