Model Comparison
GLM-4.6 vs Qwen3-235B-A22B-Instruct-2507Which is better in 2026?
GLM-4.6 significantly outperforms across most benchmarks. Qwen3-235B-A22B-Instruct-2507 is 2.9x cheaper per token.
Verdict: GLM-4.6 vs Qwen3-235B-A22B-Instruct-2507 — which is better?
GLM-4.6 (by Zhipu AI) and Qwen3-235B-A22B-Instruct-2507 (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
GLM-4.6 outperforms in 3 benchmarks (AIME 2025, GPQA, LiveCodeBench v6), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks. GLM-4.6 significantly outperforms across most benchmarks.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Instruct-2507 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-4.6 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you want the most recent training data — it shipped Sep 2025
Choose Qwen3-235B-A22B-Instruct-2507 if…
- cost matters — it's about 2.9x cheaper per token
- you process long inputs — it offers a 262,144 token context window
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.6 outperforms in 3 benchmarks (AIME 2025, GPQA, LiveCodeBench v6), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks.
GLM-4.6 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.55/1M tokens) is 3.7x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.15/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 2.5x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).
In conclusion, GLM-4.6 is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* 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-Instruct-2507, making it 51.9% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Instruct-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-Instruct-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-Instruct-2507
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Qwen3-235B-A22B-Instruct-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-Instruct-2507 was released on 2025-07-22.
GLM-4.6 is 2 months newer than Qwen3-235B-A22B-Instruct-2507.
Sep 30, 2025
8 months ago
2mo newerJul 22, 2025
10 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-Instruct-2507 is available from Fireworks, Novita.
GLM-4.6
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Key Takeaways
GLM-4.6
View detailsZhipu AI
Qwen3-235B-A22B-Instruct-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
| Feature | | |
|---|
FAQ
Common questions about GLM-4.6 vs Qwen3-235B-A22B-Instruct-2507.