GPT-4.1
OpenAI's newest flagship model. Improved reasoning, instruction following, and coding over GPT-4o.
GPT-4.1 is text & chat AI model from OpenAI, priced at €20.00 per 1M input tokens with a 1M tokens context window.
0.7
Examples
See what GPT-4.1 can generate
Design a URL shortener service. What are the key components and how would you handle 10 million requests per day?
A URL shortener at 10M req/day needs: 1) An API layer behind a load balancer to accept long URLs and return short codes. 2) A base62 encoding scheme (a-z, A-Z, 0-9) generating 6-character codes, yielding 56 billion unique URLs. 3) A distributed key-value store like Redis for fast lookups with a persistent database (PostgreSQL) as the source of truth. 4) A caching layer (CDN + Redis) since reads vastly outnumber writes. 5) Rate limiting per IP/API key to prevent abuse. At ~115 req/sec average, a few application servers with horizontal scaling can handle the load comfortably.
Write exactly 3 sentences about black holes. Each sentence must contain exactly 10 words. Do not use the word 'space'.
Black holes form when massive stars collapse under their gravity. Light cannot escape the intense gravitational pull they create. Scientists study these mysterious objects using powerful telescopes and math.
Pricing
API Integration
Use our OpenAI-compatible API to integrate GPT-4.1 into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("gpt-4-1", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("gpt-4-1", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("gpt-4-1", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — OpenAI's GPT-4.1
OpenAI was founded in 2015 as a non-profit research lab by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, Wojciech Zaremba and John Schulman. In 2019 it transitioned into a capped-profit company to raise capital from Microsoft, which has invested over $13 billion. OpenAI is the publisher of the GPT series papers (GPT-1 in 2018, GPT-2 in 2019, GPT-3 in 2020, GPT-4 Technical Report in 2023), the InstructGPT paper that introduced RLHF for instruction following, and the GPT-4o System Card. Major products include ChatGPT (launched November 2022, with over 200M weekly active users), the OpenAI API, the o-series reasoning models, Sora for text-to-video and DALL-E for image generation. GPT-4.1 was released in April 2025 as an API-only model family (4.1, 4.1 mini and 4.1 nano) optimised for developers, with major improvements over GPT-4o on coding, instruction following and long-context tasks. The 2025 reported valuation exceeded $300 billion.
Visit OpenAI →GPT-4.1 launched in April 2025 as an API-only successor to GPT-4o aimed primarily at developers and agentic workloads. It is a decoder-only Transformer trained on an updated multi-trillion-token mixture of text, code and image-text pairs, with a knowledge cutoff of June 2024. The model family ships in three sizes (4.1, 4.1 mini, 4.1 nano) sharing the same training recipe but distilled to different capability/latency points. The headline change versus GPT-4o is a 1,047,576-token context window (~1M tokens), validated with new long-context evaluations such as OpenAI's MRCR (Multi-Round Coreference Resolution) and Graphwalks benchmarks. Post-training emphasised coding (SWE-bench Verified moved to 54.6% from 33% on GPT-4o), instruction following on edge-case formats, and reliable behaviour in long agent loops. OpenAI used reinforcement learning against verifiable rewards and large-scale synthetic data generation to push these axes. The model deprecates GPT-4.5 Preview from the API and is positioned as the production workhorse for ChatGPT Enterprise and Team deployments. Vision input is supported in all three sizes, function calling and Structured Outputs are first-class, and pricing is roughly 26% cheaper than GPT-4o at launch.
- Parameters
- Undisclosed (estimated multi-hundred billion parameters dense)
- Context
- 1.0M tokens
- 1,047,576 token context window (~1M tokens)
- Major coding improvement: 54.6% on SWE-bench Verified at launch
- Strong instruction following on edge-case formats and negative constraints
- Reliable behaviour over long agent loops
- Vision input across all three model sizes (4.1, mini, nano)
- Function calling, parallel tool calls and Structured Outputs
- Knowledge cutoff June 2024 (later than GPT-4o)
- About 26% cheaper than GPT-4o at launch
- Diff-style file editing and applied-diff coding workflows
- Improved multilingual non-English benchmark performance
- Best for: coding agents, IDE copilots, large-document analysis, production assistants.
Pretrained on a multi-trillion-token web-scale mixture of text, code repositories, books, licensed datasets and image-text pairs. Knowledge cutoff is June 2024. Post-training combined supervised fine-tuning, RLHF and reinforcement learning against verifiable rewards (especially for code).
License: Proprietary commercial license via OpenAI API and Azure OpenAI Service. Not available through the ChatGPT consumer product.
Known limitations
- API-only, no ChatGPT consumer access initially
- 1M context window is supported but quality degrades on cross-document recall vs nominal capacity
- Higher cost than GPT-4o mini for cost-sensitive use cases
- Still hallucinates citations and exact quotes
- No native audio I/O (use GPT-4o for voice)
Frequently asked questions
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