OpenAI: GPT-4.1 Mini

GPT-4.1 Mini is a text model from OpenAI, released on 14 April 2025. It sits in the middle of the GPT-4.1 family: a mid-sized model that OpenAI describes as delivering performance competitive with GPT-4o while running at substantially lower latency and cost. If you already know the GPT-4.1 line, this is the smaller sibling you reach for when the full model is more than the task needs.

The model accepts text, images and files as input, and returns text only. Its context window is 1,047,576 tokens, which is just over a million tokens, and the maximum output is 32,768 tokens. The knowledge cutoff is 30 June 2024. That combination of a very large input window and a moderate output ceiling shapes what the model is good at: reading a lot and writing a bounded amount.

Practical use cases follow directly from those numbers. The million-token context makes it suitable for work over long documents, large codebases, lengthy transcripts or multi-file inputs that would otherwise need chunking and retrieval. Because images and files are accepted alongside text, you can mix a screenshot or a PDF with a written question in a single request. The 32,768-token output limit suits summarisation, extraction, classification, structured data generation and tool-driven agent loops, where the model reasons over a large input and emits a comparatively short result. Lower latency than GPT-4o also makes it reasonable for interactive features rather than only batch jobs.

You call it through api14's OpenAI-compatible API using the model id "openai/gpt-4.1-mini". Because the interface follows the OpenAI chat completions shape, existing OpenAI SDKs and clients work with only a base URL and key change. The supported request parameters are max_completion_tokens, max_tokens, response_format, seed, structured_outputs, temperature, tool_choice, tools and top_p. In practice that means you can control sampling with temperature and top_p, cap the reply length with max_completion_tokens or max_tokens, request JSON or schema-conforming output through response_format and structured_outputs, make runs more reproducible with seed, and give the model functions to call via tools and tool_choice.

The main constraint to keep in mind is the output side. A 32,768-token ceiling is generous for most extraction and summarisation work but tight if you need the model to produce a long document in one pass, and the knowledge cutoff of 30 June 2024 means anything more recent has to come from your prompt or from tools. If your task is dominated by very long generated output, or if you need modalities beyond text out, another model may fit better. For large-input, short-output work, GPT-4.1 Mini is a sensible default.