DeepSeek-V3.1 vs Qwen3.5-4B Comparison
Comparing DeepSeek-V3.1 and Qwen3.5-4B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.1 outperforms in 2 benchmarks (MMLU-Pro, MMLU-Redux), while Qwen3.5-4B is better at 2 benchmarks (GPQA, HMMT 2025).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-V3.1 has 667.0B more parameters than Qwen3.5-4B, making it 16675.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).
Input Capabilities
Supported data types and modalities
Qwen3.5-4B supports multimodal inputs, whereas DeepSeek-V3.1 does not.
Qwen3.5-4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.1
Qwen3.5-4B
License
Usage and distribution terms
DeepSeek-V3.1 is licensed under MIT, while Qwen3.5-4B 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-V3.1 was released on 2025-01-10, while Qwen3.5-4B was released on 2026-03-02.
Qwen3.5-4B is 14 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.2 years ago
Mar 2, 2026
1 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-V3.1
View detailsDeepSeek
Qwen3.5-4B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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