Gemini 2.5 Pro

Name: Gemini 2.5 Pro
Brand: Google DeepMind
SKU: gemini-2-5-pro
Price: 1.5 USD
Availability: InStock

Text & ChatÄltere VersionVerfügbar

von Google DeepMindgemini-2-5-pro

Google's latest thinking model. Excels at reasoning, coding, math, and science with massive context window.

Ausprobieren API-Schlüssel holen Vergleichen

Preis · 1 Mio. In/Out: $ 1.50 / $ 12.00
Kontext: 1'000'000 Token
Max. Ausgabe: 65'536 Token
Eingabe → Ausgabe: Text → Text
Entwickler: Google DeepMind
Aktualisiert: 23. September 2026

Dies ist eine ältere Version des Modells.

Playground

Gemini 2.5 Pro ausprobieren

Chat

$ 1.50/1 Mio. In

Schick eine Nachricht. Die Antwort kommt vollständig, sobald das Modell fertig ist (ohne Streaming).

Max. Antwortlänge (Token)

⌘/Strg + Enter

Dieser Lauf

höchstens $ 0.0123 · 1.23 Credits vorgemerkt

Abgerechnet werden die tatsächlich verbrauchten Token, der Rest der Vormerkung wird erstattet.

Neu hier?

10 Gratis-Credits ($ 0.10) bei Anmeldung mit Google

Nutzbar 24 Stunden nach der Anmeldung, bis zu 5 Läufe pro Tag und höchstens 2 Credits je Lauf. Andere Anmeldearten starten ohne Guthaben. Reicht für 8 Läufe dieses Modells.

Über Gemini 2.5 Pro

Kurz gesagtStand: 23. September 2026

Gemini 2.5 Pro ist ein Modell von Google DeepMind aus der Kategorie Text & Chat. Über Railwail kostet Gemini 2.5 Pro $ 1.50 je 1 Mio. Input-Token und $ 12.00 je 1 Mio. Output-Token. Das Kontextfenster umfasst 1'000'000 Token, eine Antwort bis zu 65'536 Token.

Hintergrund

Über Google DeepMind

Gegründet 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 and the Mixture-of-Experts architecture in early 2024, Gemini 2.0 Flash (December 2024) and Gemini 2.5 Pro (released March 2025, generally available May 2025) which introduced a built-in 'Deep Think' reasoning mode. Google DeepMind also ships Imagen, Veo, Lyria, NotebookLM and powers AI features across Google Search, Workspace and Android.

Google DeepMind besuchen

Architektur

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

Gemini 2.5 Pro is Google DeepMind's flagship reasoning model released as preview in March 2025 and generally available in May 2025. It is a natively multimodal Sparse Mixture-of-Experts (MoE) Transformer that ingests text, image, audio and video tokens through a shared embedding space, building on the architecture introduced in Gemini 1.5 Pro. Pretraining used Google's TPU v5p infrastructure on a multi-trillion-token corpus mixing web text, code, books, scientific papers, image-text pairs, audio and YouTube video frames, with a knowledge cutoff of January 2025. Post-training combined supervised fine-tuning, RLHF and reinforcement learning against verifiable rewards for math and code, plus a new 'Deep Think' training stage that teaches the model to allocate test-time thinking budgets and emit long internal chains-of-thought before the final answer. Deep Think is exposed to developers as a toggle and was the headline capability at launch, pushing Gemini 2.5 Pro to the top of the Chatbot Arena leaderboard for the first time for Google. The model supports a 1,048,576-token context window with native handling of long PDFs, hour-long videos and full code repositories. Tool use, function calling, structured output and grounding with Google Search are first-class. Safety training included red-teaming under Google's Frontier Safety Framework.

Parameter: Undisclosed (sparse MoE, total parameters in the hundreds of billions, active per-token undisclosed)
Kontext: 1'048'576 Token

Fähigkeiten

1,048,576 token context window (~1M tokens) 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 2025 and Humanity's Last Exam
Strong coding performance on SWE-bench Verified and LiveCodeBench
Function calling, JSON schema, parallel tool calls
Grounding with Google Search built into the API
Native execution of code via the Code Execution tool
Cross-lingual reasoning across 100+ languages
Available via Vertex AI, AI Studio, and the Gemini app
Best for: scientific reasoning, long-document and long-video analysis, coding, agentic workflows.

Training & Lizenz

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 is January 2025. Post-training uses supervised fine-tuning, RLHF, RL against verifiable rewards and Deep Think reasoning training.

Lizenz: Proprietary commercial license via Google AI Studio, Vertex AI and the Gemini app. Commercial use permitted under Google Cloud terms.

Sicherheitstests: 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.

Bekannte Grenzen

Deep Think adds significant latency and token cost
Long context recall quality degrades beyond ~500K tokens for some tasks
Vision occasionally misreads dense tables and handwriting
Knowledge cutoff January 2025
Region availability varies and is not yet in all EU regions for some features

Fachartikel

Preise

Preise in US-Dollar. Abgerechnet wird über vorab gekaufte Credits.

Eingabe	$ 1.50 / 1 Mio. Token
Ausgabe	$ 12.00 / 1 Mio. Token
Eingabe (Prompts über 200'000 Token)	$ 3.00 / 1 Mio. Token
Ausgabe (Prompts über 200'000 Token)	$ 18.00 / 1 Mio. Token

Abgerechnet werden die Token, die jede Anfrage tatsächlich verbraucht.
1 Credit = $ 0.01

Kostenrechner

Preisrechner

Input-Token

/ Anfr.

Output-Token

/ Anfr.

