Gemma 3n E4B Instruct Preview (May '25)
Gemma 3n E4B Instruct Preview (May '25) is an API model from Google. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Below avg
Context
32K
Up to 32,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
32k tokens
≈ 25 pages
4k Chat 聊天
32k This model 本模型
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
4.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Edge-optimised Gemma 3 variant.
- Four sizes (1B / 4B / 12B / 27B) — covers laptop-class up to single-GPU server inference
- Gemma-3-4B-IT beats Gemma-2-27B-IT; Gemma-3-27B-IT beats Gemini 1.5 Pro on shared benchmarks
- Vision input from 4B up; 1B is text-only — first Gemma family with multimodal at small sizes
Best use cases
- Single-GPU production inference (27B fits H100 / A100 80GB at FP precision)
- Laptop / phone deployment via the 1B / 4B variants
- Multilingual apps in 140 languages at open-weights pricing
Tools to try
Not ideal for
- Frontier reasoning leaderboards — Gemini 2.5 / 3 Pro and competitors lead
- Workloads needing 1M+ context (Gemma 3 caps at 128K)
Model Evolution
/ DeepMind gemma is Google/DeepMind's language model family.