What changed between this model and its predecessor in the 4o family?

Three major leaps: the context window expanded from 128K to 1.0M tokens (8x), instruction following improved significantly for complex multi-constraint prompts, and coding benchmarks rose across generation, review, and refactoring tasks. Cost dropped relative to GPT-4o.

How does the 75% prompt caching discount work with the context of 1.0M tokens?

Cached input tokens, from repeated system prompts, shared few-shot examples, or persistent context, are billed at 75% below the standard input rate. With a window of 1.0M tokens, caching a large system prompt or reference corpus across requests yields substantial savings.

Is GPT-4.1 mini a distilled version of full GPT-4.1?

OpenAI describes it as a separate model in the GPT-4.1 family, not a direct distillation. It was trained to match GPT-4o-level intelligence at lower compute requirements while sharing the GPT-4.1 family's improvements in coding and instruction following.

Can GPT-4.1 mini handle an entire codebase in one request?

The context window of 1.0M tokens accommodates most single-repository codebases. For retrieval accuracy across the full range, the GPT-4.1 family maintains strong performance even at extreme context lengths, an area where previous-generation models often degraded.

What latency improvement should I expect?

GPT-4.1 mini delivers nearly half the latency compared to GPT-4o. See live throughput and time-to-first-token metrics on this page for current measured performance.

When should I use full GPT-4.1 instead of mini?

When the task demands the absolute highest accuracy, particularly on complex coding challenges, nuanced multi-step instructions, or workloads where the quality gap between mini and full is measurable and consequential for your application.

OpenAI

GPT-4.1 mini

GPT-4.1 mini delivers GPT-4o-class intelligence at reduced cost with nearly half the latency, making it a cost-performance option in the GPT-4.1 family for high-volume production workloads. Your use is subject to OpenAI's Terms & Privacy Policies.

View API reference

Input and output price: Prices from: Input $0.40, Output $1.60, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'openai/gpt-4.1-mini',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out GPT-4.1 mini by OpenAI. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

GPT-4.1 mini

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Checking availability for your team

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

Azure

Legal:Terms•Privacy

33K

0.7 s

$0.40/M

$1.60/M

Read$0.10/M

$14/K

05/14/2025

OpenAI

Legal:Terms•Privacy

33K

1.1 s

62 tps

$0.40/M+1 more

$1.60/M+1 more

Read$0.10/M

$10/K

05/14/2025

Getting started

Call GPT-4.1 mini through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'openai/gpt-4.1-mini',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same GPT-4.1 mini request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'openai/gpt-4.1-mini',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
    temperature: 0.5,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `openai/gpt-4.1-mini`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. GPT-4.1 mini supports up to `32,768` output tokens.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 1M-token context window
Image	—	URL, base64, Uint8Array	—	—	Sent as image parts in messages; counts as input tokens
PDF	—	URL, base64, Uint8Array	—	—	Sent as file parts in messages; counts as input tokens

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK openai provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'openai/gpt-4.1-mini',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['azure', 'openai'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Image input

Send images alongside text as message parts. Images count as input tokens.

image-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'openai/gpt-4.1-mini',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image.' },
          { type: 'image', image: 'https://example.com/photo.jpg' },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

PDF input

Attach PDFs as file parts. Their contents count as input tokens.

pdf-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'openai/gpt-4.1-mini',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Summarize this document.' },
          {
            type: 'file',
            mediaType: 'application/pdf',
            data: 'https://example.com/document.pdf',
          },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'openai/gpt-4.1-mini',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by OpenAI

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

openai/gpt-6-astra

1.1M

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65 tps

$10/M+2 more

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Read$1/M

Write$12.50/M

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09/04/2026

openai/gpt-5.6-luna

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2.4 s

115 tps

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Read$0.02/M

Write$0.25/M

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07/09/2026

openai/gpt-5.6-sol

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2.1 s

84 tps

$4/M+2 more

$20/M+2 more

Read$0.40/M

Write$5/M

$10/K

07/09/2026

openai/gpt-5.6-terra

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104 tps

$2/M+2 more

$12/M+2 more

Read$0.20/M

Write$2.50/M

$10/K

07/09/2026

openai/gpt-5-nano

400K

6.0 s

132 tps

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Read$0.005/M

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openai/gpt-5-mini

400K

2.8 s

109 tps

$0.25/M+1 more

$2/M+1 more

Read$0.03/M

$14/K

08/07/2025

Copy link to headingAbout GPT-4.1 mini

GPT-4.1 mini launched on May 14, 2025 as the middle tier of the GPT-4.1 family. Three advances separate it from its predecessor.

