OpenAI releases the GPT-4.1 series of models. What is its biggest attraction to the industry?

OpenAI has released three GPT-4.1 series models. Behind this is actually a strategic adjustment of OpenAI: focusing more on specific fields, paying more attention to cost-effectiveness, and better understanding the actual needs of developers.

The biggest highlight is the context window of 1 million tokens, which is several times larger than GPT-4o's 128,000 tokens. It can support about 750,000 English words or more than 3,000 pages of documents! This is a boon for tasks that require processing massive amounts of information, such as analyzing complex code bases, translating long reports or legal documents, and can help AI assistants remember better in long conversations.

In terms of performance, especially in the areas of writing code and following instructions, which are the most important aspects for programmers, GPT-4.1 has made significant progress. Data from OpenAI and feedback from some early users show that it scores much higher than GPT-4o in the SWE-bench coding test, and even higher than GPT-4.5. The specific scores are that on SWE-benchVerified, GPT-4.1 can get 52%-54.6%, while GPT-4.5 is about 38%.

A software company called Windsurf said that in their internal tests, GPT-4.1 scored 60% higher in coding than GPT-4o, increased the efficiency of calling tools by 30%, and reduced the number of cases of modifying messy code by almost half. Simply put, this series of models can better understand the developer's intentions and write more reliable and efficient code. Listening to instructions more accurately means that it is more capable of handling complex tasks or multi-step instructions, which is very important for developing AI agents.

In addition to performance, the GPT-4.1 series has made a lot of efforts in cost-effectiveness this time. OpenAI itself said that the running cost of the new model is much lower than the previous GPT-4.5 preview version. Even compared with GPT-4o, the query cost has dropped by about 26%. The specific prices are as follows: the flagship version of GPT-4.1 charges $2 per million tokens for input and $8 for output; the mini version charges $0.4 for input and $1.6 for output; the nano version charges $0.1 for input and $0.4 for output. This tiered pricing allows developers to flexibly choose according to tasks and budgets.

Nano is suitable for fast and relatively simple tasks; Mini is more balanced between performance and cost; Standard version 4.1 is more suitable for more difficult and relatively complex tasks. OpenAI also increased the discount of prompt cache from 50% to 75%, which saves more money in processing duplicate content. As Kevin Weil, chief product officer of OpenAI, said, the cheaper and easier the model is, the more tricks it can be used, and AI can truly help more people.

By the way, the release time of OpenAI's model is also quite interesting. During this period, Google's Gemini, Anthropic's Claude, Meta, Mistral and other players have been continuously launching new models. Moreover, with the application and popularization of large AI models, corporate users have increasingly strong demands for faster, cheaper, and more practical AI. Open source models like LLaMA3 also put a lot of pressure on OpenAI. Therefore, the launch of the GPT-4.1 series is more like a strategy for OpenAI to stabilize the developer and enterprise markets, solving some pain points of previous models in long text memory, stability, and reasoning cost.

So, what does the GPT-4.1 series mean for the industry as a whole?

First of all, developers. With stronger ability to write code and process long texts, the development cycle can be shortened accordingly, and it will be easier to process code, write documents, and do tests, which may even give birth to more powerful AI programmer assistants. Secondly, for enterprises, there is hope to build a knowledge base that can access internal systems and handle complex processes. You see, Thomson Reuters used it to optimize the legal assistant CoCounsel, and the accuracy of the long text audited increased by 17%; Carlyle Investments improved the performance of data collection from complex documents by 50%; tax research company BlueJ found that on the most difficult tax issues, GPT-4.1 was 53% more accurate than GPT-4o. These examples all show that it can really play in professional fields. Moreover, the fine-tuning function will be launched on Azure later, so that enterprises can customize the model with their own data, making it more familiar with industry terms and more in line with business processes.

However, there are a few things to note when using GPT-4.1. The model is relatively rigid in understanding instructions, so it is very important to write "prompt words" (Prompt Engineering), and the requirements of the instructions must be written very clearly. In addition, although the ultra-large context of one million tokens is powerful, OpenAI officials also pointed out that the larger the window, the more reliable the model may be (for example, in internal tests, the accuracy dropped from 84% of 8,000 tokens to about 50% of 1 million tokens), so if you really want to use it on particularly long texts, you still have to test and verify it carefully. Another point is that this release did not mention GPT-4o's multimodal capabilities such as speech. At present, it seems that the 4.1 series still focuses on text and image input, and text output.

The naming of models and product lines are also quite confusing. First there was the GPT-4.5 preview version, and now there is GPT-4.1, and there is also the legendary GPT-5. The relationship is a bit complicated. OpenAI decided to stop the GPT-4.5 preview version API in July 2025, on the grounds that GPT-4.1 has similar or even better performance than 4.5 at a lower cost and faster speed. Although some tests show that GPT-4.5 may still have advantages in some aspects (such as academic knowledge), its high price and slow speed do limit large-scale use. It feels that OpenAI has found a more practical balance in GPT-4.1, giving priority to meeting the performance, speed and cost needs of most API users.

Another point: the more powerful GPT-4.1 series (compared to GPT-4o) is now only open to developers through the API, and ordinary users cannot use it on the ChatGPT interface. OpenAI said that many improvements to these research models will be gradually integrated into the GPT-4o version. This is a two-track strategy: developers use the API to explicitly select a specific version (4.1, mini, nano, 4o, etc.), which is stable and reliable; ordinary users use GPT-4o, which is constantly optimized in the background.

In general, the GPT-4.1 series released by OpenAI this time is a pragmatic optimization, not a leap forward in parameters. The focus is on improving key capabilities (coding, long text, instructions) and reducing costs. It can lower the threshold for enterprises and developers to use AI technology and further consolidate its position as a developer platform. The real breakthrough may be hidden in these iterations that make enterprise-level AI within reach.