GLM-4.6 vs Qwen3-235B-A22B-Thinking-2507 Comparison
Comparing GLM-4.6 and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
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.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.55/1M tokens) is 1.8x 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 131,072 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
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
5 months ago
2mo newerJul 25, 2025
7 months 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 Fireworks, DeepInfra. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita. The availability of providers can affect quality of the model and reliability.
GLM-4.6
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Key Takeaways
GLM-4.6
View detailsZhipu AI
Qwen3-235B-A22B-Thinking-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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