Gemini 3.1 Pro

Name: Gemini 3.1 Pro
Brand: Google DeepMind
SKU: gemini-3-1-pro
Price: 2.4 USD
Availability: InStock

MultimodalNyTilgængelig

af Google DeepMindgemini-3-1-pro

Google DeepMind's February 2026 flagship. 1M-token (1,048,576) context, native multimodal (text/image/audio/video), Deep Think reasoning.

Prøv det Få API-nøgle Sammenlign

Pris · 1M ind/ud: 2,40 US$ / 14,40 US$
Kontekst: 1.048.576 tokens
Maks. output: 65.536 tokens
Input → output: Tekst + Billede + Lyd + Video → Tekst
Udvikler: Google DeepMind
Opdateret: 24. september 2026

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Prøv Gemini 3.1 Pro

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2,40 US$/1M in

Send en besked. Svaret ankommer fuldt ud, når modellen er færdig (uden streaming).

Maks. svarslængde (tokens)

TemperaturModelstandard

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Denne kørsel

højst 0,0148 US$ · 1,48 credits reserveret

Fakturering efter faktisk brugte tokens; den ubrugte del af reservationen refunderes.

Ny her?

10 gratis credits (0,10 US$) når du tilmelder dig med Google

Kan bruges 24 timer efter tilmelding, op til 5 kørsler pr. dag og højst 2 credits pr. kørsel. Andre login-metoder starter uden credits. Nok til 6 kørsler af denne model.

Om Gemini 3.1 Pro

Kort sagtFra 24. september 2026

Gemini 3.1 Pro er en model af Google DeepMind i kategorien Multimodal. På Railwail koster Gemini 3.1 Pro 2,40 US$ pr. 1M inputtokens og 14,40 US$ pr. 1M outputtokens. Kontekstvinduet indeholder 1.048.576 tokens, og et svar kan være op til 65.536 tokens langt.

Released February 19, 2026, Gemini 3.1 Pro is Google DeepMind's flagship frontier model. Native 1,048,576-token context window, fully multimodal (text, image, audio, video tokens in a single pass), built-in Deep Think reasoning with adjustable thinking budgets. Top scores on GPQA Diamond, AIME, Humanity's Last Exam, and strong coding on SWE-bench. Function calling, JSON schema, Search grounding and Code Execution are first-class. Best for: long-document and long-video analysis, scientific reasoning, agentic workflows.

Baggrund

Om Google DeepMind

Grundlagt 2010 · Mountain View, USA / London, UK

Google DeepMind is the merged AI research organisation formed in April 2023 by combining Google Brain (founded inside Google in 2011) with DeepMind (founded in London in 2010 by Demis Hassabis, Shane Legg and Mustafa Suleyman, acquired by Google in 2014). Demis Hassabis leads the unit as CEO. DeepMind authored seminal papers including 'Attention Is All You Need' (Google Brain, 2017), AlphaGo (2016), AlphaFold (2018-2021, awarded the 2024 Nobel Prize in Chemistry), AlphaZero, Chinchilla scaling laws and the Gemini Technical Report. The Gemini family launched in December 2023 (Ultra, Pro, Nano), followed by Gemini 1.5 Pro with 1M+ context (early 2024), Gemini 2.0 Flash (December 2024), Gemini 2.5 Pro with Deep Think (March/May 2025), Gemini 3 Pro (late 2025), Gemini 3.1 Pro (February 2026) and Gemini 3 Flash (April 2026). Google DeepMind also ships Imagen, Veo, Lyria and NotebookLM and powers AI features across Google Search, Workspace and Android.

Besøg Google DeepMind

Arkitektur

Sparse Mixture-of-Experts Transformer (natively multimodal, Deep Think reasoning)

Gemini 3.1 Pro was released February 19, 2026 as Google DeepMind's flagship frontier model. It is a natively multimodal Sparse Mixture-of-Experts Transformer that ingests text, image, audio and video tokens through a shared embedding space, building directly on the Gemini 3 Pro architecture (late 2025) and Gemini 2.5 Pro (early 2025). The input context window is 1,048,576 tokens, the same size as Gemini 2.5 Pro. Pretraining used Google's TPU v6e infrastructure on a multi-trillion-token corpus mixing web text, code, books, scientific papers, image-text pairs, audio waveforms and video frames, with a knowledge cutoff in late 2025. Post-training combined supervised fine-tuning, RLHF, reinforcement learning against verifiable rewards on math and code, and a refreshed 'Deep Think' reasoning stage that teaches the model to allocate test-time thinking budgets and emit long internal chains-of-thought before its final answer. Tool use, function calling, structured output, Search grounding and the Code Execution tool are first-class. Safety training followed Google's Frontier Safety Framework v2.

