Model Comparison

DeepSeek R1 Zero vs Gemini 2.5 Pro

Gemini 2.5 Pro significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Zero outperforms in 0 benchmarks, while Gemini 2.5 Pro is better at 2 benchmarks (AIME 2024, GPQA).

Gemini 2.5 Pro significantly outperforms across most benchmarks.

Fri May 08 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 2.5 Pro specifies input context (1,048,576 tokens). Only Gemini 2.5 Pro specifies output context (65,536 tokens).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Google
Gemini 2.5 Pro
Input1,048,576 tokens
Output65,536 tokens
Fri May 08 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.5 Pro supports multimodal inputs, whereas DeepSeek R1 Zero does not.

Gemini 2.5 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek R1 Zero

Text
Images
Audio
Video

Gemini 2.5 Pro

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Gemini 2.5 Pro uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek R1 Zero

MIT

Open weights

Gemini 2.5 Pro

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Gemini 2.5 Pro was released on 2025-05-20.

Gemini 2.5 Pro is 4 months newer than DeepSeek R1 Zero.

DeepSeek R1 Zero

Jan 20, 2025

1.3 years ago

Gemini 2.5 Pro

May 20, 2025

11 months ago

4mo newer

Knowledge Cutoff

When training data ends

Gemini 2.5 Pro has a documented knowledge cutoff of 2025-01-31, while DeepSeek R1 Zero's cutoff date is not specified.

We can confirm Gemini 2.5 Pro's training data extends to 2025-01-31, but cannot make a direct comparison without DeepSeek R1 Zero's cutoff date.

DeepSeek R1 Zero

—

Gemini 2.5 Pro

Jan 2025

Outputs Comparison

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Key Takeaways

DeepSeek
Has open weights
Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
Higher AIME 2024 score (92.0% vs 86.7%)
Higher GPQA score (83.0% vs 73.3%)
DeepSeekDeepSeek R1 Zero
GoogleGemini 2.5 Pro
Compare in Playground

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Zero
Google
Gemini 2.5 Pro

FAQ

Common questions about DeepSeek R1 Zero vs Gemini 2.5 Pro.

Which is better, DeepSeek R1 Zero or Gemini 2.5 Pro?

Gemini 2.5 Pro significantly outperforms across most benchmarks. DeepSeek R1 Zero is made by DeepSeek and Gemini 2.5 Pro is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek R1 Zero compare to Gemini 2.5 Pro in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Gemini 2.5 Pro scores MRCR: 93.0%, AIME 2024: 92.0%, Global-MMLU-Lite: 88.6%, Video-MME: 84.8%, AIME 2025: 83.0%.

What are the context window sizes for DeepSeek R1 Zero and Gemini 2.5 Pro?

DeepSeek R1 Zero supports an unknown number of tokens and Gemini 2.5 Pro supports 1.0M 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 Zero and Gemini 2.5 Pro?

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 Zero and Gemini 2.5 Pro?

DeepSeek R1 Zero is developed by DeepSeek and Gemini 2.5 Pro is developed by Google.