DeepSeek-V4.1-Flash vs Gemma 3 1B
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -10.5.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to -10.5, 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
Choose Gemma 3 1B
- you are already invested in the Google ecosystem
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 18 for Gemma 3 1B
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Gemma 3 1B is better at 0 benchmarks.
DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 762.2B more parameters than Gemma 3 1B, making it 76220.5% larger.
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).
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Gemma 3 1B does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
Gemma 3 1B
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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.
MIT
Open weights
Gemma
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Gemma 3 1B was released on 2025-03-12.
DeepSeek-V4.1-Flash is 18 months newer than Gemma 3 1B.
Sep 10, 2026
5 days ago
1.5yr newerMar 12, 2025
1.5 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Gemma 3 1B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Gemma 3 1B.