Model Comparison

GLM-4.5 vs QwQ-32B

GLM-4.5 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

GLM-4.5 outperforms in 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500), while QwQ-32B is better at 0 benchmarks.

GLM-4.5 significantly outperforms across most benchmarks.

Tue Apr 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 21 2026 • llm-stats.com
Zhipu AI
GLM-4.5
Input tokens$0.40
Output tokens$1.60
Best providerDeepinfra
Alibaba Cloud / Qwen Team
QwQ-32B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

322.5B diff

GLM-4.5 has 322.5B more parameters than QwQ-32B, making it 992.3% larger.

Zhipu AI
GLM-4.5
355.0Bparameters
Alibaba Cloud / Qwen Team
QwQ-32B
32.5Bparameters
355.0B
GLM-4.5
32.5B
QwQ-32B

Context Window

Maximum input and output token capacity

Only GLM-4.5 specifies input context (131,072 tokens). Only GLM-4.5 specifies output context (131,072 tokens).

Zhipu AI
GLM-4.5
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
QwQ-32B
Input- tokens
Output- tokens
Tue Apr 21 2026 • llm-stats.com

License

Usage and distribution terms

GLM-4.5 is licensed under MIT, while QwQ-32B uses Apache 2.0.

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

GLM-4.5

MIT

Open weights

QwQ-32B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.5 was released on 2025-07-28, while QwQ-32B was released on 2025-03-05.

GLM-4.5 is 5 months newer than QwQ-32B.

GLM-4.5

Jul 28, 2025

8 months ago

4mo newer
QwQ-32B

Mar 5, 2025

1.1 years ago

Knowledge Cutoff

When training data ends

QwQ-32B has a documented knowledge cutoff of 2024-11-28, while GLM-4.5's cutoff date is not specified.

We can confirm QwQ-32B's training data extends to 2024-11-28, but cannot make a direct comparison without GLM-4.5's cutoff date.

GLM-4.5

—

QwQ-32B

Nov 2024

Outputs Comparison

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Key Takeaways

Zhipu AI
Larger context window (131,072 tokens)
Higher AIME 2024 score (91.0% vs 79.5%)
Higher GPQA score (79.1% vs 65.2%)
Higher LiveCodeBench score (72.9% vs 63.4%)
Higher MATH-500 score (98.2% vs 90.6%)
Alibaba Cloud / Qwen Team

QwQ-32B

View details

Alibaba Cloud / Qwen Team

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5
Alibaba Cloud / Qwen Team
QwQ-32B

FAQ

Common questions about GLM-4.5 vs QwQ-32B

GLM-4.5 significantly outperforms across most benchmarks. GLM-4.5 is made by Zhipu AI and QwQ-32B is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GLM-4.5 scores MATH-500: 98.2%, AIME 2024: 91.0%, MMLU-Pro: 84.6%, TAU-bench Retail: 79.7%, GPQA: 79.1%. QwQ-32B scores MATH-500: 90.6%, IFEval: 83.9%, AIME 2024: 79.5%, LiveBench: 73.1%, BFCL: 66.4%.
GLM-4.5 supports 131K tokens and QwQ-32B 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 licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-4.5 is developed by Zhipu AI and QwQ-32B is developed by Alibaba Cloud / Qwen Team.