Model Comparison

GLM-4.5-Air vs Qwen3 VL 32B InstructWhich is better in 2026?

GLM-4.5-Air significantly outperforms across most benchmarks.

Verdict: GLM-4.5-Air vs Qwen3 VL 32B Instruct — which is better?

GLM-4.5-Air (by Zhipu AI) and Qwen3 VL 32B 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-Air outperforms in 3 benchmarks (BFCL-v3, GPQA, MMLU-Pro), while Qwen3 VL 32B Instruct is better at 0 benchmarks. GLM-4.5-Air significantly outperforms across most benchmarks.

Choose GLM-4.5-Air if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks

Choose Qwen3 VL 32B Instruct if…

  • you want the most recent training data — it shipped Sep 2025

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

GLM-4.5-Air outperforms in 3 benchmarks (BFCL-v3, GPQA, MMLU-Pro), while Qwen3 VL 32B Instruct is better at 0 benchmarks.

GLM-4.5-Air significantly outperforms across most benchmarks.

Thu Jun 25 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

73.0B diff

GLM-4.5-Air has 73.0B more parameters than Qwen3 VL 32B Instruct, making it 221.2% larger.

Zhipu AI
GLM-4.5-Air
106.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct
33.0Bparameters
106.0B
GLM-4.5-Air
33.0B
Qwen3 VL 32B Instruct

Input Capabilities

Supported data types and modalities

Qwen3 VL 32B Instruct supports multimodal inputs, whereas GLM-4.5-Air does not.

Qwen3 VL 32B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.5-Air

Text
Images
Audio
Video

Qwen3 VL 32B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.5-Air is licensed under MIT, while Qwen3 VL 32B Instruct uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-4.5-Air

MIT

Open weights

Qwen3 VL 32B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.5-Air was released on 2025-07-28, while Qwen3 VL 32B Instruct was released on 2025-09-22.

Qwen3 VL 32B Instruct is 2 months newer than GLM-4.5-Air.

GLM-4.5-Air

Jul 28, 2025

11 months ago

Qwen3 VL 32B Instruct

Sep 22, 2025

9 months ago

1mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Higher BFCL-v3 score (76.4% vs 70.2%)
Higher GPQA score (75.0% vs 68.9%)
Higher MMLU-Pro score (81.4% vs 78.6%)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Instruct

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5-Air
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct

FAQ

Common questions about GLM-4.5-Air vs Qwen3 VL 32B Instruct.

Which is better, GLM-4.5-Air or Qwen3 VL 32B Instruct?

GLM-4.5-Air significantly outperforms across most benchmarks. GLM-4.5-Air is made by Zhipu AI and Qwen3 VL 32B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-4.5-Air compare to Qwen3 VL 32B Instruct in benchmarks?

GLM-4.5-Air scores MATH-500: 98.1%, AIME 2024: 89.4%, MMLU-Pro: 81.4%, TAU-bench Retail: 77.9%, BFCL-v3: 76.4%. Qwen3 VL 32B Instruct scores DocVQAtest: 96.9%, ScreenSpot: 95.8%, CharXiv-D: 90.5%, MMLU-Redux: 89.8%, AI2D: 89.5%.

What are the main differences between GLM-4.5-Air and Qwen3 VL 32B Instruct?

Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.5-Air and Qwen3 VL 32B Instruct?

GLM-4.5-Air is developed by Zhipu AI and Qwen3 VL 32B Instruct is developed by Alibaba Cloud / Qwen Team.