Model Comparison
GLM-4.5V vs Qwen3 VL 235B A22B InstructWhich is better in 2026?
Comparing GLM-4.5V and Qwen3 VL 235B A22B Instruct across benchmarks, pricing, and capabilities.
Verdict: GLM-4.5V vs Qwen3 VL 235B A22B Instruct — which is better?
GLM-4.5V (by Zhipu AI) and Qwen3 VL 235B A22B Instruct (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, Qwen3 VL 235B A22B Instruct is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 235B A22B Instruct 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…
- you want predictable pricing at $0.55/M input and $2.19/M output
Choose Qwen3 VL 235B A22B Instruct if…
- cost matters — it's about 1.6x cheaper per token
- 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 Instructdon'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 1.8x more expensive than Qwen3 VL 235B A22B Instruct ($0.30/1M tokens).
For output processing, GLM-4.5V ($2.19/1M tokens) is 1.5x more expensive than Qwen3 VL 235B A22B Instruct ($1.49/1M tokens).
In conclusion, GLM-4.5V is more expensive than Qwen3 VL 235B A22B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 VL 235B A22B Instruct 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 Instruct accepts 262,144 input tokens compared to GLM-4.5V's 131,072 tokens. Qwen3 VL 235B A22B Instruct 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 Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.5V
Qwen3 VL 235B A22B Instruct
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while Qwen3 VL 235B A22B Instruct 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 Instruct was released on 2025-09-22.
Qwen3 VL 235B A22B Instruct is 1 month newer than GLM-4.5V.
Aug 11, 2025
11 months ago
Sep 22, 2025
9 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 Instruct is available from DeepInfra, Novita.
GLM-4.5V
Qwen3 VL 235B A22B Instruct
Outputs Comparison
Key Takeaways
GLM-4.5V
View detailsZhipu AI
No standout differentiators in the data we have for this pair.
Qwen3 VL 235B A22B Instruct
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 Instruct side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GLM-4.5V vs Qwen3 VL 235B A22B Instruct.