DeepSeek-V3 vs Gemini 2.0 Flash Thinking Comparison
Comparing DeepSeek-V3 and Gemini 2.0 Flash Thinking across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3 outperforms in 0 benchmarks, while Gemini 2.0 Flash Thinking is better at 2 benchmarks (AIME 2024, GPQA).
Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-V3 does not.
Gemini 2.0 Flash Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3
Gemini 2.0 Flash Thinking
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Gemini 2.0 Flash Thinking uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT + Model License (Commercial use allowed)
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while Gemini 2.0 Flash Thinking was released on 2025-01-21.
Gemini 2.0 Flash Thinking is 1 month newer than DeepSeek-V3.
Dec 25, 2024
1.2 years ago
Jan 21, 2025
1.2 years ago
3w newerKnowledge Cutoff
When training data ends
Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-V3's cutoff date is not specified.
We can confirm Gemini 2.0 Flash Thinking's training data extends to 2024-08-01, but cannot make a direct comparison without DeepSeek-V3's cutoff date.
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Aug 2024
Outputs Comparison
Key Takeaways
DeepSeek-V3
View detailsDeepSeek
Detailed Comparison
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