Model Comparison
DeepSeek-R1-0528 vs Qwen2.5-Omni-7B
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1-0528 outperforms in 3 benchmarks (GPQA, MMLU-Pro, MMLU-Redux), while Qwen2.5-Omni-7B is better at 0 benchmarks.
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 664.0B more parameters than Qwen2.5-Omni-7B, making it 9485.7% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Qwen2.5-Omni-7B supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Qwen2.5-Omni-7B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Qwen2.5-Omni-7B
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while Qwen2.5-Omni-7B 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-R1-0528 was released on 2025-05-28, while Qwen2.5-Omni-7B was released on 2025-03-27.
DeepSeek-R1-0528 is 2 months newer than Qwen2.5-Omni-7B.
May 28, 2025
12 months ago
2mo newerMar 27, 2025
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-R1-0528
View detailsDeepSeek
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1-0528 vs Qwen2.5-Omni-7B.