Gemini 3.1 Flash Lite is Google’s high-efficiency multimodal model, optimized for low latency, high throughput, and cost-effective AI workloads. It supports text, images, video, audio, and PDF inputs, making it ideal for lightweight agentic workflows, data extraction, content processing, and other high-volume applications where speed and cost are critical. The model supports configurable thinking levels (minimal, low, medium, and high), allowing developers to balance intelligence, latency, and cost for different workloads. At half the price of Gemini 3 Flash, Gemini 3.1 Flash Lite delivers excellent value for scalable production deployments.
Pricing
USD · live rate| Pricing | USD / M |
|---|---|
| Input | $0.25 |
| Output | $1.50 |
| Cache hit | $0.03 |
| Image input | — |
Providers
Same model, multi-channel live comparison · best values highlighted| Provider | Context | Max output | Input /M | Output /M | Cache /M | Latency | Throughput |
|---|---|---|---|---|---|---|---|
| Google Vertex | 1M | 66K | $0.25 | $1.50 | $0.03 | 2120ms | 197 t/s |
Usage trends
API example
Model Center normalizes requests and responses across providers behind one OpenAI-compatible API.
Call this model directly or through the OpenAI SDK — one API key for every model in the catalog.
from openai import OpenAI
client = OpenAI(
base_url="https://router-integration.test.cogfoundry.ai/api/v1",
api_key="$MODEL_CENTER_API_KEY",
)
completion = client.chat.completions.create(
model="google/gemini-3.1-flash-lite",
messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}],
stream=True,
)
for chunk in completion:
if chunk.choices and chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="", flush=True)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://router-integration.test.cogfoundry.ai/api/v1",
apiKey: process.env.MODEL_CENTER_API_KEY,
});
const stream = await client.chat.completions.create({
model: "google/gemini-3.1-flash-lite",
messages: [{ role: "user", content: "Which number is larger, 9.11 or 9.8?" }],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}curl https://router-integration.test.cogfoundry.ai/api/v1/chat/completions \
-H "Authorization: Bearer $MODEL_CENTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-3.1-flash-lite",
"messages": [{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}],
"stream": true
}'import requests
response = requests.post(
"https://router-integration.test.cogfoundry.ai/api/v1/messages",
headers={"Authorization": "Bearer $MODEL_CENTER_API_KEY", "Content-Type": "application/json"},
json={
"model": "google/gemini-3.1-flash-lite",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}],
},
)
print(response.json()["content"][0]["text"])const response = await fetch("https://router-integration.test.cogfoundry.ai/api/v1/messages", {
method: "POST",
headers: {
Authorization: "Bearer $MODEL_CENTER_API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "google/gemini-3.1-flash-lite",
max_tokens: 1024,
messages: [{ role: "user", content: "Which number is larger, 9.11 or 9.8?" }],
}),
});
const data = await response.json();
console.log(data.content[0].text);curl https://router-integration.test.cogfoundry.ai/api/v1/messages \
-H "Authorization: Bearer $MODEL_CENTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-3.1-flash-lite",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}]
}'