Model Comparison
DeepSeek-V3.1 vs QwQ-32B
QwQ-32B shows notably better performance in the majority of benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.1 outperforms in 1 benchmarks (GPQA), while QwQ-32B is better at 2 benchmarks (AIME 2024, LiveCodeBench).
QwQ-32B shows notably better performance in the majority of benchmarks.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
DeepSeek-V3.1 has 638.5B more parameters than QwQ-32B, making it 1964.6% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while QwQ-32B was released on 2025-03-05.
QwQ-32B is 2 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.4 years ago
Mar 5, 2025
1.2 years ago
1mo newerKnowledge Cutoff
When training data ends
QwQ-32B has a documented knowledge cutoff of 2024-11-28, while DeepSeek-V3.1'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-V3.1's cutoff date.
—
Nov 2024
Outputs Comparison
Key Takeaways
DeepSeek-V3.1
View detailsDeepSeek
QwQ-32B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.1 vs QwQ-32B.