Model Comparison

DeepSeek-R1 vs Gemini 2.0 Flash Thinking

Comparing DeepSeek-R1 and Gemini 2.0 Flash Thinking across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Gemini 2.0 Flash Thinking don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Fri May 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-R1 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-R1

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1 is licensed under MIT, 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-R1

MIT

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

Gemini 2.0 Flash Thinking is 0 month newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.3 years ago

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.3 years ago

1d newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-R1'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-R1's cutoff date.

DeepSeek-R1

—

Gemini 2.0 Flash Thinking

Aug 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Has open weights
Google
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
Google
Gemini 2.0 Flash Thinking

FAQ

Common questions about DeepSeek-R1 vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-R1 or Gemini 2.0 Flash Thinking?

DeepSeek-R1 (DeepSeek) and Gemini 2.0 Flash Thinking (Google) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-R1 compare to Gemini 2.0 Flash Thinking in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for DeepSeek-R1 and Gemini 2.0 Flash Thinking?

DeepSeek-R1 supports 131K 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-R1 and Gemini 2.0 Flash Thinking?

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

Who makes DeepSeek-R1 and Gemini 2.0 Flash Thinking?

DeepSeek-R1 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.