GPT-4o

Name: GPT-4o
Brand: OpenAI
SKU: gpt-4o
Price: 3 USD
Availability: InStock

Text & chatAvailable

by OpenAIgpt-4o

OpenAI's most capable multimodal model. Excellent for complex reasoning, coding, and creative tasks.

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Price · 1M in / out: $3.00 / $12.00
Context: 128,000 tokens
Max. output: 16,384 tokens
Input → output: Text → Text
Developer: OpenAI
Updated: September 23, 2026

Playground

Try GPT-4o

Chat

$3.00/1M in

Send a message. The answer arrives in full once the model is done (no streaming).

System prompt

Max. answer length (tokens)

⌘/Ctrl + Enter

This run

at most $0.0123 · 1.23 credits reserved

Billed by the tokens actually used; the unused part of the reservation is refunded.

New here?

10 free credits ($0.10) when you sign up with Google

Usable 24 hours after sign-up, up to 5 runs per day and at most 2 credits per run. Other sign-in methods start without credits. Enough for 8 runs of this model.

About GPT-4o

TL;DRAs of September 23, 2026

GPT-4o is a model by OpenAI in the Text & chat category. On Railwail, GPT-4o costs $3.00 per 1M input tokens and $12.00 per 1M output tokens. The context window holds 128,000 tokens, and one response can be up to 16,384 tokens long.

Background

About OpenAI

Founded 2015 · San Francisco, USA

OpenAI was founded in December 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, Wojciech Zaremba and John Schulman as a non-profit AI research lab with a $1B pledged commitment. The lab restructured into a capped-profit company (OpenAI LP) in 2019 to attract capital from Microsoft, which has invested over $13 billion. OpenAI's most-cited papers include 'Improving Language Understanding by Generative Pre-Training' (GPT-1, 2018), 'Language Models are Few-Shot Learners' (GPT-3, 2020), the GPT-4 Technical Report (2023) and the InstructGPT/RLHF paper (2022). The ChatGPT consumer product, launched in November 2022, reached 100M weekly active users faster than any consumer software in history. Product milestones include GPT-3.5, GPT-4 (March 2023), GPT-4 Turbo, GPT-4o (May 2024), the o1/o3 reasoning family (late 2024/2025), GPT-4.1 (April 2025) and the Sora video model. Sam Altman is CEO. The company's last reported valuation in 2025 exceeded $300 billion.

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Architecture

Decoder-only Transformer (natively multimodal)

GPT-4o (the 'o' stands for 'omni') was released in May 2024 as OpenAI's first natively multimodal flagship: a single neural network trained end-to-end on text, audio and images, replacing the earlier cascade of separate ASR, LLM and TTS pipelines used by ChatGPT Voice. Inputs and outputs can be any combination of text, audio and image tokens, and the model emits audio with a median response latency of 320ms for spoken conversation. Architecturally it is a decoder-only Transformer with a unified tokenizer that mixes BPE text tokens, image patches and discretised audio frames. Pretraining used a multi-trillion-token web-scale corpus filtered for quality, plus large image-text pair datasets and licensed audio. Post-training applied RLHF with human raters, model-graded rewards and red-teaming under OpenAI's Preparedness Framework. GPT-4o introduced a new tokenizer with substantially better compression for non-English scripts, reducing token counts by 1.1x-4.4x across languages such as Hindi, Arabic, Korean and Tamil. The context window is 128K tokens with 16K maximum output. Function calling, JSON mode, vision input and Structured Outputs (with strict schema adherence) are first-class. The model card discloses Preparedness Framework evaluations against CBRN, cyber and persuasion risks.

Parameters: Undisclosed (estimated in the hundreds of billions, dense)
Context: 128,000 tokens

Capabilities

Natively multimodal: text, image and audio in and out
Real-time voice conversation with sub-second latency
128K context window with 16K max output
Improved non-English tokenizer (up to 4.4x fewer tokens for some languages)
Vision: charts, diagrams, screenshots, OCR
Function calling and parallel tool calls
Structured Outputs with strict JSON schema
Strong coding performance with file editing via tools
Singing, emotional speech and laughter in audio mode
Multilingual fluency across 50+ languages
Best for: real-time voice agents, multimodal assistants, general-purpose chat, vision tasks.

Training & license

Trained on a multi-trillion-token mixture of web text, code, books, licensed third-party data and large image-text and audio datasets. Knowledge cutoff is October 2023. OpenAI does not disclose exact dataset composition.

License: Proprietary, available via OpenAI API and Azure OpenAI Service. Commercial use allowed under OpenAI Terms of Use.

