DeepSeek-V3.2 (Thinking) vs GLM-4.5-Air Comparison
Comparing DeepSeek-V3.2 (Thinking) and GLM-4.5-Air across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) outperforms in 6 benchmarks (BrowseComp, GPQA, Humanity's Last Exam, LiveCodeBench, MMLU-Pro, SWE-Bench Verified), while GLM-4.5-Air is better at 0 benchmarks.
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-V3.2 (Thinking) has 579.0B more parameters than GLM-4.5-Air, making it 546.2% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while GLM-4.5-Air was released on 2025-07-28.
DeepSeek-V3.2 (Thinking) is 4 months newer than GLM-4.5-Air.
Dec 1, 2025
3 months ago
4mo newerJul 28, 2025
7 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
GLM-4.5-Air
View detailsZhipu AI
Detailed Comparison
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