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DeepSeek-V2.5 vs Gemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking leads the LLM Stats Score 16.7 to 8.1.

DeepSeek · Google · Updated for 2026

Which is better?

Gemini 2.0 Flash Thinking leads the overall LLM Stats Score 16.7 to 8.1, ranking #218 overall.

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

Choose DeepSeek-V2.5

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

Choose Gemini 2.0 Flash Thinking

  • overall performance matters — it scores 16.7 and ranks #218 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Jan 2025

At a glance

The differences that matter most.

DeepSeek-V2.5
DeepSeek
Gemini 2.0 Flash Thinking
Google
Core performance indexes
LLM Stats Score
8.1
#280
16.7
#218
Reasoning
8.2
#276
17.0
#211
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.14 / M
— / M
Output price
$0.28 / M
— / M
Context window
8,192
—

Individual benchmarks

15 reported for DeepSeek-V2.5 · 3 for Gemini 2.0 Flash Thinking

No common benchmarks found

DeepSeek-V2.5 and Gemini 2.0 Flash Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Sun Sep 13 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-V2.5 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-V2.5

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, 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-V2.5

deepseek

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

Gemini 2.0 Flash Thinking is 9 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.4 years ago

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.6 years ago

8mo newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-V2.5'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-V2.5's cutoff date.

DeepSeek-V2.5

—

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-V2.5 and Gemini 2.0 Flash Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V2.5
✓ Preferred
Gemini 2.0 Flash Thinking
Open in Playground

FAQ

Common questions about DeepSeek-V2.5 vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-V2.5 or Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash Thinking leads the LLM Stats Score 16.7 to 8.1. DeepSeek-V2.5 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-V2.5 compare to Gemini 2.0 Flash Thinking in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for DeepSeek-V2.5 and Gemini 2.0 Flash Thinking?

DeepSeek-V2.5 supports 8K 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-V2.5 and Gemini 2.0 Flash Thinking?

Key differences include LLM Stats Score (8.1 vs 16.7), multimodal support (no vs yes), licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V2.5 and Gemini 2.0 Flash Thinking?

DeepSeek-V2.5 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.

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