GLM-4.6 vs Qwen3-235B-A22B-Thinking-2507
GLM-4.6 and Qwen3-235B-A22B-Thinking-2507 are closely matched at 29.0 and 28.1 on the LLM Stats Score. GLM-4.6 is 1.1x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.6 and Qwen3-235B-A22B-Thinking-2507 are closely matched on the overall LLM Stats Score at 29.0 and 28.1.
The models split the 4 individual benchmarks reported for both models evenly.
On price, GLM-4.6 is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Thinking-2507 also accepts a larger context window (262,144 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-4.6
- cost matters — it's about 1.1x cheaper per token
- you want the most recent training data — it shipped Sep 2025
Choose Qwen3-235B-A22B-Thinking-2507
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GLM-4.6 · 25 for Qwen3-235B-A22B-Thinking-2507
GLM-4.6 outperforms in 2 benchmarks (AIME 2025, LiveCodeBench v6), while Qwen3-235B-A22B-Thinking-2507 is better at 2 benchmarks (GPQA, Humanity's Last Exam).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.50/1M tokens) is 1.7x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 1.5x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than GLM-4.6.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.6 has 122.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 51.9% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to GLM-4.6's 202,752 tokens. GLM-4.6 can generate longer responses up to 202,752 tokens, while Qwen3-235B-A22B-Thinking-2507 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.6 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.
GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.6
Qwen3-235B-A22B-Thinking-2507
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.6 was released on 2025-09-30, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
GLM-4.6 is 2 months newer than Qwen3-235B-A22B-Thinking-2507.
Sep 30, 2025
11 months ago
2mo newerJul 25, 2025
1.2 years 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-4.6 is available from DeepInfra, Fireworks. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
GLM-4.6
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Qwen3-235B-A22B-Thinking-2507.