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DeepSeek-V4.1-Flash vs Gemma 4 12B

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 22.0.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 22.0, ranking #12 overall.

In the 3 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 3; 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 #12 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results
  • you want the most recent training data — it shipped Sep 2026

Choose Gemma 4 12B

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Gemma 4 12B
Google
Core performance indexes
LLM Stats Score
51.8
#12
22.0
#179
Reasoning
48.9
#17
23.0
#166
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
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

3 shared
Index
DeepSeek-V4.1-Flash
Gemma 4 12B
Math
35.2#43
18.4#181
Vision
29.5#31
10.9#113
Multimodal
34.3#13
17.0#78
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 15 for Gemma 4 12B

3 shared

DeepSeek-V4.1-Flash outperforms in 3 benchmarks (CodeForces, GPQA, Humanity's Last Exam), while Gemma 4 12B is better at 0 benchmarks.

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

Fri Sep 11 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

751.2B diff

DeepSeek-V4.1-Flash has 751.2B more parameters than Gemma 4 12B, making it 6281.5% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Google
Gemma 4 12B
12.0Bparameters
763.2B
DeepSeek-V4.1-Flash
12.0B
Gemma 4 12B

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 4 12B
Input- tokens
Output- tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Gemma 4 12B 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 4 12B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Gemma 4 12B uses Apache 2.0.

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

DeepSeek-V4.1-Flash

MIT

Open weights

Gemma 4 12B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Gemma 4 12B was released on 2026-05-23.

DeepSeek-V4.1-Flash is 4 months newer than Gemma 4 12B.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 days ago

3mo newer
Gemma 4 12B

May 23, 2026

3 months ago

Knowledge Cutoff

When training data ends

Gemma 4 12B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-V4.1-Flash's cutoff date is not specified.

We can confirm Gemma 4 12B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash's cutoff date.

DeepSeek-V4.1-Flash

—

Gemma 4 12B

Jan 2025

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 4 12B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Gemma 4 12B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Gemma 4 12B.

Which is better, DeepSeek-V4.1-Flash or Gemma 4 12B?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 22.0. DeepSeek-V4.1-Flash is made by DeepSeek and Gemma 4 12B 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 4 12B 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 4 12B scores FLEURS: 93.1%, MMMLU: 83.4%, MathVision: 79.7%, GPQA: 78.8%, AIME 2026: 77.5%.

What are the context window sizes for DeepSeek-V4.1-Flash and Gemma 4 12B?

DeepSeek-V4.1-Flash supports 1.0M tokens and Gemma 4 12B 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 4 12B?

Key differences include LLM Stats Score (51.8 vs 22.0), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Gemma 4 12B?

DeepSeek-V4.1-Flash is developed by DeepSeek and Gemma 4 12B is developed by Google.

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