Safety testing: Evaluated under OpenAI's Preparedness Framework across CBRN, cyber, persuasion and model autonomy risks. External red-teamers included over 100 experts in chemistry, biology, security and misinformation.

Known limitations

Can hallucinate citations and factual details
Voice mode sometimes mimics user voice unexpectedly
Knowledge cutoff October 2023 without tools
Performance on hardest reasoning trails o-series models
Audio output is not available in all API regions

Research papers

Pricing

Prices in US dollars. Usage is charged from prepaid credits.

Input	$3.00 / 1M tokens
Output	$12.00 / 1M tokens

Billed by the tokens each request actually uses.
1 credit = $0.01

Cost calculator

Price calculator

Input tokens

/ req.

Output tokens

/ req.

Requests

Total

$0.90

90 credits

Per request

$0.009 · 0.9 credits

Each request is rounded up to 0.01 credits.

API

Call GPT-4o with your Railwail API key. Use this model ID in the request:

gpt-4oAPI documentation Get an API key

curl https://railwail.com/api/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "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="gpt-4o",
    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: "gpt-4o",
  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("gpt-4o", [
  { role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });

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

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Specifications

Model ID: gpt-4o
Developer: OpenAI
Category: Text & chat
Input: Text
Output: Text
Context window: 128,000 tokens
Max. output: 16,384 tokens
Billing: By usage (tokens or GPU time)
Model size: Undisclosed (estimated in the hundreds of billions, dense)
License: Proprietary, available via OpenAI API and Azure OpenAI Service. Commercial use allowed under OpenAI Terms of Use.
Catalog entry updated: September 23, 2026

Example prompts

Examples from the Railwail catalog. They were not generated live on this page.

Creative Writing
Write a haiku about the first snowfall of winter
Show example answer
Silent flakes descend, blanketing the sleeping earth— winter whispers soft.
Complex Analysis
Explain the difference between supervised and unsupervised machine learning in simple terms
Show example answer
Supervised learning is like studying with an answer key—you feed the model labeled examples so it learns to predict the correct output. Unsupervised learning is more like exploring a dataset without labels, where the model discovers hidden patterns, groupings, or structures on its own. Think of it as the difference between a teacher grading your homework versus sorting a pile of unlabeled photos into albums by similarity.

Use cases

What it is used for

Real-time voice assistants
Multimodal customer support
Vision-based document analysis
Coding copilots with tool use
Live translation
Accessibility tools

Related guides

Best Large Language Models

All models in Text & chat

Frequently asked questions

What is GPT-4o?

GPT-4o is a model by OpenAI in the Text & chat category. On Railwail you can call it with an API key through the Railwail API.

How much does GPT-4o cost on Railwail?

On Railwail, GPT-4o costs $3.00 per 1M input tokens and $12.00 per 1M output tokens. You are charged for what each request actually uses. Usage is paid from prepaid credits; 1 credit equals $0.01.

What is the context window of GPT-4o?

The context window of GPT-4o holds 128,000 tokens. One response can be up to 16,384 tokens long.

How fast is GPT-4o?

There are not enough measured runs of GPT-4o on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is GPT-4o better than Claude Fable 5.1?

That depends on the task. GPT-4o (OpenAI) and Claude Fable 5.1 (Anthropic) are both models in the Text & chat category. The comparison page shows their prices and specifications side by side.

Compare GPT-4o and Claude Fable 5.1

How do I use GPT-4o through the API?

Create a Railwail API key and send your request with the model ID gpt-4o. Code examples for curl, Python and JavaScript are in the API section of this page.

Comparable models

All in this category

Claude Fable 5.1Anthropic
Anthropic's model for the most demanding reasoning and long-horizon agentic work. 1M-token context window, up to 128K output tokens, adaptive thinking that is always on.
$12.00/1M in
300 % more expensive per unit
Compare GPT-4o vs. Claude Fable 5.1
Claude Opus 4.8Anthropic
The most capable model of Anthropic's Opus 4 series. State of the art on long-horizon agentic work, coding and knowledge tasks, with a 1M-token context window at standard pricing.
$6.00/1M in
100 % more expensive per unit
Compare GPT-4o vs. Claude Opus 4.8
Claude Opus 5.5Anthropic
Anthropic's current Opus model for long-running agentic coding and knowledge work. 1M-token context window, up to 128K output tokens, adaptive thinking that is always on.
$4.80/1M in
60 % more expensive per unit
Compare GPT-4o vs. Claude Opus 5.5

Use GPT-4o via the API

One API key for every model on Railwail. Usage is charged from prepaid credits, 1 credit = $0.01.

Get API key Browse all models