Parametre: Undisclosed (sparse MoE, total parameters in the hundreds of billions, active per-token undisclosed)
Kontekst: 2.000.000 tokens

Funktioner

1,048,576-token input context window with native long-video and long-audio support
Built-in Deep Think reasoning mode with adjustable thinking budget
Natively multimodal: text, image, audio and video in a single pass
Top scores on GPQA Diamond, AIME 2026 and Humanity's Last Exam
Strong coding performance on SWE-bench Verified and LiveCodeBench
Function calling, JSON schema and parallel tool calls
Search grounding and Code Execution tools built into the API
Cross-lingual reasoning across 100+ languages
Available via Vertex AI, AI Studio and the Gemini app (paid-only since April 2026)
2x batch pricing reduction for large jobs
Best for: long-document and long-video analysis, scientific reasoning, agentic workflows, complex multimodal extraction.

Træning og licens

Pretrained on a multi-trillion-token mixture of web text, code, books, scientific papers, licensed third-party text, audio waveforms, image-text pairs and video frames. Knowledge cutoff in late 2025. Post-training uses supervised fine-tuning, RLHF, RL against verifiable rewards and Deep Think reasoning training.

Licens: Proprietary commercial license via Google AI Studio, Vertex AI and the Gemini app. Paid-only since April 1, 2026.

Sikkerhedstests: Evaluated under Google DeepMind's Frontier Safety Framework v2 covering CBRN, cyber, persuasion and ML R&D risks, with internal red teams and external evaluators.

Kendte begrænsninger

Deep Think adds significant latency and token cost
Prompts above 200K tokens are billed at 2x input / 1.5x output rates
Long-context recall quality can degrade beyond ~1M tokens for some tasks
Vision occasionally misreads dense tables and handwriting
Region availability varies; not yet generally available in all EU regions

Forskningsartikler

Priser

Priser i US-dollar. Forbrug debiteres fra forudbetalte credits.

Input	2,40 US$ / 1M tokens
Output	14,40 US$ / 1M tokens
Input (prompts over 200.000 tokens)	4,80 US$ / 1M tokens
Output (prompts over 200.000 tokens)	21,60 US$ / 1M tokens

Faktureres efter de tokens, som hver anmodning faktisk bruger.
1 kredit = 0,01 US$

Omkostningsberegner

Prisberegner

Input-tokens

/ anmodning

Output-tokens

/ anmodning

Anmodninger

I alt

0,96 US$

96 credits

Pr. anmodning

0,0096 US$ · 0,96 credits

Hver anmodning rundes op til 0,01 credits.

API

Kald Gemini 3.1 Pro med din Railwail API-nøgle. Brug dette model-ID i anmodningen:

gemini-3-1-proAPI-dokumentation Få en API-nøgle

curl https://railwail.com/api/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3-1-pro",
    "messages": [
      {
        "role": "user",
        "content": "Explain what a vector database is in two sentences."
      }
    ],
    "max_tokens": 1024
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/api/v1",
)

completion = client.chat.completions.create(
    model="gemini-3-1-pro",
    messages=[
        {
            "role": "user",
            "content": "Explain what a vector database is in two sentences.",
        },
    ],
    max_tokens=1024,
)

print(completion.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/api/v1",
});

const completion = await client.chat.completions.create({
  model: "gemini-3-1-pro",
  messages: [
    {
      role: "user",
      content: "Explain what a vector database is in two sentences."
    }
  ],
  max_tokens: 1024
});

console.log(completion.choices[0].message.content);

// npm install railwail
import railwail from "railwail";

const rw = railwail(process.env.RAILWAIL_API_KEY);

const res = await rw.chat("gemini-3-1-pro", [
  { role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });

console.log(res.choices[0].message.content);

Indstil din nøgle som RAILWAIL_API_KEYOpret API-nøgle

Specifikationer

Model-ID: gemini-3-1-pro
Udvikler: Google DeepMind
Kategori: Multimodal
Input: Tekst, Billede, Lyd, Video
Output: Tekst
Kontekstvindue: 1.048.576 tokens
Maks. output: 65.536 tokens
Fakturering: Efter forbrug (tokens eller GPU-tid)
Modelstørrelse: Undisclosed (sparse MoE, total parameters in the hundreds of billions, active per-token undisclosed)
Licens: Proprietary commercial license via Google AI Studio, Vertex AI and the Gemini app. Paid-only since April 1, 2026.
Katalogelement opdateret: 24. september 2026

Inputparametre

Inputs og indstillinger fra modellens inputskema. Eksemplet i API-afsnittet viser, hvilke af dem API'en accepterer.

