DeepSeek-V3.1 vs QvQ-72B-Preview Comparison
Comparing DeepSeek-V3.1 and QvQ-72B-Preview across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.1 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
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-V3.1 has 597.6B more parameters than QvQ-72B-Preview, making it 814.2% 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).
Input Capabilities
Supported data types and modalities
QvQ-72B-Preview supports multimodal inputs, whereas DeepSeek-V3.1 does not.
QvQ-72B-Preview can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.1
QvQ-72B-Preview
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while QvQ-72B-Preview was released on 2024-12-25.
DeepSeek-V3.1 is 1 month newer than QvQ-72B-Preview.
Jan 10, 2025
1.2 years ago
2w newerDec 25, 2024
1.2 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-V3.1
View detailsDeepSeek
QvQ-72B-Preview
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
| Feature | | |
|---|