Model Comparison
DeepSeek-V3 vs QwQ-32B
QwQ-32B significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3 outperforms in 1 benchmarks (IFEval), while QwQ-32B is better at 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500).
QwQ-32B 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 has 638.5B more parameters than QwQ-32B, making it 1964.6% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while QwQ-32B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT + Model License (Commercial use allowed)
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while QwQ-32B was released on 2025-03-05.
QwQ-32B is 2 months newer than DeepSeek-V3.
Dec 25, 2024
1.3 years ago
Mar 5, 2025
1.1 years ago
2mo newerKnowledge Cutoff
When training data ends
QwQ-32B has a documented knowledge cutoff of 2024-11-28, while DeepSeek-V3'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's cutoff date.
—
Nov 2024
Outputs Comparison
Key Takeaways
DeepSeek-V3
View detailsDeepSeek
QwQ-32B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3 vs QwQ-32B