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DeepSeek-V4.1-Flash-1017 vs Gemma 3n E2B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to -10.5.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to -10.5, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026
  • you need open weights you can self-host or fine-tune

Choose Gemma 3n E2B

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Gemma 3n E2B
Google
Core performance indexes
LLM Stats Score
51.0
#15
-10.5
#390
Reasoning
48.6
#18
-10.7
#383
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
Gemma 3n E2B
Math
31.8#67
-6.3#332
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 11 for Gemma 3n E2B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Gemma 3n E2Bdon'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

Model Size

Parameter count comparison

755.2B diff

DeepSeek-V4.1-Flash-1017 has 755.2B more parameters than Gemma 3n E2B, making it 9440.1% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Google
Gemma 3n E2B
8.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
8.0B
Gemma 3n E2B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Google
Gemma 3n E2B
Input- tokens
Output- tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Gemma 3n E2B support multimodal inputs.

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

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Gemma 3n E2B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Gemma 3n E2B uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Gemma 3n E2B

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Gemma 3n E2B was released on 2025-06-26.

DeepSeek-V4.1-Flash-1017 is 16 months newer than Gemma 3n E2B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1.3yr newer
Gemma 3n E2B

Jun 26, 2025

1.3 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B has a documented knowledge cutoff of 2024-06-01, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.

We can confirm Gemma 3n E2B's training data extends to 2024-06-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.

DeepSeek-V4.1-Flash-1017

—

Gemma 3n E2B

Jun 2024

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Gemma 3n E2B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Gemma 3n E2B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Gemma 3n E2B.

Which is better, DeepSeek-V4.1-Flash-1017 or Gemma 3n E2B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to -10.5. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Gemma 3n E2B 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-1017 compare to Gemma 3n E2B in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. Gemma 3n E2B scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Gemma 3n E2B?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Gemma 3n E2B 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-1017 and Gemma 3n E2B?

Key differences include LLM Stats Score (51.0 vs -10.5), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Gemma 3n E2B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Gemma 3n E2B is developed by Google.

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