gemma3:4b
Shared local model served from Mac Ultra Ollama.
A dedicated public landing page for this synced catalog row.
Synced from the active catalog
Validated for gateway requests
Free model row
Free model row
This page exists for direct linking, onboarding, and SEO. The same model row powers /v1/models, the public catalog, the app pricing screen, and request validation for /v1/chat/completions.
The same model route, but with one account, one balance, and one API shape.
This model is currently listed at $0.00 per token through UAI. Free rows can have lower rate limits, but they are useful for testing and evaluation.
Call gemma3:4b with the same UAI API key, the same /v1/chat/completions shape, and the same request validation used across the rest of the catalog.
Pricing, balance, and request history stay consolidated in one UAI account instead of spreading usage across separate Google dashboards.
Current synced numbers for this exact model row.
Need another option? Browse the full models catalog or compare rows in the signed-in pricing table.
Use this model with your UAI key over the standard OpenAI-compatible route.
Create a key in API Keys, then send requests to https://uai.sh/v1/chat/completions.
curl https://uai.sh/v1/chat/completions \
-H "Authorization: Bearer uai-YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemma3:4b",
"messages": [{"role": "user", "content": "Say hi in one short sentence."}],
"stream": false
}'
from openai import OpenAI
client = OpenAI(base_url="https://uai.sh/v1", api_key="uai-YOUR_API_KEY")
response = client.chat.completions.create(
model="gemma3:4b",
messages=[{"role": "user", "content": "Say hi in one short sentence."}],
stream=False
)
print(response.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://uai.sh/v1",
apiKey: "uai-YOUR_API_KEY"
});
const response = await client.chat.completions.create({
model: "gemma3:4b",
messages: [{ role: "user", content: "Say hi in one short sentence." }],
stream: false
});
console.log(response.choices[0].message.content);
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