DeepSeek-V3.2 (Non-thinking) vs Phi-3.5-vision-instruct Comparison
Comparing DeepSeek-V3.2 (Non-thinking) and Phi-3.5-vision-instruct across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and Phi-3.5-vision-instruct 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 680.8B more parameters than Phi-3.5-vision-instruct, making it 16209.5% 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
Phi-3.5-vision-instruct supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
Phi-3.5-vision-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Non-thinking)
Phi-3.5-vision-instruct
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Phi-3.5-vision-instruct was released on 2024-08-23.
DeepSeek-V3.2 (Non-thinking) is 16 months newer than Phi-3.5-vision-instruct.
Dec 1, 2025
3 months ago
1.3yr newerAug 23, 2024
1.6 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
Phi-3.5-vision-instruct
View detailsMicrosoft
Detailed Comparison
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