Model Comparison
DeepSeek-R1 vs QwQ-32B
Comparing DeepSeek-R1 and QwQ-32B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and QwQ-32B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
DeepSeek-R1 has 638.5B more parameters than QwQ-32B, making it 1964.6% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).
License
Usage and distribution terms
DeepSeek-R1 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-R1 was released on 2025-01-20, while QwQ-32B was released on 2025-03-05.
QwQ-32B is 1 month newer than DeepSeek-R1.
Jan 20, 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-R1'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-R1's cutoff date.
—
Nov 2024
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
QwQ-32B
View detailsAlibaba Cloud / Qwen Team
No standout differentiators in the data we have for this pair.
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1 vs QwQ-32B.