The AI arena is free today

Open Superagent

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

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -10.6.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to -10.6, 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
  • you need open weights you can self-host or fine-tune

Choose Gemma 3n E2B Instructed

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Gemma 3n E2B Instructed
Google
Core performance indexes
LLM Stats Score
51.8
#13
-10.6
#368
Reasoning
48.9
#18
-11.0
#360
Coding
44.2
#5
-4.4
#257
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
Math
35.2#43
-8.0#324
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 18 for Gemma 3n E2B Instructed

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Gemma 3n E2B Instructed 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

755.2B diff

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

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

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
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 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

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Gemma 3n E2B Instructed 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

Gemma 3n E2B Instructed

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Gemma 3n E2B Instructed was released on 2025-06-26.

DeepSeek-V4.1-Flash is 15 months newer than Gemma 3n E2B Instructed.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 weeks ago

1.2yr newer
Gemma 3n E2B Instructed

Jun 26, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed 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'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

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 side-by-side, then vote on the output you prefer.

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

FAQ

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

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

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -10.6. DeepSeek-V4.1-Flash is made by DeepSeek and Gemma 3n E2B Instructed 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 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 scores HumanEval: 66.5%, MMLU: 60.1%, Global-MMLU-Lite: 59.0%, MBPP: 56.6%, Global-MMLU: 55.1%.

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

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

Key differences include LLM Stats Score (51.8 vs -10.6), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

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

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

Related comparisons