Gemini 2.5 Flash is Google’s efficient, high-performance AI model built for demanding reasoning, coding, mathematics, and scientific workloads. Its integrated thinking capabilities enable stronger problem solving, improved accuracy, and better handling of complex multi-step tasks. Developers can fine-tune reasoning depth using the “max tokens for reasoning” setting, providing flexible control over the trade-off between intelligence, speed, and cost for different AI applications.
Pricing
USD · live rate| Pricing | USD / M |
|---|---|
| Input | $0.30 |
| Output | $2.50 |
| 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 |
|---|---|---|---|---|---|---|---|
| OpenRouter | 1M | 66K | $0.30 | $2.50 | $0.03 | 1937ms | 260 t/s |
| Google Vertex | 1M | 66K | $0.30 | $2.50 | $0.07 | 13408ms | 461 t/s |
Usage trends
API example
Model Center normalizes requests and responses across providers behind one OpenAI-compatible API.
Call this model directly or through the OpenAI SDK — one API key for every model in the catalog.
from openai import OpenAI
client = OpenAI(
base_url="https://router-integration.test.cogfoundry.ai/api/v1",
api_key="$MODEL_CENTER_API_KEY",
)
completion = client.chat.completions.create(
model="google/gemini-2.5-flash",
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",
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",
"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",
"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",
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",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}]
}'