#ai

We trained GPT‐4.1 to reliably attend to information across the full 1 million context length. We’ve also trained it to be far more reliable than GPT‐4o at noticing relevant text, and ignoring distractors across long and short context lengths

We also recommend using Predicted Outputs⁠(opens in a new window) to reduce latency of full file rewrites.

GPT‐4.1 also scores 87.4% on IFEval, compared to 81.0% for GPT‐4o. IFEval uses prompts with verifiable instructions (for example, specifying content length or avoiding certain terms or formats).

For API developers looking to edit large files, GPT‐4.1 is much more reliable at code diffs across a range of formats. GPT‐4.1 more than doubles GPT‐4o’s score on Aider’s polyglot diff benchmark

⁠, and even beats GPT‐4.5 by 8%abs

Through efficiency improvements to our inference systems, we’ve been able to offer lower prices on the GPT‐4.1 series.GPT‐4.1 is 26% less expensive than GPT‐4o for median queries, and GPT‐4.1 nano is our cheapest and fastest model ever

In IFEval

⁠ , models must generate answers that comply with various instructions.

For tasks that demand low latency, GPT‐4.1 nano is our fastest and cheapest model available. It delivers exceptional performance at a small size with its 1 million token context window, and scores 80.1% on MMLU, 50.3% on GPQA, and 9.8% on Aider polyglot coding—even higher than GPT‐4o mini. It’s ideal for tasks like classification or autocompletion.

deprecating GPT‐4.5 Preview in the API, as GPT‐4.1 offers improved or similar performance on many key capabilities at much lower cost and latency

One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM. To overcome thes

... See more