Less than three months after the release of GPT-5, OpenAI has now launched the first major update of the GPT-5.1 large language model (LLM). The fifth-generation GPT architecture has made significant progress in reasoning, generation speed, and output quality. But it still faces criticism from users for losing GPT-4’s warm, empathetic conversational style. The new update promises to fix this problem and introduce something called “adaptive reasoning” to the default model in all layers, which can now let it decide whether to think before responding.
Over the past few hours, I have been testing the GPT-5.1 Instant model to see where it has improved and where OpenAI still has room for improvement. However, before we get into the details, it’s important to understand that this is very much a fine-tuning exercise rather than a dramatic shift like GPT-5. There are some differences compared to its predecessor, but this time, the differences are quite subtle.
Unless it gets to the point (more on that later).
GPT-5.1 speed and responsiveness
The most obvious upgrade is speed. GPT 5.1 produces text with fewer pauses, maintaining a steady flow even in longer output. In earlier versions, the model would sometimes stop and then resume mid-sentence, which made the interaction feel unbalanced. GPT 5.1 significantly reduces this behavior.
After each prompt, the model began to respond faster and complete its answer with less noticeable “think time.” This creates a smoother flow, making it easier to rely on the tool for tasks that require multiple iterations, such as handling writing projects or code troubleshooting. These improvements are not absolute, and adaptive inference will still slow down the model slightly whenever subtle questions are asked. But the overall interaction feels smoother to me.
GPT 5.1 accuracy and inference improvements
This is where the subtle improvements begin. GPT-5 is already a huge improvement over its predecessor for inference-based tasks. However, in my experience, I have repeatedly encountered ChatGPT responses that are filled with jargon and technical language, which hurts readability and understanding.
GPT-5.1 inference task
When I asked GPT-5.1 for an explanation of the “Ship of Theseus” paradox, I noticed that the jargon was almost non-existent and the explanation felt conversational and easy to understand. Even when asked to tackle more complex and evolving topics, such as quantum computing anchored by Google DeepMind’s Quantum Echo algorithm, it managed to provide an understandable and comprehensive response.
In both cases, accuracy was not affected either. However, accuracy based on information is not the only parameter to judge an AI chatbot. Accuracy also comes into play when the prompt has very strict instructions to follow. In my tests, despite giving the chatbot a complex set of instructions, it was still able to comply with them smoothly.
GPT-5.1 accuracy test
The model can also make it easier to express uncertainty when it cannot confirm a claim. While this doesn’t eliminate factual drift, especially in niche topics, it makes errors easier to spot. The logical explanation of GPT 5.1 also seems less prone to circular reasoning. In problems that require step-by-step thinking, models tend to maintain lines of reasoning without falling into contradictions. These improvements help but do not replace the need for manual verification.
GPT 5.1 for writing and conversation
These are two separate areas, but it makes sense to bring them together because one affects the other. When it comes to writing, many users will be happy to know that you can now instruct ChatGPT to avoid dashes and it will actually listen. I also noticed that this version produced slightly more user-friendly articles than the previous version.
It’s also easy to control your writing style. When I asked it to write an essay for a 12th grade student, the language reflected that level of understanding. In contrast, when asked to rewrite the paper in the style of a college professor, the answers became more polished and nuanced.
GPT-5.1 writing comparison
Technical quality is nearly identical to GPT-5, and I didn’t notice any noteworthy improvements. When undefined, the structure still follows the typical title, introduction, pointer, conclusion, and future outlook style. However, it handles context shifts more reliably, whether the goal is to summarize, rewrite, or polish a paragraph.
Speaking of dialogue, this is where the magic happens. The ability to capture the nuances of the writing task is evident here, and the overall attitude of the chatbot becomes more friendly and welcoming. Previously, ask it for advice about feeling frustrated and it would just jot down the solution. It now pauses to acknowledge the user before continuing with the solution.
There are other subtle hints at its dialogue improvements. It addresses users by name, asks for their opinions, and takes feedback into consideration. Yes, it’s still not on the level of GPT-4o or GPT-4.1 in that regard, but it’s a huge upgrade. For those who don’t like the human conversational style, you can always adjust by customizing the instructions or changing the presets in personalization settings.
GPT-5.1 restrictions
To fix the problem at the beginning of the article, for some reason GPT-5.1 like Points. If you enter a prompt that contains multiple key points, the model will immediately resort to bullet points. While this structure was also present in previous models, this version feels a bit over the top.
But nitpicking aside, these improvements still fall short of high specificity requirements. When a task requires domain-level knowledge in a highly technical domain, the model may fall into confident but incorrect interpretations. This behavior occurs less frequently than in earlier models, but still needs to be treated with caution. In some cases, it also tends to be verbose, making answers longer than necessary unless explicitly instructed otherwise.
Browsing performance has improved with more stable sources and more predictable citation behavior, but it’s still important to verify claims. The model sometimes relies on older information or mixes in irrelevant details in edge cases. These issues are not surprising given the common limitations of large language models, but they are still worth noting when it comes to the practical usability of GPT-5.1.
All in all, there is an interesting Easter egg in GPT-5.1, intentionally or unintentionally. Ask the model: Is there a seahorse emoji?
