Model Comparison
DeepSeek-R1 vs QvQ-72B-Preview
Comparing DeepSeek-R1 and QvQ-72B-Preview across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and QvQ-72B-Preview 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 597.6B more parameters than QvQ-72B-Preview, making it 814.2% 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).
Input Capabilities
Supported data types and modalities
QvQ-72B-Preview supports multimodal inputs, whereas DeepSeek-R1 does not.
QvQ-72B-Preview can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
QvQ-72B-Preview
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while QvQ-72B-Preview uses Qwen.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while QvQ-72B-Preview was released on 2024-12-25.
DeepSeek-R1 is 1 month newer than QvQ-72B-Preview.
Jan 20, 2025
1.3 years ago
3w newerDec 25, 2024
1.4 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
QvQ-72B-Preview
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1 vs QvQ-72B-Preview.