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Gemini 3.1 Pro

입력:$1.6/M
출력:$9.6/M
출시일: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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Gemini 3.1 Pro의 Playground

Gemini 3.1 Pro의 Playground를 탐색하세요 — 모델을 테스트하고 실시간으로 쿼리를 실행하는 대화형 환경입니다. 프롬프트를 시도하고, 매개변수를 조정하며, 즉시 반복하여 개발을 가속화하고 사용 사례를 검증하세요.

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

자주 묻는 질문

Gemini 3.1 Pro 가격

[모델명]의 경쟁력 있는 가격을 살펴보세요. 다양한 예산과 사용 요구에 맞게 설계되었습니다. 유연한 요금제로 사용한 만큼만 지불하므로 요구사항이 증가함에 따라 쉽게 확장할 수 있습니다. [모델명]이 비용을 관리 가능한 수준으로 유지하면서 프로젝트를 어떻게 향상시킬 수 있는지 알아보세요.

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

Gemini 3.1 Pro의 샘플 코드 및 API

[모델 이름]의 포괄적인 샘플 코드와 API 리소스에 액세스하여 통합 프로세스를 간소화하세요. 자세한 문서는 단계별 가이드를 제공하여 프로젝트에서 [모델 이름]의 모든 잠재력을 활용할 수 있도록 돕습니다.

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 Pro의 버전

Gemini 3.1 Pro에 여러 스냅샷이 존재하는 이유는 업데이트 후 출력 변동으로 인해 일관성을 유지하기 위해 이전 스냅샷을 보관하거나, 개발자에게 적응 및 마이그레이션을 위한 전환 기간을 제공하거나, 글로벌 또는 지역별 엔드포인트에 따라 다양한 스냅샷을 제공하여 사용자 경험을 최적화하기 위한 것 등이 포함될 수 있습니다. 버전 간 상세한 차이점은 공식 문서를 참고해 주시기 바랍니다.

Version
gemini-3.1-pro-preview