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A detailed report on the gpt-4.1-mini LLM from OpenAI, based on publicly available information, is provided below. This report focuses on quantitative details and sourced information. Overview GPT-4.1-mini is a small, efficient, and capable language model from OpenAI, released on April 14, 2025. It is designed to be a faster and more cost-effective alternative to larger models like GPT-4o, while still offering strong performance across a variety of tasks, including instruction-following, coding, and vision understanding. It is part of the larger GPT-4.1 family of models, which also includes the larger GPT-4.1 and the smaller GPT-4.1-nano. Key Specifications

Feature Detail Source
Model Name gpt-4.1-mini Box Developer Documentation
API Model Name azure__openai__gpt_4.1_mini Box Developer Documentation
Release Date April 14, 2025 Box Developer Documentation, Wikipedia
Knowledge Cutoff June 2024 Box Developer Documentation, Wikipedia
Input Context Window 1 million tokens Box Developer Documentation, AI/ML API Documentation, OpenRouter
Maximum Output Tokens 32,768 (32k) tokens Box Developer Documentation, AI/ML API Documentation, OpenRouter
Open Source No Box Developer Documentation, AI/ML API Documentation

Performance and Benchmarks

GPT-4.1-mini has demonstrated significant performance improvements over its predecessors and is competitive with larger models on various benchmarks.

Benchmark Score Source
MMLU 87.5% DocsBot AI
Global MMLU 78.5% DocsBot AI
AIME 2024 49.6% DocsBot AI
IFEval 84.1% OpenRouter, DocsBot AI
Hard Instruction Evals 45.1% OpenRouter
MultiChallenge 35.8% OpenRouter
Aider’s Polyglot Diff 31.6% OpenRouter

Pricing

The pricing for GPT-4.1-mini is designed to be cost-effective, especially for applications with high-volume or real-time needs.

Cost Type Rate Source
Input Tokens $0.40 per million tokens OpenRouter, DocsBot AI
Output Tokens $1.60 per million tokens OpenRouter, DocsBot AI

It has been noted that there is a 75% discount for cached inputs.

Key Features and Capabilities

  • Multimodality: GPT-4.1-mini can process both text and image inputs and generate text outputs.
  • Coding: The model shows strong coding ability and is particularly reliable at following diff formats, which can reduce cost and latency. It has been noted to more than double GPT-4o’s score on Aider’s polyglot diff benchmark.
  • Instruction Following: GPT-4.1-mini is trained to follow instructions more literally than previous models, making it highly steerable and responsive to well-specified prompts.
  • Long Context: With a 1 million token context window, the model can process and reason over large amounts of text.
  • Availability: GPT-4.1-mini is available through the OpenAI API and is also used as a fallback model for free users of ChatGPT when GPT-4o usage limits are reached. It is also available on Microsoft Azure.

Use Cases

GPT-4.1-mini is well-suited for a variety of applications, including:

  • Interactive applications with tight performance constraints.
  • Agentic tasks that require the model to solve coding problems or use tools.
  • Content creation and summarization.
  • Information extraction from large documents.
  • Customer service bots and other conversational AI.