Anfragen

Gesamt

$ 0.75

75 Credits

Je Anfrage

$ 0.0075 · 0.75 Credits

Jede Anfrage wird auf 0,01 Credits aufgerundet.

API

Rufe Gemini 2.5 Pro mit deinem Railwail-API-Schlüssel auf. Diese Modell-ID gehört in die Anfrage:

gemini-2-5-proAPI-Dokumentation API-Schlüssel holen

curl https://railwail.com/api/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2-5-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-2-5-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-2-5-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-2-5-pro", [
  { role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });

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

Schlüssel als RAILWAIL_API_KEY setzenAPI-Schlüssel erstellen

Spezifikationen

Modell-ID: gemini-2-5-pro
Entwickler: Google DeepMind
Kategorie: Text & Chat
Eingabe: Text
Ausgabe: Text
Kontextfenster: 1'000'000 Token
Max. Ausgabe: 65'536 Token
Abrechnung: Nach Verbrauch (Token bzw. GPU-Zeit)
Lebenszyklus: Ältere Version
Modellgrösse: Undisclosed (sparse MoE, total parameters in the hundreds of billions, active per-token undisclosed)
Lizenz: Proprietary commercial license via Google AI Studio, Vertex AI and the Gemini app. Commercial use permitted under Google Cloud terms.
Katalogeintrag aktualisiert: 23. September 2026

Beispiel-Prompts

Beispiele aus dem Railwail-Katalog. Sie wurden nicht live auf dieser Seite erzeugt.

Scientific Analysis
Explain CRISPR-Cas9 gene editing: how it works, current medical applications, and ethical considerations
Beispielantwort anzeigen
CRISPR-Cas9 works like molecular scissors guided by a GPS. A guide RNA is designed to match a specific DNA sequence; the Cas9 protein then cuts the DNA at that exact location, allowing scientists to delete, repair, or insert genetic material. Current medical applications include sickle cell disease treatment (Casgevy, approved 2023), where patients' stem cells are edited ex vivo to produce functional hemoglobin. Clinical trials are underway for certain cancers, hereditary blindness, and HIV resistance. Ethical considerations center on germline editing (changes passed to future generations), equitable access to expensive therapies, and the slippery slope toward 'designer babies'—most scientists support a moratorium on heritable human genome editing while governance frameworks catch up.
Long Context Analysis
I have a 200-page legal contract. Identify all clauses related to intellectual property ownership, data retention, and termination conditions.
Beispielantwort anzeigen
I can process your entire 200-page contract within my context window. Here's what I would systematically extract: 1) IP Ownership clauses: Look for sections titled 'Intellectual Property,' 'Work Product,' or 'Ownership of Deliverables'—these define who owns code, designs, and inventions created during the engagement. 2) Data Retention: Sections covering 'Data Handling,' 'Confidentiality,' or 'Information Security' typically specify how long each party retains data post-termination, deletion timelines, and compliance with regulations like GDPR. 3) Termination Conditions: Usually in a dedicated 'Termination' section, covering for-cause vs. for-convenience termination, notice periods, cure periods for breaches, and post-termination obligations. Upload the document and I'll provide exact clause references, page numbers, and a summary of each relevant provision with any potential conflicts between sections.

Einsatzgebiete

Wofür es genutzt wird

Long-video and long-PDF analysis
Scientific research and literature review
Coding agents with Deep Think
Search-grounded chatbots
Multimodal data extraction
Enterprise document understanding

Passende Übersichten

Best Large Language Models

Alle Modelle: Text & Chat

Häufige Fragen

Was ist Gemini 2.5 Pro?

Gemini 2.5 Pro ist ein Modell von Google DeepMind aus der Kategorie Text & Chat. Über Railwail lässt es sich mit einem API-Schlüssel über die Railwail-API aufrufen.

Was kostet Gemini 2.5 Pro bei Railwail?

Über Railwail kostet Gemini 2.5 Pro $ 1.50 je 1 Mio. Input-Token und $ 12.00 je 1 Mio. Output-Token. Abgerechnet wird, was jede Anfrage tatsächlich verbraucht. Bezahlt wird mit vorab gekauften Credits; 1 Credit entspricht $ 0.01.

Wie gross ist das Kontextfenster von Gemini 2.5 Pro?

Das Kontextfenster von Gemini 2.5 Pro umfasst 1'000'000 Token. Eine Antwort kann bis zu 65'536 Token lang sein.

Wie schnell ist Gemini 2.5 Pro?

Für Gemini 2.5 Pro gibt es bei Railwail noch zu wenige gemessene Läufe, um eine Laufzeit anzugeben. Sie hängt von der Eingabe, den Einstellungen und der Auslastung beim Anbieter ab.

Ist Gemini 2.5 Pro besser als Claude Fable 5.1?

Das hängt von der Aufgabe ab. Gemini 2.5 Pro (Google DeepMind) und Claude Fable 5.1 (Anthropic) sind beide Modelle aus der Kategorie Text & Chat. Die Vergleichsseite zeigt Preise und Spezifikationen nebeneinander.

Gemini 2.5 Pro und Claude Fable 5.1 vergleichen

Wie nutze ich Gemini 2.5 Pro über die API?

Erstelle einen Railwail-API-Schlüssel und sende deine Anfrage mit der Modell-ID gemini-2-5-pro. Codebeispiele für curl, Python und JavaScript stehen im Abschnitt API auf dieser Seite.

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Gemini 2.5 Pro über die API nutzen

Ein API-Schlüssel für alle Modelle auf Railwail. Abgerechnet wird über vorab gekaufte Credits, 1 Credit = $ 0.01.

API-Schlüssel holen Alle Modelle ansehen