Gemma 3n 4B APIOPEN WEIGHTS33K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Gemma 3n 4B?

Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks...

Gemma 3n 4B on the release timeline

Gemma 3n 4B pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Gemma 3n 4B is cheaper than 95% of the 309 models in the catalog.

What Gemma 3n 4B costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Gemma 3n 4B vs alternatives

When not to use Gemma 3n 4B

• It is served by a single provider, so there is less failover headroom during an outage.

Specs

How to call Gemma 3n 4B

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="google/gemma-3n-e4b-it",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"google/gemma-3n-e4b-it","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "google/gemma-3n-e4b-it",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Gemma 3n 4B and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Gemma 3n 4B

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Gemma 3n 4B follows concrete instructions better than abstract ones.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
• Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.

Tips are derived from Gemma 3n 4B's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Gemma 3n 4B

Popular open-source projects for running and building with Gemma 3n 4B — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Gemma 3n 4B

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → google/gemma-3n-e4b-it. Messages, streaming and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Gemma 3n 4B ships open weights (google/gemma-3n-E4B-it, ~20.3K downloads and 918 likes in the last 30 days), so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 1 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff. See the error-code guide and failover setup.

Related reading

Frequently asked questions