The AI arena is free today

Open Superagent

DeepSeek R1 Zero vs Gemini 2.5 Flash

DeepSeek R1 Zero and Gemini 2.5 Flash are closely matched at 16.2 and 22.1 on the LLM Stats Score.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek R1 Zero and Gemini 2.5 Flash are closely matched on the overall LLM Stats Score at 16.2 and 22.1.

In the 2 individual benchmarks reported for both models, Gemini 2.5 Flash wins 2; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek R1 Zero

  • you need open weights you can self-host or fine-tune

Choose Gemini 2.5 Flash

  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results
  • you want the most recent training data — it shipped May 2025

At a glance

The differences that matter most.

Core performance indexes
16.2
#205
22.1
#165
16.5
#197
21.7
#162
4.3
#194
8.5
#167
Cost, coverage & limits
Benchmark wins
0 of 2
2 of 2
Input price
— / M
$0.30 / M
Output price
— / M
$2.50 / M
Context window
1,048,576

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek R1 Zero
Gemini 2.5 Flash
17.7#180
16.4#194
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

4 reported for DeepSeek R1 Zero · 14 for Gemini 2.5 Flash

2 shared

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

Gemini 2.5 Flash significantly outperforms across most benchmarks.

Sat Aug 29 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

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

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Google
Gemini 2.5 Flash
Input1,048,576 tokens
Output65,536 tokens
Sat Aug 29 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

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

Gemini 2.5 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 2.5 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Gemini 2.5 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 2.5 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

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

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

DeepSeek R1 Zero

Jan 20, 2025

1.6 years ago

Gemini 2.5 Flash

May 20, 2025

1.3 years ago

4mo newer

Knowledge Cutoff

When training data ends

Gemini 2.5 Flash 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 Flash'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 Flash

Jan 2025

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against DeepSeek R1 Zero and Gemini 2.5 Flash side-by-side, then vote on the output you prefer.

DeepSeek R1 Zero
✓ Preferred
Gemini 2.5 Flash
Open in Playground

FAQ

Common questions about DeepSeek R1 Zero vs Gemini 2.5 Flash.

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

DeepSeek R1 Zero and Gemini 2.5 Flash are closely matched on the LLM Stats Score at 16.2 and 22.1. DeepSeek R1 Zero is made by DeepSeek and Gemini 2.5 Flash is made by Google. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

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

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Gemini 2.5 Flash scores Global-MMLU-Lite: 88.4%, AIME 2024: 88.0%, FACTS Grounding: 85.3%, GPQA: 82.8%, MMMU: 79.7%.

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

DeepSeek R1 Zero supports an unknown number of tokens and Gemini 2.5 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 2.5 Flash?

Key differences include LLM Stats Score (16.2 vs 22.1), 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 Flash?

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