GPT-4.1 is a new model from OpenAI, and developers are keen to see how it performs for coding. Let's take a look at what makes GPT-4.1 suitable for coding tasks.
Good Instruction Following
GPT-4.1 shows good performance in following explicit instructions, which is helpful for precise coding changes. Users on Reddit have noted that it avoids "detrimental side quests" that some other models might undertake.
The 16x Eval simple coding evaluation ranked GPT-4.1 highly for its conciseness and ability to follow instructions well.
Some developers feel the code it generates feels more "human", making it easier to integrate into existing projects. One user also found it followed a 15-step instruction set flawlessly for a complex project, highlighting its precision.
Where GPT-4.1 Might Stumble
While GPT-4.1 is good at following instructions, it may struggle with tasks requiring a lot of output code. Other models like Gemini and Claude might handle generating large amounts of code better. This is an important consideration for projects that need extensive code generation.
GPT-4.1 is not a thinking (reasoning) model. This can be a weakness if you want the model to take more initiative or infer intent.
GPT-4.1 Versus Other Models
Models like Claude 3.7 Sonnet, o3 and Gemini 2.5 Pro are more assertive and take more initiative compared to GPT-4.1.
When comparing models against GPT-4.1, Claude 3.7 is noted for its ability in automatically pulling context and generating pretty UI. GPT-4.1, on the other hand, excels at smaller, precise edits and sticking to instructions.
Gemini 2.5 Pro is another popular model for coding. It can handle generating or editing more than 500 lines in one go, an area where GPT-4.1 might be weaker. However, it is not as good at following instructions as GPT-4.1.
It's important to note that GPT-4.1 is considered an upgrade over GPT-4o for software and coding tasks, while being cheaper than GPT-4o.
Getting the Most Out of GPT-4.1
To use GPT-4.1 well, you should be very clear and literal in your prompts. The model follows directions more strictly than older versions, so precise instructions lead to better results. Using structure like Markdown or XML-style tags in your prompts can also help it understand the task better.
If you are working with long contexts, OpenAI recommends placing your most important instructions at both the beginning and end of your prompt. You can also encourage step-by-step problem-solving by asking the model to "think step by step." This can lead to more accurate and thoughtful responses.
Choosing the Right Model
Choosing the right AI model depends on your specific needs and prompting style. If you prefer to be in control and give clear instructions, GPT-4.1 is a good option, similar to Claude 3.5 Sonnet. It is well-suited for tasks where you have a well-defined scope and want predictable behavior.
However, if your task involves exploring ideas, broad refactoring, or you want the model to take more initiative, you might consider other models such as Claude 3.7 Sonnet or Gemini 2.5 Pro.