First, the context window expanded from 128K to 1.0M tokens, an 8x increase. An entire codebase, a full conversation history spanning days, or a collection of legal documents all fit in a single request. Combined with the 75% prompt caching discount available across the GPT-4.1 family, long-context workflows that reuse system prompts become very affordable.

Second, instruction following improved materially. OpenAI trained the GPT-4.1 family with a focus on adherence to complex, multi-constraint prompts. For developers building structured pipelines where the model must follow formatting rules, respect output schemas, and handle edge cases in system instructions, this reduces debugging time and increases reliability.

Third, coding capability stepped up. The GPT-4.1 family brought measurable gains on code generation, review, and refactoring benchmarks compared to the GPT-4o generation. GPT-4.1 mini inherits those gains, making it capable enough for code assistance tasks that previously required a full-size model.

The result: GPT-4o-class intelligence at lower cost and nearly half the latency. For most production workloads, GPT-4.1 mini is the right choice.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Low latency and a context window of 1.0M tokens make GPT-4.1 mini unusually versatile. It can stream responses in real-time chat while also handling batch jobs that load entire codebases into context. Those two patterns rarely coexist in a single model at this price.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use GPT-4.1 mini

Best for

Production chat interfaces: Streaming products where reduced latency and cost directly improve unit economics
Long-context workloads: Codebase analysis, document comparison, and extended conversation histories that use the window of 1.0M tokens
Strict output formatting: Pipelines where improved instruction following reduces error rates
Agentic loops: Many sequential calls where both speed and cost per call compound over the session
Code assistance: Generation tasks that benefit from the GPT-4.1 family's coding improvements

Consider alternatives when

Maximum coding accuracy: Full GPT-4.1 is the stronger choice when top-tier instruction adherence is required
Lightweight tasks: GPT-4.1 nano handles classification, routing, or simple extraction at even lower cost
STEM reasoning dominant: Dedicated reasoning models may outperform on complex math or science workloads

Copy link to headingConclusion

GPT-4.1 mini combines an 8x context expansion, stronger instruction following, improved coding, and reduced cost relative to GPT-4o. Together, these changes make it the default model for production traffic in the GPT-4.1 family.

Copy link to headingFrequently Asked Questions

What changed between this model and its predecessor in the 4o family?
Three major leaps: the context window expanded from 128K to 1.0M tokens (8x), instruction following improved significantly for complex multi-constraint prompts, and coding benchmarks rose across generation, review, and refactoring tasks. Cost dropped relative to GPT-4o.
How does the 75% prompt caching discount work with the context of 1.0M tokens?
Cached input tokens, from repeated system prompts, shared few-shot examples, or persistent context, are billed at 75% below the standard input rate. With a window of 1.0M tokens, caching a large system prompt or reference corpus across requests yields substantial savings.
Is GPT-4.1 mini a distilled version of full GPT-4.1?
OpenAI describes it as a separate model in the GPT-4.1 family, not a direct distillation. It was trained to match GPT-4o-level intelligence at lower compute requirements while sharing the GPT-4.1 family's improvements in coding and instruction following.
Can GPT-4.1 mini handle an entire codebase in one request?
The context window of 1.0M tokens accommodates most single-repository codebases. For retrieval accuracy across the full range, the GPT-4.1 family maintains strong performance even at extreme context lengths, an area where previous-generation models often degraded.
What latency improvement should I expect?
GPT-4.1 mini delivers nearly half the latency compared to GPT-4o. See live throughput and time-to-first-token metrics on this page for current measured performance.
When should I use full GPT-4.1 instead of mini?
When the task demands the absolute highest accuracy, particularly on complex coding challenges, nuanced multi-step instructions, or workloads where the quality gap between mini and full is measurable and consequential for your application.

GPT-4.1 mini

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Image input

PDF input

Tool calling

Copy link to headingMore models by OpenAI

Copy link to headingAbout GPT-4.1 mini

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use GPT-4.1 mini

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions