DeepSeek-V3.2 (Non-thinking) vs DeepSeek VL2 Tiny Comparison
Comparing DeepSeek-V3.2 (Non-thinking) and DeepSeek VL2 Tiny across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and DeepSeek VL2 Tiny 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-V3.2 (Non-thinking) has 682.0B more parameters than DeepSeek VL2 Tiny, making it 22733.3% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).
Input Capabilities
Supported data types and modalities
DeepSeek VL2 Tiny supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
DeepSeek VL2 Tiny can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Non-thinking)
DeepSeek VL2 Tiny
License
Usage and distribution terms
DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while DeepSeek VL2 Tiny uses deepseek.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
deepseek
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while DeepSeek VL2 Tiny was released on 2024-12-13.
DeepSeek-V3.2 (Non-thinking) is 12 months newer than DeepSeek VL2 Tiny.
Dec 1, 2025
3 months ago
11mo newerDec 13, 2024
1.3 years ago
Knowledge 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 VL2 Tiny
View detailsDeepSeek
Detailed Comparison
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