DeepSeek-R1 vs Qwen3.5-27B Comparison
Comparing DeepSeek-R1 and Qwen3.5-27B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Qwen3.5-27B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-R1 has 644.0B more parameters than Qwen3.5-27B, making it 2385.2% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Qwen3.5-27B supports multimodal inputs, whereas DeepSeek-R1 does not.
Qwen3.5-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Qwen3.5-27B
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Qwen3.5-27B 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 was released on 2025-01-20, while Qwen3.5-27B was released on 2026-02-24.
Qwen3.5-27B is 13 months newer than DeepSeek-R1.
Jan 20, 2025
1.1 years ago
Feb 24, 2026
2 weeks ago
1.1yr 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
View detailsDeepSeek
Qwen3.5-27B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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