Model Comparison

DeepSeek-V3 0324 vs Gemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3 0324 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.

Wed May 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 0324 specifies input context (163,840 tokens). Only DeepSeek-V3 0324 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3 0324
Input163,840 tokens
Output163,840 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Wed May 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-V3 0324 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 0324

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3 0324 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.

DeepSeek-V3 0324

MIT + Model License (Commercial use allowed)

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3 0324 was released on 2025-03-25, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

DeepSeek-V3 0324 is 2 months newer than Gemini 2.0 Flash Thinking.

DeepSeek-V3 0324

Mar 25, 2025

1.1 years ago

2mo newer
Gemini 2.0 Flash Thinking

Jan 21, 2025

1.3 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-V3 0324'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 0324's cutoff date.

DeepSeek-V3 0324

—

Gemini 2.0 Flash Thinking

Aug 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Has open weights
Google
Supports multimodal inputs
Higher AIME 2024 score (73.3% vs 59.4%)
Higher GPQA score (74.2% vs 68.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3 0324
Google
Gemini 2.0 Flash Thinking

FAQ

Common questions about DeepSeek-V3 0324 vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-V3 0324 or Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. DeepSeek-V3 0324 is made by DeepSeek and Gemini 2.0 Flash Thinking is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3 0324 compare to Gemini 2.0 Flash Thinking in benchmarks?

DeepSeek-V3 0324 scores MATH-500: 94.0%, MMLU-Pro: 81.2%, GPQA: 68.4%, AIME 2024: 59.4%, LiveCodeBench: 49.2%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for DeepSeek-V3 0324 and Gemini 2.0 Flash Thinking?

DeepSeek-V3 0324 supports 164K tokens and Gemini 2.0 Flash Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3 0324 and Gemini 2.0 Flash Thinking?

Key differences include multimodal support (no vs yes), licensing (MIT + Model License (Commercial use allowed) vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 0324 and Gemini 2.0 Flash Thinking?

DeepSeek-V3 0324 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.