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DeepSeek-V4.1-Flash vs Gemma 3n E2B Instructed LiteRT (Preview)

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -9.6.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to -9.6, ranking #13 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 #13 on LLM Stats
  • your work emphasizes reasoning and coding — 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

Choose Gemma 3n E2B Instructed LiteRT (Preview)

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

Core performance indexes
51.8
#13
-9.6
#365
48.9
#18
-9.8
#358
44.2
#5
-4.4
#256
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

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash
Gemma 3n E2B Instructed LiteRT (Preview)
35.2#43
-6.4#322
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 28 for Gemma 3n E2B Instructed LiteRT (Preview)

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Gemma 3n E2B Instructed LiteRT (Preview) is better at 0 benchmarks.

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

Sun Sep 20 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

761.3B diff

DeepSeek-V4.1-Flash has 761.3B more parameters than Gemma 3n E2B Instructed LiteRT (Preview), making it 39858.4% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Google
Gemma 3n E2B Instructed LiteRT (Preview)
1.9Bparameters
763.2B
DeepSeek-V4.1-Flash
1.9B
Gemma 3n E2B Instructed LiteRT (Preview)

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
Gemma 3n E2B Instructed LiteRT (Preview)
Input- tokens
Output- tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Gemma 3n E2B Instructed LiteRT (Preview) 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

Gemma 3n E2B Instructed LiteRT (Preview)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Gemma 3n E2B Instructed LiteRT (Preview) uses Gemma.

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

DeepSeek-V4.1-Flash

MIT

Open weights

Gemma 3n E2B Instructed LiteRT (Preview)

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Gemma 3n E2B Instructed LiteRT (Preview) was released on 2025-05-20.

DeepSeek-V4.1-Flash is 16 months newer than Gemma 3n E2B Instructed LiteRT (Preview).

DeepSeek-V4.1-Flash

Sep 10, 2026

1 weeks ago

1.3yr newer
Gemma 3n E2B Instructed LiteRT (Preview)

May 20, 2025

1.3 years ago

Knowledge Cutoff

When training data ends

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

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

DeepSeek-V4.1-Flash

Gemma 3n E2B Instructed LiteRT (Preview)

Jun 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 Gemma 3n E2B Instructed LiteRT (Preview) side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Gemma 3n E2B Instructed LiteRT (Preview)
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Gemma 3n E2B Instructed LiteRT (Preview).

Which is better, DeepSeek-V4.1-Flash or Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -9.6. DeepSeek-V4.1-Flash is made by DeepSeek and Gemma 3n E2B Instructed LiteRT (Preview) 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 Gemma 3n E2B Instructed LiteRT (Preview) 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%. Gemma 3n E2B Instructed LiteRT (Preview) 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 and Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V4.1-Flash supports 1.0M tokens and Gemma 3n E2B Instructed LiteRT (Preview) 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 Gemma 3n E2B Instructed LiteRT (Preview)?

Key differences include LLM Stats Score (51.8 vs -9.6), licensing (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V4.1-Flash is developed by DeepSeek and Gemma 3n E2B Instructed LiteRT (Preview) is developed by Google.