Gemini 2.5 Flash-Lite is the lightweight member of the Gemini 2.5 family, optimized for ultra-fast responses, high throughput, and cost-efficient AI workloads. It delivers improved performance, faster token generation, and greater efficiency compared with previous Flash-class models. By default, thinking capabilities (multi-step reasoning) are disabled to maximize speed and minimize cost. Developers can enable reasoning through the Reasoning API parameter when deeper intelligence is required, providing flexible control over the balance between latency, capability, and resource usage.
Pricing
USD · live rate| Pricing | USD / M |
|---|---|
| Input | $0.10 |
| Output | $0.40 |
| Cache hit | $0.01 |
| Image input | — |
Providers
Same model, multi-channel live comparison · best values highlighted| Provider | Context | Max output | Input /M | Output /M | Cache /M | Latency | Throughput |
|---|---|---|---|---|---|---|---|
| OpenRouter | 1M | 64K | $0.10 | $0.40 | $0.01 | 3653ms | 2140 t/s |
| Google Vertex | 1M | 64K | $0.10 | $0.40 | $0.03 | 956ms | 306 t/s |
Usage trends
API example
Model Center normalizes requests and responses across providers behind one OpenAI-compatible API.
Call this model directly or through the OpenAI SDK — one API key for every model in the catalog.
from openai import OpenAI
client = OpenAI(
base_url="https://router-integration.test.cogfoundry.ai/api/v1",
api_key="$MODEL_CENTER_API_KEY",
)
completion = client.chat.completions.create(
model="google/gemini-2.5-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-2.5-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-2.5-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-2.5-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-2.5-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-2.5-flash-lite",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}]
}'