DeepSeek R1 Distill Qwen 1.5B vs Qwen2.5 VL 32B Instruct
Qwen2.5 VL 32B Instruct leads the LLM Stats Score 9.9 to -2.8.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen2.5 VL 32B Instruct leads the overall LLM Stats Score 9.9 to -2.8, ranking #257 overall.
In the 1 individual benchmarks reported for both models, Qwen2.5 VL 32B Instruct 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 1.5B
- you are already invested in the DeepSeek ecosystem
Choose Qwen2.5 VL 32B Instruct
- overall performance matters — it scores 9.9 and ranks #257 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Feb 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 1.5B · 28 for Qwen2.5 VL 32B Instruct
DeepSeek R1 Distill Qwen 1.5B outperforms in 0 benchmarks, while Qwen2.5 VL 32B Instruct is better at 1 benchmark (GPQA).
Qwen2.5 VL 32B Instruct significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
Qwen2.5 VL 32B Instruct has 31.7B more parameters than DeepSeek R1 Distill Qwen 1.5B, making it 1782.0% larger.
Input capabilities
Documented input modalities across available providers
Qwen2.5 VL 32B Instruct supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 1.5B does not.
Qwen2.5 VL 32B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek R1 Distill Qwen 1.5B
Qwen2.5 VL 32B Instruct
License
Usage and distribution terms
DeepSeek R1 Distill Qwen 1.5B is licensed under MIT, while Qwen2.5 VL 32B Instruct 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 1.5B was released on 2025-01-20, while Qwen2.5 VL 32B Instruct was released on 2025-02-28.
Qwen2.5 VL 32B Instruct is 1 month newer than DeepSeek R1 Distill Qwen 1.5B.
Jan 20, 2025
1.6 years ago
Feb 28, 2025
1.5 years ago
1mo 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 1.5B and Qwen2.5 VL 32B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Distill Qwen 1.5B vs Qwen2.5 VL 32B Instruct.