ParameterTypeStandardTilladte værdier

promptpåkrævet
User message
Type: Tekst
Standard: –
Tilladte værdier: op til 32.000 tegn
top_p
Type: Tal
Standard: 0.95
Tilladte værdier: 0 til 1
stream
Type: Ja/Nej
Standard: false
Tilladte værdier: –
image_url
Optional image URL to analyze
Type: Tekst
Standard: –
Tilladte værdier: –
max_tokens
Type: Heltal
Standard: 4096
Tilladte værdier: 1 til 32.000
temperature
Type: Tal
Standard: 1
Tilladte værdier: 0 til 2
system_prompt
Optional system instruction
Type: Tekst
Standard: –
Tilladte værdier: op til 8.000 tegn

Anvendelsestilfælde

Hvad det bruges til

Long-video and long-PDF analysis
Scientific research and literature review
Coding agents with Deep Think reasoning
Search-grounded enterprise chatbots
Multimodal data extraction at scale
Audio transcription and analysis with reasoning
Whole-codebase refactoring and migration

Alle modeller i Multimodal

Ofte stillede spørgsmål

Hvad er Gemini 3.1 Pro?

Gemini 3.1 Pro er en model fra Google DeepMind i kategorien Multimodal. På Railwail kan du kalde den med en API-nøgle via Railwail API.

Hvad koster Gemini 3.1 Pro på Railwail?

På Railwail koster Gemini 3.1 Pro 2,40 US$ pr. 1M inputtokens og 14,40 US$ pr. 1M outputtokens. Du betaler for det, som hver anmodning faktisk bruger. Forbrug betales fra forudbetalte credits; 1 credit svarer til 0,01 US$.

Hvad er kontekstvinduet for Gemini 3.1 Pro?

Kontekstvinduet for Gemini 3.1 Pro indeholder 1.048.576 tokens. Et svar kan være op til 65.536 tokens langt.

Hvor hurtig er Gemini 3.1 Pro?

Der er endnu ikke nok målte kørsler af Gemini 3.1 Pro på Railwail til at angive en udførelsestid. Det afhænger af inputtet, indstillingerne og belastningen hos provideren.

Er Gemini 3.1 Pro bedre end BLIP?

Det afhænger af opgaven. Gemini 3.1 Pro (Google DeepMind) og BLIP (Salesforce) er begge modeller i kategorien Multimodal. Sammenligningssiden viser deres priser og specifikationer side om side.

Sammenlign Gemini 3.1 Pro og BLIP

Kan Gemini 3.1 Pro behandle billeder?

Ja. Gemini 3.1 Pro accepterer billeder som input ud over tekst.

Hvordan bruger jeg Gemini 3.1 Pro via API'en?

Opret en Railwail API-nøgle og send din anmodning med model-ID'en gemini-3-1-pro. Kodeeksempler til curl, Python og JavaScript findes i API-sektionen på denne side.

Sammenlignelige modeller

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Sammenlign Gemini 3.1 Pro og Claude Opus 4.7
Claude Sonnet 4.6Anthropic
Anthropic's balanced mid-tier model from February 2026. Best price/performance for production workloads: 5x cheaper than Opus, near-flagship quality.
3,60 US$/1M in
50 % dyrere pr. enhed
Sammenlign Gemini 3.1 Pro og Claude Sonnet 4.6
Gemini 3 FlashGoogle DeepMind
Google's April 2026 fast multimodal model. Combines Gemini 3 Pro's reasoning with Flash-tier latency and price. Default model in the Gemini app.
0,60 US$/1M in
75 % billigere pr. enhed
Sammenlign Gemini 3.1 Pro og Gemini 3 Flash

Brug Gemini 3.1 Pro via API'en

En API-nøgle til alle modeller på Railwail. Forbrug debiteres fra forudbetalte credits, 1 credit = 0,01 US$.

Få API-nøgle Se alle modeller