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
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
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
1.2 years ago
Mar 5, 2025
1.1 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
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1 vs QwQ-32B