Gemini 2.5 Pro is Google’s advanced AI model built for complex reasoning, software development, mathematics, and scientific applications. With integrated thinking capabilities, it performs deeper multi-step reasoning to deliver more accurate answers, stronger problem solving, and better understanding of complex contexts. Gemini 2.5 Pro achieves leading performance across a range of industry benchmarks, demonstrating strong capabilities in coding, reasoning, and complex task execution. Its advanced intelligence and reliability make it well suited for demanding applications such as AI agents, research workflows, and enterprise-grade automation.
Pricing
USD · live rate| Pricing | USD / M |
|---|---|
| Input | $1.25 ≤200K $2.50 >200K |
| Output | $10.00 ≤200K $15.00 >200K |
| Cache hit | $0.13 ≤200K $0.25 >200K |
| 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 | $1.25 ≤200K $2.50 >200K | $10.00 ≤200K $15.00 >200K | $0.13 ≤200K $0.25 >200K | 1555ms | 98 t/s |
| Google Vertex | 1M | 66K | $1.25 ≤200K $2.50 >200K | $10.00 ≤200K $15.00 >200K | $0.13 ≤200K $0.25 >200K | 7345ms | 184 t/s |
Usage trends
API example
Model Center normalizes requests and responses across providers behind one OpenAI-compatible API.
Call this model directly or through the OpenAI SDK — one API key for every model in the catalog.
from openai import OpenAI
client = OpenAI(
base_url="https://router-integration.test.cogfoundry.ai/api/v1",
api_key="$MODEL_CENTER_API_KEY",
)
completion = client.chat.completions.create(
model="google/gemini-2.5-pro",
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-pro",
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-pro",
"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-pro",
"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-pro",
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-pro",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}]
}'