Model Comparison

GLM-4.7 vs Qwen3 VL 32B ThinkingWhich is better in 2026?

GLM-4.7 significantly outperforms across most benchmarks.

Verdict: GLM-4.7 vs Qwen3 VL 32B Thinking — which is better?

GLM-4.7 (by Zhipu AI) and Qwen3 VL 32B 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.

GLM-4.7 outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 0 benchmarks. GLM-4.7 significantly outperforms across most benchmarks.

Choose GLM-4.7 if…

  • you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
  • you want the most recent training data — it shipped Dec 2025

Choose Qwen3 VL 32B Thinking if…

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

GLM-4.7 outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 0 benchmarks.

GLM-4.7 significantly outperforms across most benchmarks.

Wed Jul 22 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

325.0B diff

GLM-4.7 has 325.0B more parameters than Qwen3 VL 32B Thinking, making it 984.8% larger.

Zhipu AI
GLM-4.7
358.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
358.0B
GLM-4.7
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only GLM-4.7 specifies input context (202,800 tokens). Only GLM-4.7 specifies output context (131,072 tokens).

Zhipu AI
GLM-4.7
Input202,800 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Wed Jul 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-4.7 and Qwen3 VL 32B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GLM-4.7

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.7 is licensed under MIT, while Qwen3 VL 32B Thinking uses Apache 2.0.

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

GLM-4.7

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.7 was released on 2025-12-22, while Qwen3 VL 32B Thinking was released on 2025-09-22.

GLM-4.7 is 3 months newer than Qwen3 VL 32B Thinking.

GLM-4.7

Dec 22, 2025

7 months ago

3mo newer
Qwen3 VL 32B Thinking

Sep 22, 2025

10 months ago

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

Zhipu AI
Larger context window (202,800 tokens)
Higher AIME 2025 score (95.7% vs 83.7%)
Higher GPQA score (85.7% vs 73.1%)
Higher LiveCodeBench v6 score (84.9% vs 65.6%)
Higher MMLU-Pro score (84.3% vs 82.1%)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

No standout differentiators in the data we have for this pair.

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against GLM-4.7 and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

GLM-4.7
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground
AI Model Comparison Table
Feature
Zhipu AI
GLM-4.7
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking

FAQ

Common questions about GLM-4.7 vs Qwen3 VL 32B Thinking.

Which is better, GLM-4.7 or Qwen3 VL 32B Thinking?

GLM-4.7 significantly outperforms across most benchmarks. GLM-4.7 is made by Zhipu AI and Qwen3 VL 32B 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-4.7 compare to Qwen3 VL 32B Thinking in benchmarks?

GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for GLM-4.7 and Qwen3 VL 32B Thinking?

GLM-4.7 supports 203K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.7 and Qwen3 VL 32B Thinking?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.7 and Qwen3 VL 32B Thinking?

GLM-4.7 is developed by Zhipu AI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

Related comparisons