Model Comparison
GLM-4.5V vs Qwen3 VL 235B A22B ThinkingWhich is better in 2026?
Comparing GLM-4.5V and Qwen3 VL 235B A22B Thinking across benchmarks, pricing, and capabilities.
Verdict: GLM-4.5V vs Qwen3 VL 235B A22B Thinking — which is better?
GLM-4.5V (by Zhipu AI) and Qwen3 VL 235B A22B Thinking (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.
On price, GLM-4.5V is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 235B A22B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-4.5V if…
- cost matters — it's about 1.3x cheaper per token
Choose Qwen3 VL 235B A22B Thinking if…
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5V and Qwen3 VL 235B A22B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.5V ($0.55/1M tokens) is 1.2x more expensive than Qwen3 VL 235B A22B Thinking ($0.45/1M tokens).
For output processing, GLM-4.5V ($2.19/1M tokens) is 1.6x cheaper than Qwen3 VL 235B A22B Thinking ($3.49/1M tokens).
In conclusion, Qwen3 VL 235B A22B Thinking is more expensive than GLM-4.5V.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 VL 235B A22B Thinking has 128.0B more parameters than GLM-4.5V, making it 118.5% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 235B A22B Thinking accepts 262,144 input tokens compared to GLM-4.5V's 131,072 tokens. Qwen3 VL 235B A22B Thinking can generate longer responses up to 262,144 tokens, while GLM-4.5V is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.5V and Qwen3 VL 235B A22B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.5V
Qwen3 VL 235B A22B Thinking
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while Qwen3 VL 235B A22B Thinking 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-4.5V was released on 2025-08-11, while Qwen3 VL 235B A22B Thinking was released on 2025-09-22.
Qwen3 VL 235B A22B Thinking is 1 month newer than GLM-4.5V.
Aug 11, 2025
11 months ago
Sep 22, 2025
10 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-4.5V is available from Fireworks, Novita. Qwen3 VL 235B A22B Thinking is available from DeepInfra, Novita.
GLM-4.5V
Qwen3 VL 235B A22B Thinking
Outputs Comparison
Key Takeaways
GLM-4.5V
View detailsZhipu AI
Qwen3 VL 235B A22B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GLM-4.5V and Qwen3 VL 235B A22B Thinking side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about GLM-4.5V vs Qwen3 VL 235B A22B Thinking.