Gemini 3 Flash Preview is Google’s fast, efficient thinking model for agentic workflows, coding, and interactive AI applications. It delivers near-Pro-level reasoning and tool-use performance with lower latency and cost, making it ideal for multi-turn conversations, autonomous agents, and collaborative development. With a 1 million token context window, multimodal input support (text, images, audio, video, and PDFs), configurable thinking levels, structured outputs, tool calling, and context caching, Gemini 3 Flash Preview provides a strong balance of intelligence, speed, and efficiency.
定价
USD · 实时汇率| 定价 | USD / M |
|---|---|
| 输入 | $0.50 |
| 输出 | $3.00 |
| 命中缓存 | $0.05 |
| 图片输入 | — |
供应商
同一模型，多渠道实时比价 · 最优值绿色高亮| 供应商 | 上下文 | 最大输出 | 输入 /M | 输出 /M | 缓存 /M | 延迟 | 吞吐 |
|---|---|---|---|---|---|---|---|
| Google Vertex | 1M | 66K | $0.50 | $3.00 | $0.05 | 8013ms | 1310 t/s |
| OpenRouter | 1M | 66K | $0.50 | $3.00 | $0.05 | 5026ms | 65 t/s |
运行趋势
接入示例
Model Center 会在不同服务提供商之间规范化处理请求和响应，为你统一接口。
提供兼容 OpenAI 的 Completion API：既可直接调用，也可通过 OpenAI SDK 调用，一把 key 调用全部模型。
from openai import OpenAI
client = OpenAI(
base_url="https://router-integration.test.cogfoundry.ai/api/v1",
api_key="$MODEL_CENTER_API_KEY",
)
completion = client.chat.completions.create(
model="google/gemini-3-flash",
messages=[{"role": "user", "content": "9.11 和 9.8 哪个大？"}],
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-flash",
messages: [{ role: "user", content: "9.11 和 9.8 哪个大？" }],
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-flash",
"messages": [{"role": "user", "content": "9.11 和 9.8 哪个大？"}],
"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-flash",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "9.11 和 9.8 哪个大？"}],
},
)
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-flash",
max_tokens: 1024,
messages: [{ role: "user", content: "9.11 和 9.8 哪个大？" }],
}),
});
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-flash",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "9.11 和 9.8 哪个大？"}]
}'