DeepSeek-V4.1-Flash-1017 vs Gemma 4 E4B
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 13.3. Gemma 4 E4B is 13.1x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 13.3, ranking #15 overall.
On price, Gemma 4 E4B is roughly 13.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 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-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 process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Gemma 4 E4B
- cost matters — it's about 13.1x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 11 for Gemma 4 E4B
DeepSeek-V4.1-Flash-1017 and Gemma 4 E4Bdon'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
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 15.0x more expensive than Gemma 4 E4B ($0.02/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 12.0x more expensive than Gemma 4 E4B ($0.10/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Gemma 4 E4B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 755.2B more parameters than Gemma 4 E4B, making it 9440.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Gemma 4 E4B's 131,072 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Gemma 4 E4B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and Gemma 4 E4B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
Gemma 4 E4B
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Gemma 4 E4B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
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 E4B was released on 2026-04-02.
DeepSeek-V4.1-Flash-1017 is 7 months newer than Gemma 4 E4B.
Oct 17, 2026
2 days ago
6mo newerApr 2, 2026
6 months ago
Knowledge Cutoff
When training data ends
Gemma 4 E4B 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 E4B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.
—
Jan 2025
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Gemma 4 E4B is available from DeepInfra.
DeepSeek-V4.1-Flash-1017
Gemma 4 E4B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Gemma 4 E4B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Gemma 4 E4B.