Model Comparison

DeepSeek R1 Zero vs Gemini 2.0 FlashWhich is better in 2026?

DeepSeek R1 Zero significantly outperforms across most benchmarks.

Verdict: DeepSeek R1 Zero vs Gemini 2.0 Flash — which is better?

DeepSeek R1 Zero (by DeepSeek) and Gemini 2.0 Flash (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek R1 Zero outperforms in 2 benchmarks (GPQA, LiveCodeBench), while Gemini 2.0 Flash is better at 0 benchmarks. DeepSeek R1 Zero significantly outperforms across most benchmarks.

Choose DeepSeek R1 Zero if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Jan 2025
  • you need open weights you can self-host or fine-tune

Choose Gemini 2.0 Flash if…

  • you want predictable pricing at $0.10/M input and $0.40/M output
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Zero outperforms in 2 benchmarks (GPQA, LiveCodeBench), while Gemini 2.0 Flash is better at 0 benchmarks.

DeepSeek R1 Zero significantly outperforms across most benchmarks.

Tue Jun 09 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 2.0 Flash specifies input context (1,048,576 tokens). Only Gemini 2.0 Flash specifies output context (8,192 tokens).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Google
Gemini 2.0 Flash
Input1,048,576 tokens
Output8,192 tokens
Tue Jun 09 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash supports multimodal inputs, whereas DeepSeek R1 Zero does not.

Gemini 2.0 Flash 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 2.0 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Gemini 2.0 Flash 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 2.0 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Gemini 2.0 Flash was released on 2024-12-01.

DeepSeek R1 Zero is 2 months newer than Gemini 2.0 Flash.

DeepSeek R1 Zero

Jan 20, 2025

1.4 years ago

1mo newer
Gemini 2.0 Flash

Dec 1, 2024

1.5 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash has a documented knowledge cutoff of 2024-08-01, while DeepSeek R1 Zero's cutoff date is not specified.

We can confirm Gemini 2.0 Flash's training data extends to 2024-08-01, but cannot make a direct comparison without DeepSeek R1 Zero's cutoff date.

DeepSeek R1 Zero

—

Gemini 2.0 Flash

Aug 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Has open weights
Higher GPQA score (73.3% vs 62.1%)
Higher LiveCodeBench score (50.0% vs 35.1%)
Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
DeepSeekDeepSeek R1 Zero
GoogleGemini 2.0 Flash
Compare in Playground

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Zero
Google
Gemini 2.0 Flash

FAQ

Common questions about DeepSeek R1 Zero vs Gemini 2.0 Flash.

Which is better, DeepSeek R1 Zero or Gemini 2.0 Flash?

DeepSeek R1 Zero significantly outperforms across most benchmarks. DeepSeek R1 Zero is made by DeepSeek and Gemini 2.0 Flash 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 2.0 Flash in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Gemini 2.0 Flash scores Natural2Code: 92.9%, MATH: 89.7%, FACTS Grounding: 83.6%, MMLU-Pro: 76.4%, EgoSchema: 71.5%.

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

DeepSeek R1 Zero supports an unknown number of tokens and Gemini 2.0 Flash 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 2.0 Flash?

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 2.0 Flash?

DeepSeek R1 Zero is developed by DeepSeek and Gemini 2.0 Flash is developed by Google.

Related comparisons