GLM-5.2 vs Qwen3.8 Max
Qwen3.8 Max leads the LLM Stats Score 52.0 to 45.6. GLM-5.2 is 2.1x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Max leads the overall LLM Stats Score 52.0 to 45.6, ranking #11 overall.
In the 8 individual benchmarks reported for both models, Qwen3.8 Max wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.2 is roughly 2.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.2 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-5.2
- cost matters — it's about 2.1x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose Qwen3.8 Max
- overall performance matters — it scores 52.0 and ranks #11 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 6 of 8 exact shared results
- 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
19 reported for GLM-5.2 · 42 for Qwen3.8 Max
GLM-5.2 outperforms in 2 benchmarks (FrontierSWE, Humanity's Last Exam), while Qwen3.8 Max is better at 6 benchmarks (DeepSWE 1.1, GPQA, NL2Repo, SWE-Bench Pro, Terminal-Bench 2.1, Toolathlon).
Qwen3.8 Max 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-5.2 ($0.75/1M tokens) is 2.2x cheaper than Qwen3.8 Max ($1.65/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 2.1x cheaper than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Qwen3.8 Max is more expensive than GLM-5.2.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Max has 1647.0B more parameters than GLM-5.2, making it 218.7% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Qwen3.8 Max's 256,000 tokens. GLM-5.2 can generate longer responses up to 1,048,576 tokens, while Qwen3.8 Max is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Max supports multimodal inputs, whereas GLM-5.2 does not.
Qwen3.8 Max can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Qwen3.8 Max
License
Usage and distribution terms
GLM-5.2 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-5.2 was released on 2026-06-16, while Qwen3.8 Max was released on 2026-08-02.
Qwen3.8 Max is 2 months newer than GLM-5.2.
Jun 16, 2026
3 months ago
Aug 2, 2026
1 months ago
1mo 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-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
GLM-5.2
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Qwen3.8 Max.