GLM-5.3-Flash vs Qwen3.8 Max
GLM-5.3-Flash shows notably better performance in the majority of benchmarks. GLM-5.3-Flash is 10.4x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3-Flash outperforms in 5 benchmarks (AutomationBench, DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Toolathlon), while Qwen3.8 Max is better at 2 benchmarks (Agents' Last Exam, Terminal-Bench 2.1). GLM-5.3-Flash shows notably better performance in the majority of benchmarks.
On price, GLM-5.3-Flash is roughly 10.4x 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 (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- you want the strongest raw capability — it leads on 5 of 7 shared benchmarks
- cost matters — it's about 10.4x cheaper per token
- 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 Qwen3.8 Max
- you want predictable pricing at $1.65/M input and $4.95/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash outperforms in 5 benchmarks (AutomationBench, DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Toolathlon), while Qwen3.8 Max is better at 2 benchmarks (Agents' Last Exam, Terminal-Bench 2.1).
GLM-5.3-Flash shows notably better performance in the majority of benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 11.0x cheaper than Qwen3.8 Max ($1.65/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 9.9x cheaper than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Qwen3.8 Max is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Max has 2080.0B more parameters than GLM-5.3-Flash, making it 650.0% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,000,000 input tokens compared to Qwen3.8 Max's 256,000 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-5.3-Flash and Qwen3.8 Max support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Qwen3.8 Max
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Qwen3.8 Max uses Qwen3.8-Max License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen3.8-Max License
Open weights
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Qwen3.8 Max was released on 2026-08-02.
GLM-5.3-Flash is 1 month newer than Qwen3.8 Max.
Aug 26, 2026
0 days ago
3w newerAug 2, 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.
Provider Availability
GLM-5.3-Flash is available from ZAI. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
GLM-5.3-Flash
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Qwen3.8 Max.