DeepSeek-R1-0528 vs Qwen3.5-2B Comparison
Comparing DeepSeek-R1-0528 and Qwen3.5-2B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1-0528 outperforms in 3 benchmarks (GPQA, MMLU-Pro, MMLU-Redux), while Qwen3.5-2B is better at 0 benchmarks.
DeepSeek-R1-0528 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-R1-0528 has 669.0B more parameters than Qwen3.5-2B, making it 33450.0% 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
Qwen3.5-2B supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Qwen3.5-2B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Qwen3.5-2B
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while Qwen3.5-2B 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 Qwen3.5-2B was released on 2026-03-02.
Qwen3.5-2B is 9 months newer than DeepSeek-R1-0528.
May 28, 2025
9 months ago
Mar 2, 2026
1 weeks ago
9mo 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-R1-0528
View detailsDeepSeek
Qwen3.5-2B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
| Feature | | |
|---|