Model Comparison

DeepSeek-V4-Flash-0731 vs Gemini 2.0 Flash ThinkingWhich is better in 2026?

Comparing DeepSeek-V4-Flash-0731 and Gemini 2.0 Flash Thinking across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-V4-Flash-0731 vs Gemini 2.0 Flash Thinking — which is better?

DeepSeek-V4-Flash-0731 (by DeepSeek) and Gemini 2.0 Flash Thinking (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.

Choose DeepSeek-V4-Flash-0731 if…

  • you want the most recent training data — it shipped Jul 2026
  • you need open weights you can self-host or fine-tune

Choose Gemini 2.0 Flash Thinking if…

  • you are already invested in the Google ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Flash-0731 and Gemini 2.0 Flash Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Flash-0731 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0731 specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V4-Flash-0731
Input1,048,576 tokens
Output65,536 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Mon Aug 03 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 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-V4-Flash-0731

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Flash-0731 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-V4-Flash-0731

MIT

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

DeepSeek-V4-Flash-0731 is 19 months newer than Gemini 2.0 Flash Thinking.

DeepSeek-V4-Flash-0731

Jul 31, 2026

3 days ago

1.5yr newer
Gemini 2.0 Flash Thinking

Jan 21, 2025

1.5 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while DeepSeek-V4-Flash-0731'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-V4-Flash-0731's cutoff date.

DeepSeek-V4-Flash-0731

—

Gemini 2.0 Flash Thinking

Aug 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (1,048,576 tokens)
Has open weights
Google
Supports multimodal inputs

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V4-Flash-0731 and Gemini 2.0 Flash Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-0731
✓ Preferred
Gemini 2.0 Flash Thinking
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V4-Flash-0731
Google
Gemini 2.0 Flash Thinking

FAQ

Common questions about DeepSeek-V4-Flash-0731 vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-V4-Flash-0731 or Gemini 2.0 Flash Thinking?

DeepSeek-V4-Flash-0731 (DeepSeek) and Gemini 2.0 Flash Thinking (Google) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V4-Flash-0731 compare to Gemini 2.0 Flash Thinking in benchmarks?

DeepSeek-V4-Flash-0731 scores Terminal-Bench 2.1: 82.7%, CyberGym: 76.7%, Toolathlon: 70.3%, DSBench-FullStack: 68.7%, DSBench-Hard: 59.6%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for DeepSeek-V4-Flash-0731 and Gemini 2.0 Flash Thinking?

DeepSeek-V4-Flash-0731 supports 1.0M 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-V4-Flash-0731 and Gemini 2.0 Flash Thinking?

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-V4-Flash-0731 and Gemini 2.0 Flash Thinking?

DeepSeek-V4-Flash-0731 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.

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