Model Comparison

DeepSeek-R1-0528 vs Gemini 2.0 Flash Thinking

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-R1-0528 outperforms in 2 benchmarks (AIME 2024, GPQA), while Gemini 2.0 Flash Thinking is better at 0 benchmarks.

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Wed Apr 22 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 22 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
Google
Gemini 2.0 Flash Thinking
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Wed Apr 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-R1-0528 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-R1-0528

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1-0528 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-R1-0528

MIT

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

DeepSeek-R1-0528 is 4 months newer than Gemini 2.0 Flash Thinking.

DeepSeek-R1-0528

May 28, 2025

10 months ago

4mo newer
Gemini 2.0 Flash Thinking

Jan 21, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-R1-0528'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-R1-0528's cutoff date.

DeepSeek-R1-0528

—

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
Higher AIME 2024 score (91.4% vs 73.3%)
Higher GPQA score (81.0% vs 74.2%)
Google
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Google
Gemini 2.0 Flash Thinking

FAQ

Common questions about DeepSeek-R1-0528 vs Gemini 2.0 Flash Thinking

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 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.
DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.
DeepSeek-R1-0528 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.
Key differences include multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1-0528 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.