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DeepSeek-V4.1-Flash vs Gemini 2.0 Flash Thinking

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 16.7.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 16.7, ranking #12 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; 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-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Sep 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-V4.1-Flash
DeepSeek
Gemini 2.0 Flash Thinking
Google
Core performance indexes
LLM Stats Score
51.8
#12
16.7
#218
Reasoning
48.9
#17
17.0
#211
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 3 for Gemini 2.0 Flash Thinking

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Gemini 2.0 Flash Thinking is better at 0 benchmarks.

DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.

Fri Sep 11 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-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Gemini 2.0 Flash Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash 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-V4.1-Flash

MIT

Open weights

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

DeepSeek-V4.1-Flash is 20 months newer than Gemini 2.0 Flash Thinking.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 days 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-V4.1-Flash'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-V4.1-Flash's cutoff date.

DeepSeek-V4.1-Flash

—

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-V4.1-Flash and Gemini 2.0 Flash Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Gemini 2.0 Flash Thinking
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Gemini 2.0 Flash Thinking.

Which is better, DeepSeek-V4.1-Flash or Gemini 2.0 Flash Thinking?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 16.7. DeepSeek-V4.1-Flash 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-V4.1-Flash compare to Gemini 2.0 Flash Thinking in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for DeepSeek-V4.1-Flash and Gemini 2.0 Flash Thinking?

DeepSeek-V4.1-Flash 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-V4.1-Flash and Gemini 2.0 Flash Thinking?

Key differences include LLM Stats Score (51.8 vs 16.7), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Gemini 2.0 Flash Thinking?

DeepSeek-V4.1-Flash is developed by DeepSeek and Gemini 2.0 Flash Thinking is developed by Google.

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