Model Comparison

GLM-4.5-Air vs Qwen3 VL 4B Thinking

GLM-4.5-Air significantly outperforms across most benchmarks.

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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 4B Thinking is better at 0 benchmarks.

GLM-4.5-Air significantly outperforms across most benchmarks.

Thu May 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

102.0B diff

GLM-4.5-Air has 102.0B more parameters than Qwen3 VL 4B Thinking, making it 2550.0% larger.

GLM-4.5-Air

106.0Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

106.0B

GLM-4.5-Air

4.0B

Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 4B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 4B Thinking specifies output context (262,144 tokens).

GLM-4.5-Air

Input- tokens

Output- tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Thu May 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 4B Thinking supports multimodal inputs, whereas GLM-4.5-Air does not.

Qwen3 VL 4B Thinking 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 4B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

GLM-4.5-Air is licensed under MIT, while Qwen3 VL 4B Thinking 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 4B Thinking

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 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 2 months newer than GLM-4.5-Air.

GLM-4.5-Air

Jul 28, 2025

9 months ago

Qwen3 VL 4B Thinking

Sep 22, 2025

7 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

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Key Takeaways

GLM-4.5-Air

View details

Zhipu AI

Higher BFCL-v3 score (76.4% vs 67.3%)

Higher GPQA score (75.0% vs 64.1%)

Higher MMLU-Pro score (81.4% vs 73.6%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature	GLM-4.5-Air	Qwen3 VL 4B Thinking

FAQ

Common questions about GLM-4.5-Air vs Qwen3 VL 4B Thinking.

Which is better, GLM-4.5-Air or Qwen3 VL 4B Thinking?

GLM-4.5-Air significantly outperforms across most benchmarks. GLM-4.5-Air is made by Zhipu AI and Qwen3 VL 4B Thinking 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 4B Thinking 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 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

What are the context window sizes for GLM-4.5-Air and Qwen3 VL 4B Thinking?

GLM-4.5-Air supports an unknown number of tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.5-Air and Qwen3 VL 4B Thinking?

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 4B Thinking?

GLM-4.5-Air is developed by Zhipu AI and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.