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DeepSeek-V3 vs Gemini 2.0 Flash Thinking

DeepSeek-V3 and Gemini 2.0 Flash Thinking are closely matched at 15.7 and 17.0 on the LLM Stats Score.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V3 and Gemini 2.0 Flash Thinking are closely matched on the overall LLM Stats Score at 15.7 and 17.0.

In the 2 individual benchmarks reported for both models, Gemini 2.0 Flash Thinking 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-V3

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

Choose Gemini 2.0 Flash Thinking

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

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
Gemini 2.0 Flash Thinking
Google
Core performance indexes
LLM Stats Score
15.7
#216
17.0
#207
Reasoning
14.8
#217
17.2
#199
Cost, coverage & limits
Benchmark wins
0 of 2
2 of 2
Input price
$0.27 / M
— / M
Output price
$1.10 / M
— / M
Context window
131,072
—

Individual benchmarks

20 reported for DeepSeek-V3 · 3 for Gemini 2.0 Flash Thinking

2 shared

DeepSeek-V3 outperforms in 0 benchmarks, while Gemini 2.0 Flash Thinking is better at 2 benchmarks (AIME 2024, GPQA).

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Sun Sep 06 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Sun Sep 06 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-V3 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-V3

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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-V3

MIT + Model License (Commercial use allowed)

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

Gemini 2.0 Flash Thinking is 1 month newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.7 years ago

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.6 years ago

3w newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-V3'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-V3's cutoff date.

DeepSeek-V3

—

Gemini 2.0 Flash Thinking

Aug 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Gemini 2.0 Flash Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Gemini 2.0 Flash Thinking
Open in Playground

FAQ

Common questions about DeepSeek-V3 vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-V3 or Gemini 2.0 Flash Thinking?

DeepSeek-V3 and Gemini 2.0 Flash Thinking are closely matched on the LLM Stats Score at 15.7 and 17.0. DeepSeek-V3 is made by DeepSeek and Gemini 2.0 Flash Thinking 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-V3 compare to Gemini 2.0 Flash Thinking in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for DeepSeek-V3 and Gemini 2.0 Flash Thinking?

DeepSeek-V3 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-V3 and Gemini 2.0 Flash Thinking?

Key differences include LLM Stats Score (15.7 vs 17.0), multimodal support (no vs yes), licensing (MIT + Model License (Commercial use allowed) vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Gemini 2.0 Flash Thinking?

DeepSeek-V3 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.

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