Model Comparison
GLM-4.5 vs Qwen3 VL 30B A3B InstructWhich is better in 2026?
GLM-4.5 significantly outperforms across most benchmarks. Qwen3 VL 30B A3B Instruct is 2.2x cheaper per token.
Verdict: GLM-4.5 vs Qwen3 VL 30B A3B Instruct — which is better?
GLM-4.5 (by Zhipu AI) and Qwen3 VL 30B A3B 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.
GLM-4.5 outperforms in 3 benchmarks (BFCL-v3, GPQA, MMLU-Pro), while Qwen3 VL 30B A3B Instruct is better at 0 benchmarks. GLM-4.5 significantly outperforms across most benchmarks.
On price, Qwen3 VL 30B A3B Instruct is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose GLM-4.5 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
Choose Qwen3 VL 30B A3B Instruct if…
- cost matters — it's about 2.2x cheaper per token
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5 outperforms in 3 benchmarks (BFCL-v3, GPQA, MMLU-Pro), while Qwen3 VL 30B A3B Instruct is better at 0 benchmarks.
GLM-4.5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.5 ($0.40/1M tokens) is 2.0x more expensive than Qwen3 VL 30B A3B Instruct ($0.20/1M tokens).
For output processing, GLM-4.5 ($1.60/1M tokens) is 2.3x more expensive than Qwen3 VL 30B A3B Instruct ($0.70/1M tokens).
In conclusion, GLM-4.5 is more expensive than Qwen3 VL 30B A3B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.5 has 324.0B more parameters than Qwen3 VL 30B A3B Instruct, making it 1045.2% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 131,072 tokens. GLM-4.5 can generate longer responses up to 131,072 tokens, while Qwen3 VL 30B A3B Instruct is limited to 32,768 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 30B A3B Instruct supports multimodal inputs, whereas GLM-4.5 does not.
Qwen3 VL 30B A3B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.5
Qwen3 VL 30B A3B Instruct
License
Usage and distribution terms
GLM-4.5 is licensed under MIT, while Qwen3 VL 30B A3B 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-4.5 was released on 2025-07-28, while Qwen3 VL 30B A3B Instruct was released on 2025-09-22.
Qwen3 VL 30B A3B Instruct is 2 months newer than GLM-4.5.
Jul 28, 2025
10 months ago
Sep 22, 2025
8 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-4.5 is available from DeepInfra, Fireworks, Novita. Qwen3 VL 30B A3B Instruct is available from Novita, DeepInfra.
GLM-4.5
Qwen3 VL 30B A3B Instruct
Outputs Comparison
Key Takeaways
GLM-4.5
View detailsZhipu AI
Qwen3 VL 30B A3B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.5 vs Qwen3 VL 30B A3B Instruct.