DeepSeek R1 Distill Qwen 14B vs Qwen2.5-Omni-7B
DeepSeek R1 Distill Qwen 14B leads the LLM Stats Score 11.0 to 5.3.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek R1 Distill Qwen 14B leads the overall LLM Stats Score 11.0 to 5.3, ranking #249 overall.
In the 1 individual benchmarks reported for both models, DeepSeek R1 Distill Qwen 14B wins 1; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek R1 Distill Qwen 14B
- overall performance matters — it scores 11.0 and ranks #249 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
Choose Qwen2.5-Omni-7B
- you want the most recent training data — it shipped Mar 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
4 reported for DeepSeek R1 Distill Qwen 14B · 45 for Qwen2.5-Omni-7B
DeepSeek R1 Distill Qwen 14B outperforms in 1 benchmarks (GPQA), while Qwen2.5-Omni-7B is better at 0 benchmarks.
DeepSeek R1 Distill Qwen 14B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek R1 Distill Qwen 14B has 7.8B more parameters than Qwen2.5-Omni-7B, making it 111.4% larger.
Input capabilities
Documented input modalities across available providers
Qwen2.5-Omni-7B supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 14B 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 Distill Qwen 14B
Qwen2.5-Omni-7B
License
Usage and distribution terms
DeepSeek R1 Distill Qwen 14B 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 Distill Qwen 14B was released on 2025-01-20, while Qwen2.5-Omni-7B was released on 2025-03-27.
Qwen2.5-Omni-7B is 2 months newer than DeepSeek R1 Distill Qwen 14B.
Jan 20, 2025
1.6 years ago
Mar 27, 2025
1.4 years ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Distill Qwen 14B and Qwen2.5-Omni-7B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Distill Qwen 14B vs Qwen2.5-Omni-7B.