Introducing GPT-4.1 in the API

A new series of GPT models featuring major improvements on coding, instruction following, and long context—plus our first-ever nano model.

Today, we’re launching three new models in the API: GPT‑4.1, GPT‑4.1 mini, and GPT‑4.1 nano. These models outperform GPT‑4o and GPT‑4o mini across the board, with major gains in coding and instruction following. They also have larger context windows—supporting up to 1 million tokens of context—and are able to better use that context with improved long-context comprehension. They feature a refreshed knowledge cutoff of June 2024.

GPT‑4.1 excels at the following industry standard measures: 

• Coding: GPT‑4.1 scores 54.6% on SWE-bench Verified, improving by 21.4%_abs over GPT‑4o and 26.6%_abs over GPT‑4.5—making it a leading model for coding.

• Instruction following: On Scale’s MultiChallenge⁠(opens in a new window) benchmark, a measure of instruction following ability, GPT‑4.1 scores 38.3%, a 10.5%_abs increase over GPT‑4o.

• Long context: On Video-MME⁠(opens in a new window), a benchmark for multimodal long context understanding, GPT‑4.1 sets a new state-of-the-art result—scoring 72.0% on the long, no subtitles category, a 6.7%_abs improvement over GPT‑4o.

While benchmarks provide valuable insights, we trained these models with a focus on real-world utility. Close collaboration and partnership with the developer community enabled us to optimize these models for the tasks that matter most to their applications.

To this end, the GPT‑4.1 model family offers exceptional performance at a lower cost. These models push performance forward at every point on the latency curve.

GPT‑4.1 mini is a significant leap in small model performance, even beating GPT‑4o in many benchmarks. It matches or exceeds GPT‑4o in intelligence evals while reducing latency by nearly half and reducing cost by 83%. 

Coding

GPT‑4o

GPT‑4.1

Real world examples

Instruction following

Real world examples

Long Context

Real world examples

Vision

Pricing

*Based on typical input/output and cache ratios.