Model Comparison

GLM-5 vs Qwen3 VL 32B Thinking

Comparing GLM-5 and Qwen3 VL 32B Thinking across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and Qwen3 VL 32B Thinking don'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

Cost data unavailable.

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

711.0B diff

GLM-5 has 711.0B more parameters than Qwen3 VL 32B Thinking, making it 2154.5% larger.

Zhipu AI
GLM-5
744.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
744.0B
GLM-5
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Wed Apr 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 32B Thinking supports multimodal inputs, whereas GLM-5 does not.

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

GLM-5

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 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-5

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Qwen3 VL 32B Thinking was released on 2025-09-22.

GLM-5 is 5 months newer than Qwen3 VL 32B Thinking.

GLM-5

Feb 11, 2026

2 months ago

4mo newer
Qwen3 VL 32B Thinking

Sep 22, 2025

6 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

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Key Takeaways

Larger context window (200,000 tokens)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking

FAQ

Common questions about GLM-5 vs Qwen3 VL 32B Thinking

GLM-5 (Zhipu AI) and Qwen3 VL 32B Thinking (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.
GLM-5 supports 200K 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.
Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-5 is developed by Zhipu AI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.