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DeepSeek-V4-Pro-0813 vs Gemini 2.0 Flash Thinking

Comparing DeepSeek-V4-Pro-0813 and Gemini 2.0 Flash Thinking across benchmarks, pricing, and capabilities.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4-Pro-0813 and Gemini 2.0 Flash Thinking trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Pro-0813

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

Choose Gemini 2.0 Flash Thinking

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4-Pro-0813
DeepSeek
Gemini 2.0 Flash Thinking
Google
Benchmark wins
—
—
Input price
$0.43 / M
— / M
Output price
$0.87 / M
— / M
Context window
1,048,576
—
Released
Aug 2026
Jan 2025
License
MIT
Proprietary

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Pro-0813 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

Playground indexes and blind preference scores

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-0813 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-0813 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4-Pro-0813
Input1,048,576 tokens
Output393,216 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Mon Aug 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 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-Pro-0813

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Pro-0813 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-Pro-0813

MIT

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

DeepSeek-V4-Pro-0813 is 19 months newer than Gemini 2.0 Flash Thinking.

DeepSeek-V4-Pro-0813

Aug 13, 2026

1 weeks ago

1.6yr newer
Gemini 2.0 Flash Thinking

Jan 21, 2025

1.6 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-Pro-0813'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-Pro-0813's cutoff date.

DeepSeek-V4-Pro-0813

—

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-V4-Pro-0813 and Gemini 2.0 Flash Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-0813
✓ Preferred
Gemini 2.0 Flash Thinking
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-0813 vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-V4-Pro-0813 or Gemini 2.0 Flash Thinking?

DeepSeek-V4-Pro-0813 (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-Pro-0813 compare to Gemini 2.0 Flash Thinking in benchmarks?

DeepSeek-V4-Pro-0813 scores Terminal-Bench 2.1: 87.9%, CyberGym: 83.3%, Toolathlon: 74.1%, DSBench-FullStack: 71.1%, DSBench-Hard: 67.2%. 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-Pro-0813 and Gemini 2.0 Flash Thinking?

DeepSeek-V4-Pro-0813 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-Pro-0813 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-Pro-0813 and Gemini 2.0 Flash Thinking?

DeepSeek-V4-Pro-0813 is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.

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