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DeepSeek-V4.1-Flash-1017 vs Gemma 3 1B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to -10.8.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to -10.8, 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 and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Gemma 3 1B

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Gemma 3 1B
Google
Core performance indexes
LLM Stats Score
51.0
#15
-10.8
#391
Reasoning
48.6
#18
-12.4
#385
Coding
39.1
#15
-10.3
#287
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 3 1B
Math
31.8#67
-9.2#337
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 18 for Gemma 3 1B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Gemma 3 1Bdon'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

762.2B diff

DeepSeek-V4.1-Flash-1017 has 762.2B more parameters than Gemma 3 1B, making it 76220.5% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Google
Gemma 3 1B
1.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
1.0B
Gemma 3 1B

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 3 1B
Input- tokens
Output- tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Gemma 3 1B does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Gemma 3 1B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Gemma 3 1B uses Gemma.

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 3 1B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Gemma 3 1B was released on 2025-03-12.

DeepSeek-V4.1-Flash-1017 is 19 months newer than Gemma 3 1B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

3 days ago

1.6yr newer
Gemma 3 1B

Mar 12, 2025

1.6 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

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

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

FAQ

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

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

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to -10.8. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Gemma 3 1B 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 3 1B 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 3 1B scores IFEval: 80.2%, GSM8k: 62.8%, Natural2Code: 56.0%, MATH: 48.0%, HumanEval: 41.5%.

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

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

Key differences include LLM Stats Score (51.0 vs -10.8), multimodal support (yes vs no), licensing (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.

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

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

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