OpenAI: GPT-4.1 (batch)

GPT-4.1 is OpenAI’s non-reasoning model for instruction following and tool calling, with broad knowledge across domains. It accepts text and images and returns text without a dedicated reasoning step. Its large context makes it useful when the task is clearly defined but the relevant material spans substantial documents or code. State the operation you want, the source material it applies to and the form of the final result.

For document processing, separate the instructions from the material using headings or other clear boundaries. Label documents and preserve identifiers that the answer needs to reference. Define the fields for extraction, the criteria for comparison or the audience and length for a rewrite. An illustrative output can be a table of requirements with their source locations, a summary organized by a specified set of questions, or a structured record that distinguishes missing information from an explicit value.

For coding, provide the relevant files, the requested change and the behavior that must remain intact. Ask for the actual artifact you need: a revised function, a complete file, proposed tool arguments or an explanation of the affected logic. Examples can clarify a formatting rule or an edge case, but should follow the same requirements as the real task. OpenAI’s prompting guidance emphasizes direct instructions, roles, source boundaries and representative examples rather than assuming that a long prompt is automatically precise.

The exact GPT-4.1 specification lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. The large input context does not imply equally large output, so a request to rewrite extensive material should identify the portion and final artifact required. Keep enough room for the answer and supply current information when the task depends on facts after the cutoff. GPT-4.1’s documented model type is chat; reasoning-effort settings from newer reasoning models are not part of this profile.

OpenAI documents Chat Completions, Responses, Assistants, Batch and fine-tuning interfaces for GPT-4.1. The listed features include streaming, Structured Outputs, function calling, file uploads, image input, web and file search, fine-tuning and prompt caching. Its tools include code interpretation, image generation and MCP connections as well as search and functions. The model’s own output is text, while a tool can return a separate artifact or observation. Define the purpose and arguments of an application operation and use its actual returned result; a model-generated request is not confirmation that an external operation completed.

A distinctive feature is Predicted Outputs in OpenAI’s Chat Completions interface. When most of a desired text or code result is already known, the existing content can be supplied as a prediction to help regenerate the complete result with a small change. For example, provide a current file and ask for a narrowly specified property rename while retaining the surrounding code. The prediction is expected output content, not an instruction or a guarantee that the unchanged parts will remain correct. Inspect the regenerated artifact against the requested modification and the original material.

Predicted Outputs have their own compatibility limits. OpenAI documents text-only use, with no audio inputs or outputs and no function calling in that request. They do not support multiple choices with n greater than 1, log probabilities, positive presence or frequency penalties, or the max_completion_tokens parameter. Keep this regeneration workflow distinct from a task that needs tool calls. Prediction text can match portions of the output, and the producer reports accepted and rejected prediction tokens; an unsuitable prediction can be partly unused, so the feature does not guarantee a particular speedup or result.

For ordinary instruction-following tasks, a useful GPT-4.1 result is a document or code artifact that follows a defined contract, an extraction with explicit fields, or a tool request whose arguments fit the supplied operation. For a small change to a mostly unchanged file, the predicted-output workflow gives a more specific way to prepare the expected result. In both cases, preserve the source material needed to check the answer and make ambiguity explicit. The model’s context capacity and tools support the workflow; the task brief still determines what a complete, usable answer contains.