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DeepSeek-V4.1-Flash-1017 vs Gemma 4 E2B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 6.3.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 6.3, 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 51.2 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Gemma 4 E2B

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Gemma 4 E2B
Google
Core performance indexes
LLM Stats Score
51.2
#15
6.3
#317
Reasoning
48.8
#18
5.1
#318
Agents
38.7
#8
-3.6
#206
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

3 shared
Index
DeepSeek-V4.1-Flash-1017
Gemma 4 E2B
Math
31.8#69
5.7#283
Vision
32.3#25
0.6#198
Multimodal
35.0#14
2.8#163
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 11 for Gemma 4 E2B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Gemma 4 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

758.1B diff

DeepSeek-V4.1-Flash-1017 has 758.1B more parameters than Gemma 4 E2B, making it 14864.8% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Google
Gemma 4 E2B
5.1Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
5.1B
Gemma 4 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 4 E2B
Input- tokens
Output- tokens
Sat Oct 24 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Gemma 4 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 4 E2B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Gemma 4 E2B uses Apache 2.0.

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 4 E2B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Gemma 4 E2B was released on 2026-04-02.

DeepSeek-V4.1-Flash-1017 is 7 months newer than Gemma 4 E2B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

5 days ago

6mo newer
Gemma 4 E2B

Apr 2, 2026

6 months ago

Knowledge Cutoff

When training data ends

Gemma 4 E2B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.

We can confirm Gemma 4 E2B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.

DeepSeek-V4.1-Flash-1017

—

Gemma 4 E2B

Jan 2025

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Gemma 4 E2B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Gemma 4 E2B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Gemma 4 E2B.

Which is better, DeepSeek-V4.1-Flash-1017 or Gemma 4 E2B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 6.3. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Gemma 4 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 4 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 4 E2B scores MMMLU: 67.4%, MMLU-Pro: 60.0%, MathVision: 52.4%, MMMU-Pro: 44.2%, LiveCodeBench v6: 44.0%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Gemma 4 E2B?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Gemma 4 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 4 E2B?

Key differences include LLM Stats Score (51.2 vs 6.3), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Gemma 4 E2B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Gemma 4 E2B is developed by Google.

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