Model Comparison
DeepSeek-V2.5 vs Qwen2.5-Omni-7B
DeepSeek-V2.5 significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 3 benchmarks (GSM8k, HumanEval, MATH), while Qwen2.5-Omni-7B is better at 0 benchmarks.
DeepSeek-V2.5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-V2.5 has 229.0B more parameters than Qwen2.5-Omni-7B, making it 3271.4% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).
Input Capabilities
Supported data types and modalities
Qwen2.5-Omni-7B supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Qwen2.5-Omni-7B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Qwen2.5-Omni-7B
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, 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.
deepseek
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Qwen2.5-Omni-7B was released on 2025-03-27.
Qwen2.5-Omni-7B is 11 months newer than DeepSeek-V2.5.
May 8, 2024
2.0 years ago
Mar 27, 2025
1.1 years ago
10mo newerKnowledge 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-V2.5
View detailsDeepSeek
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V2.5 vs Qwen2.5-Omni-7B