GPT-4o Mini

Name: GPT-4o Mini
Brand: OpenAI
SKU: gpt-4o-mini
Price: 0.18 USD
Availability: InStock

Text & chatAvailable

by OpenAIgpt-4o-mini

Small, fast, and affordable model for lightweight tasks. Great balance of speed and capability.

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Price · 1M in / out: $0.18 / $0.72
Context: 128,000 tokens
Max. output: 16,384 tokens
Input → output: Text → Text
Run time (median): 3.6 s
Developer: OpenAI

Playground

Try GPT-4o Mini

Chat

$0.18/1M 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 $0.0008 · 0.08 credits reserved

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

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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 125 runs of this model.

About GPT-4o Mini

TL;DRAs of September 23, 2026

GPT-4o Mini is a model by OpenAI in the Text & chat category. On Railwail, GPT-4o Mini costs $0.18 per 1M input tokens and $0.72 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 as a non-profit AI research lab by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, Wojciech Zaremba and John Schulman. In 2019 it transitioned to a capped-profit structure (OpenAI LP) to take Microsoft investment, now totalling over $13 billion. Foundational publications include the GPT series papers (GPT-1 through GPT-4), the InstructGPT/RLHF paper (2022) and the GPT-4o System Card (2024). The company ships ChatGPT (launched November 2022), the GPT API, the o-series reasoning models, Sora for video, DALL-E for images and Whisper for speech recognition. Sam Altman remains CEO; Greg Brockman is President. OpenAI's 2025 valuation exceeded $300 billion and the company employs more than 1,500 staff in San Francisco, London, Tokyo, Dublin and other offices. The mission statement focuses on building safe and beneficial AGI for all of humanity.

Visit OpenAI

Architecture

Decoder-only Transformer (small natively multimodal)

GPT-4o mini was announced in July 2024 as a low-cost, fast replacement for GPT-3.5 Turbo. It is a small natively multimodal Transformer derived from the same training stack as GPT-4o, supporting text and vision input with text output. The model was trained on a multi-trillion-token web-scale corpus including code, books, licensed text and image-text pairs, with a knowledge cutoff of October 2023. Post-training combined supervised fine-tuning with RLHF and direct preference optimisation on smaller curated datasets. OpenAI applied an 'instruction hierarchy' training method to better resist jailbreaks and prompt injections by distinguishing system, developer and user instructions during alignment. At launch GPT-4o mini scored 82% on MMLU and outperformed GPT-3.5 Turbo on the Chatbot Arena leaderboard while costing approximately 60% less. It supports the full GPT-4o feature set including function calling, parallel tool calls, JSON mode, Structured Outputs and vision input. The model is the default backbone for ChatGPT Free as of mid-2024 and is widely used as a router and cheap tool-calling layer in agentic systems. Fine-tuning was opened to developers in late 2024 with both supervised fine-tuning and reinforcement fine-tuning options on the API.

Parameters: Undisclosed (estimated ~8B-20B parameters dense)
Context: 128,000 tokens

Capabilities

Very low cost per token (around $0.15 input / $0.60 output per 1M tokens at launch)
128K context window with 16K max output
Vision input for images and PDFs
Function calling and parallel tool calls
Structured Outputs with strict JSON schema
82% MMLU at launch (better than GPT-3.5 Turbo)
Trained with instruction hierarchy to resist prompt injection
Supervised and reinforcement fine-tuning available
Fast time-to-first-token for chat workloads
Multilingual coverage but optimised for English
Best for: cheap chatbots, classification, large-scale data labeling, agent routers, customer support.

Training & license

Pretrained on OpenAI's curated multi-trillion-token mixture of web text, code, books and image-text pairs, with a knowledge cutoff of October 2023. Post-training uses supervised fine-tuning, RLHF and the instruction-hierarchy alignment objective.

License: Proprietary, accessible via OpenAI API and Azure OpenAI Service. Commercial use permitted under OpenAI Terms.

Safety testing: Evaluated under OpenAI's Preparedness Framework and System Card process; smaller than GPT-4o so deemed low risk for CBRN uplift but tested against persuasion, cyber and prompt-injection threats.

Known limitations

Weaker reasoning than full GPT-4o on hard math and multi-step problems
Higher hallucination rate than larger flagship models
Knowledge cutoff October 2023
No audio input or output
Vision quality below full GPT-4o on complex charts

Research papers

Pricing

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

Input	$0.18 / 1M tokens
Output	$0.72 / 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

$0.06

6 credits

Per request

$0.0006 · 0.06 credits

Each request is rounded up to 0.01 credits.

API

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

gpt-4o-miniAPI 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-mini",
    "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-mini",
    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-mini",
  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-mini", [
  { role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });

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

Set your key as RAILWAIL_API_KEYCreate API key

Specifications

Model ID: gpt-4o-mini
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)
Run time (median): 3.6 s7 completed runs on Railwail in the last 90 days
Model size: Undisclosed (estimated ~8B-20B parameters dense)
License: Proprietary, accessible via OpenAI API and Azure OpenAI Service. Commercial use permitted under OpenAI Terms.
Catalog entry updated: September 23, 2026

Example prompts

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

Quick Summary
Summarize the key points of the Agile methodology in 3 bullet points
Show example answer
1) Agile emphasizes iterative development, delivering working software in short cycles called sprints rather than one big release. 2) It prioritizes collaboration between cross-functional teams and stakeholders, with regular feedback loops. 3) Agile embraces change—requirements can evolve throughout the project based on user feedback and shifting priorities.
Email Draft
Write a professional email declining a meeting invitation due to a scheduling conflict
Show example answer
Subject: Re: Meeting Invitation – Schedule Conflict Hi [Name], Thank you for the invitation. Unfortunately, I have a prior commitment at that time and won't be able to attend. I'd love to stay in the loop—could you share the meeting notes afterward? Alternatively, I'm available on Thursday afternoon if rescheduling works for the group. Best regards

Use cases

What it is used for

Cost-sensitive chatbots
Large-scale classification and labeling
Agent tool routers
Email drafting and summarisation
Coding completions in IDEs
Customer-service automation

Related guides

Best Large Language Models

All models in Text & chat

Frequently asked questions

What is GPT-4o Mini?

GPT-4o Mini 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 Mini cost on Railwail?

On Railwail, GPT-4o Mini costs $0.18 per 1M input tokens and $0.72 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 Mini?

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

How fast is GPT-4o Mini?

On Railwail, the median run time of GPT-4o Mini over the last 90 days was 3.6 s, based on 7 completed runs.

Is GPT-4o Mini better than Claude Fable 5.1?

That depends on the task. GPT-4o Mini (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 Mini and Claude Fable 5.1

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

Create a Railwail API key and send your request with the model ID gpt-4o-mini. 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
6,567 % more expensive per unit
Compare GPT-4o Mini 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
3,233 % more expensive per unit
Compare GPT-4o Mini 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
2,567 % more expensive per unit
Compare GPT-4o Mini vs. Claude Opus 5.5

Use GPT-4o Mini 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