The AI arena is free today

Open Superagent

DeepSeek-V4.1-Flash vs Gemma 3n E4B Instructed

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -4.7. DeepSeek-V4.1-Flash is 75.8x cheaper per token.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to -4.7, 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.

On price, DeepSeek-V4.1-Flash is roughly 75.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 input tokens), making it the stronger choice for long documents and large codebases.

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
  • cost matters — it's about 75.8x cheaper per token
  • you process long inputs — it offers a 1,040,000 token context window
  • 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 E4B Instructed

  • you want predictable pricing at $20.00/M input and $40.00/M output

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Gemma 3n E4B Instructed
Google
Core performance indexes
LLM Stats Score
51.8
#13
-4.7
#350
Reasoning
48.9
#18
-5.5
#349
Coding
44.2
#5
-0.1
#242
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
$20.00 / M
Output price
$0.66 / M
$40.00 / M
Context window
1,040,000
32,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash
Gemma 3n E4B Instructed
Math
35.2#43
-1.8#306
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 18 for Gemma 3n E4B Instructed

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Gemma 3n E4B 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

Pricing Analysis

Price comparison per million tokens

DeepSeek-V4.1-Flash costs less

For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 90.9x cheaper than Gemma 3n E4B Instructed ($20.00/1M tokens).

For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 60.6x cheaper than Gemma 3n E4B Instructed ($40.00/1M tokens).

In conclusion, Gemma 3n E4B Instructed is more expensive than DeepSeek-V4.1-Flash.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sun Sep 20 2026 • llm-stats.com
DeepSeek
DeepSeek-V4.1-Flash
Input tokens$0.22
Output tokens$0.66
Best providerFireworks
Google
Gemma 3n E4B Instructed
Input tokens$20.00
Output tokens$40.00
Best providerTogether
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

755.2B diff

DeepSeek-V4.1-Flash has 755.2B more parameters than Gemma 3n E4B Instructed, making it 9440.1% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Google
Gemma 3n E4B Instructed
8.0Bparameters
763.2B
DeepSeek-V4.1-Flash
8.0B
Gemma 3n E4B Instructed

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Gemma 3n E4B Instructed's 32,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Gemma 3n E4B Instructed is limited to 32,000 tokens.

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Google
Gemma 3n E4B Instructed
Input32,000 tokens
Output32,000 tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Gemma 3n E4B 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 E4B Instructed

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Gemma 3n E4B 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 E4B Instructed

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Gemma 3n E4B Instructed was released on 2025-06-26.

DeepSeek-V4.1-Flash is 15 months newer than Gemma 3n E4B Instructed.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 weeks ago

1.2yr newer
Gemma 3n E4B Instructed

Jun 26, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E4B 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 E4B 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 E4B Instructed

Jun 2024

Provider Availability

DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Gemma 3n E4B Instructed is available from Together.

DeepSeek-V4.1-Flash

fireworks logo
Fireworks
Input Price:Input: $0.22/1MOutput Price:Output: $0.66/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
deepseek logo
DeepSeek
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

Gemma 3n E4B Instructed

together logo
Together
Input Price:Input: $20.00/1MOutput Price:Output: $40.00/1M
* Prices shown are per million tokens

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 E4B Instructed side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Gemma 3n E4B Instructed
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Gemma 3n E4B Instructed.

Which is better, DeepSeek-V4.1-Flash or Gemma 3n E4B Instructed?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -4.7. DeepSeek-V4.1-Flash is made by DeepSeek and Gemma 3n E4B 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 E4B 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 E4B Instructed scores HumanEval: 75.0%, MGSM: 67.0%, MMLU: 64.9%, Global-MMLU-Lite: 64.5%, MBPP: 63.6%.

Is DeepSeek-V4.1-Flash cheaper than Gemma 3n E4B Instructed?

DeepSeek-V4.1-Flash is 90.9x cheaper for input tokens. DeepSeek-V4.1-Flash costs $0.22/M input and $0.66/M output via fireworks. Gemma 3n E4B Instructed costs $20.00/M input and $40.00/M output via together.

What are the context window sizes for DeepSeek-V4.1-Flash and Gemma 3n E4B Instructed?

DeepSeek-V4.1-Flash supports 1.0M tokens and Gemma 3n E4B Instructed supports 32K 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 E4B Instructed?

Key differences include LLM Stats Score (51.8 vs -4.7), context window (1.0M vs 32K), input pricing ($0.22 vs $20.00/M), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Gemma 3n E4B Instructed?

DeepSeek-V4.1-Flash is developed by DeepSeek and Gemma 3n E4B Instructed is developed by Google.

Related comparisons