Introducing GPT-4.1 in the API

Introducing GPT-4.1 in the API | OpenAI

April 14, 2025

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.

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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 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, 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%.

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.

These improvements in instruction following reliability and long context comprehension also make the GPT‑4.1 models considerably more effective at powering agents, or systems that can independently accomplish tasks on behalf of users. When combined with primitives like the Responses API⁠, developers can now build agents that are more useful and reliable at real-world software engineering, extracting insights from large documents, resolving customer requests with minimal hand-holding, and other complex tasks.

Note that GPT‑4.1 will only be available via the API. In ChatGPT, many of the improvements in instruction following, coding, and intelligence have been gradually incorporated into the latest version⁠ of GPT‑4o, and we will continue to incorporate more with future releases.

We will also begin 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. GPT‑4.5 Preview will be turned off in three months, on July 14, 2025, to allow time for developers to transition. GPT‑4.5 was introduced as a research preview to explore and experiment with a large, compute-intensive model, and we’ve learned a lot from developer feedback. We’ll continue to carry forward the creativity, writing quality, humor, and nuance you told us you appreciate in GPT‑4.5 into future API models.

Below, we break down how GPT‑4.1 performs across several benchmarks, along with examples from alpha testers like Windsurf, Qodo, Hex, Blue J, Thomson Reuters, and Carlyle that showcase how it performs in production on domain-specific tasks.

Coding

GPT‑4.1 is significantly better than GPT‑4o at a variety of coding tasks, including agentically solving coding tasks, frontend coding, making fewer extraneous edits, following diff formats reliably, ensuring consistent tool usage, and more.

On SWE-bench Verified, a measure of real-world software engineering skills, GPT‑4.1 completes 54.6% of tasks, compared to 33.2% for GPT‑4o (2024-11-20). This reflects improvements in model ability to explore a code repository, finish a task, and produce code that both runs and passes tests.

For SWE-bench Verified, a model is given a code repository and issue description, and must generate a patch to solve the issue. Performance is highly dependent on the prompts and tools used. To aid in reproducing and contextualizing our results, we describe our setup for GPT‑4.1 here⁠. Our scores omit 23 of 500 problems whose solutions could not run on our infrastructure; if these are conservatively scored as 0, the 54.6% score becomes 52.1%.

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.This evaluation is both a measure of coding capabilities across various programming languages and a measure of model ability to produce changes in whole and diff formats. We’ve specifically trained GPT‑4.1 to follow diff formats more reliably, which allows developers to save both cost and latency by only having the model output changed lines, rather than rewriting an entire file. For best code diff performance, please refer to our prompting guide⁠. For developers who prefer rewriting entire files, we’ve increased output token limits for GPT‑4.1 to 32,768 tokens (up from 16,384 tokens for GPT‑4o). We also recommend using Predicted Outputs⁠ to reduce latency of full file rewrites.

In Aider’s polyglot benchmark, models solve coding exercises from Exercism⁠ by editing source files, with one retry allowed. The ‘whole’ format requires the model to rewrite the entire file, which can be slow and costly. The ‘diff’ format requires the model to write a series of search/replace blocks⁠.

GPT‑4.1 also substantially improves upon GPT‑4o in frontend coding, and is capable of creating web apps that are more functional and aesthetically pleasing. In our head-to-head comparisons, paid human graders preferred GPT‑4.1’s websites over GPT‑4o’s 80% of the time.

Prompt: Make a flashcard web application. The user should be able to create flashcards, search through their existing flashcards,…