Model Comparison
DeepSeek-V3.2-Exp vs QwQ-32B
DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Exp outperforms in 2 benchmarks (GPQA, LiveCodeBench), while QwQ-32B is better at 0 benchmarks.
DeepSeek-V3.2-Exp 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-Exp has 652.5B more parameters than QwQ-32B, making it 2007.7% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2-Exp specifies input context (163,840 tokens). Only DeepSeek-V3.2-Exp specifies output context (65,536 tokens).
License
Usage and distribution terms
DeepSeek-V3.2-Exp 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.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2-Exp was released on 2025-09-29, while QwQ-32B was released on 2025-03-05.
DeepSeek-V3.2-Exp is 7 months newer than QwQ-32B.
Sep 29, 2025
6 months ago
6mo newerMar 5, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
QwQ-32B has a documented knowledge cutoff of 2024-11-28, while DeepSeek-V3.2-Exp'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 DeepSeek-V3.2-Exp's cutoff date.
—
Nov 2024
Outputs Comparison
Key Takeaways
DeepSeek-V3.2-Exp
View detailsDeepSeek
QwQ-32B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2-Exp vs QwQ-32B