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Gemini 3.1 Pro

輸入:$1.6/M
輸出:$9.6/M
發布時間:Feb 18, 2026

Gemini 3.1 Pro is the next generation in the Gemini series of models, a suite of highly-capable, natively multimodal, reasoning models. Gemini 3 Pro is now Google’s most advanced model for complex tasks, and can comprehend vast datasets, challenging problems from different information sources, including text, audio, images, video, and entire code repositories

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商業用途

Gemini 3.1 Pro 的 Playground

探索 Gemini 3.1 Pro 的 Playground — 一個互動式環境，可測試模型並即時執行查詢。嘗試提示、調整參數，並立即迭代以加速開發並驗證使用案例。

Technical specifications — Gemini 3.1 Pro

Itemgemini-3-pro (public summary)
ProviderGoogle
Canonical model idgemini-3-pro (public preview)
Input typesText, Image, Video, Audio, PDF
Output typesText (natural language, structured outputs, function-call payloads)
Input token limit (context)1,048,576 tokens
Output token limit65,536 tokens
Function-calling / tool useSupported (function calling, structured outputs, tool integrations)
MultimodalityFull multimodal support (images, video, audio, documents)
Code execution & agentic flowsSupported (agent mode, code assist, tool orchestration)
Knowledge cutoffJanuary 2025

What is Gemini 3.1 Pro ?

Gemini 3.1 Pro is Google’s publicly flagship in the Gemini 3 family, positioned as a state-of-the-art multimodal reasoning model with advanced agentic and developer tooling. The model emphasizes high-capacity context handling (over 1M token inputs), broad media support (images, video, audio, PDF), and deep integrations for tool use, function calling, and code-centric workflows (e.g., Gemini Code Assist and agent modes).

Gemini 3 Pro is presented by Google as optimized for both interactive developer experiences (low-latency coding and agent workflows) and high-fidelity multimodal understanding (interpreting and reasoning across mixed media inputs).

Main Features of Gemini 3.1 Pro

Gemini-3.1 Pro (via its Preview) introduces the following features:

Multimodal Integration

Processes inputs across:

  • Natural language
  • Images
  • Speech/audio
  • Video

with a unified token representation for cross-modal reasoning.

Extended Context Window

An exceptionally large context capacity of up to ~1 million tokens enables handling of:

  • Long documents
  • Multidocument synthesis
  • Codebases and transcripts.

This surpasses many competing models that typically support ~32 K–262 K tokens.

Sparse Mixture-of-Experts (MoE) Scaling

Sparse MoE routing allows scaling internal model capacity without proportional compute costs, improving reasoning at scale.

Advanced Reasoning / Planning

Innovations like chain-of-thought training, reinforcement learning from human feedback, and specialized benchmarks make it strong on logical and mathematical tasks.

Supposed benchmarks:

AIME 2025: 100% (with code execution)
SWE-Bench Verified: 83.9%
ARC-AGI-2: 71.8%
LiveCodeBench Pro: 2844 Elo
Terminal-Bench 2.0: 63.5%
MMMLU: 93.6%

Representative enterprise use cases

  • End-to-end media pipelines: Ingest video, transcript, and images to produce synchronized summaries, metadata, and structured insight at scale.
  • Large-scale code generation and review: Use in IDEs and CI pipelines to auto-generate code, refactor multi-file projects, and produce test suggestions across large codebases.
  • Agentic automation: Coordinate multi-tool agents that interact with cloud services, orchestration systems, and internal APIs using structured function calls.
  • Research & content production: Draft long-form content (reports, books) that combine text and embedded multimedia with internal cross-references preserved.

How to access Gemini 3.1 Pro API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests to Gemini 3.1 Pro API

Select the “gemini-3.1-pro” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. base url is Gemini Generating Content and Chat.

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

See also Gemini 3 Pro API

常見問題

Gemini 3.1 Pro 的定價

探索 Gemini 3.1 Pro 的競爭性定價，專為滿足各種預算和使用需求而設計。我們靈活的方案確保您只需為實際使用量付費，讓您能夠隨著需求增長輕鬆擴展。了解 Gemini 3.1 Pro 如何在保持成本可控的同時提升您的專案效果。

ModelComet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
gemini-3.1-pro-preview
輸入:$1.6/M
輸出:$9.6/M
輸入:$2/M
輸出:$12/M
-20%

Gemini 3.1 Pro 的範例程式碼和 API

存取完整的範例程式碼和 API 資源，以簡化您的 Gemini 3.1 Pro 整合流程。我們詳盡的文件提供逐步指引，協助您在專案中充分發揮 Gemini 3.1 Pro 的潛力。

curl "https://api.cometapi.com/v1beta/models/gemini-3.1-pro-preview:generateContent" \
  -H "Authorization: $COMETAPI_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "Explain how AI works in a few words"
          }
        ]
      }
    ]
  }'

cURL Code Example

curl "https://api.cometapi.com/v1beta/models/gemini-3.1-pro-preview:generateContent" \
  -H "Authorization: $COMETAPI_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "Explain how AI works in a few words"
          }
        ]
      }
    ]
  }'

Python Code Example

from google import genai
import os

# Get your CometAPI key from https://www.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com"

client = genai.Client(
    http_options={"api_version": "v1beta", "base_url": BASE_URL},
    api_key=COMETAPI_KEY,
)

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="Explain how AI works in a few words",
)

print(response.text)

JavaScript Code Example

// Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
const api_key = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const base_url = "https://api.cometapi.com/v1beta";
const model = "gemini-3.1-pro-preview";
const operator = "generateContent";

async function main() {
  const response = await fetch(`${base_url}/models/${model}:${operator}`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: api_key,
    },
    body: JSON.stringify({
      contents: [
        {
          parts: [{ text: "Explain how AI works in a few words" }],
        },
      ],
    }),
  });

  const data = await response.json();
  console.log(data.candidates[0].content.parts[0].text);
}

await main();

Gemini 3.1 Pro的版本

Gemini 3.1 Pro擁有多個快照的原因可能包括：更新後輸出結果存在差異需保留舊版快照以確保一致性、為開發者提供適應與遷移的過渡期，以及不同快照對應全球或區域端點以優化使用者體驗等潛在因素。各版本間的具體差異請參閱官方文件說明。

Version
gemini-3.1-pro-preview