GPT-4.1 explained: Everything you need to know

What is GPT-4.1?

GPT-4.1 is a family of transformer-based large language models (LLMs) developed by OpenAI as the company's flagship general-purpose model. It builds on the architecture of previous GPT-4 era models while incorporating reliability and information processing advances.

The GPT-4.1 series includes three models: the primary GPT-4.1, GPT-4.1 mini and GPT-4.1 nano. For all three models in the family, OpenAI used an advanced training approach that the company claims was informed by direct developer feedback.

While useful as a general-purpose LLM, GPT-4.1 has a series of optimizations focused on developer experience. Among the improvements are optimized front-end coding capabilities. For example, during the live stream announcement from OpenAI for the new model, the company demonstrated how GPT-4.1 could build an application with a single prompt and a reasonably user-friendly interface.

The GPT-4.1 models have also been optimized to improve instruction following. GPT-4.1 will more closely and accurately follow instructions for complex multi-step prompts than its predecessor models. On the internal OpenAI instruction-following benchmark, GPT-4.1 scored 49%, significantly outperforming GPT-4o, which only scored 29%.

As with GPT-4o, GPT-4.1 is a multimodal model that supports text and image analysis. OpenAI has expanded the context window for GPT-4.1 to support up to 1 million tokens, enabling the analysis of longer data sets. In support of the longer context window, OpenAI has also improved GPT-4.1's attention mechanisms so that the models can correctly parse and retrieve information from the long data sets.

Regarding pricing, GPT-4.1 costs $2 per million input tokens and $8 per million output tokens, positioning it as a premium offering in the GPT-4.1 lineup.

What is GPT 4.1 Mini?

As with GPT-4o, GPT-4.1 has a mini version. The basic concept behind the mini versions is that the LLM is smaller and can run at a lower cost.

GPT-4.1 mini is a reduced-size model that maintains performance comparable to GPT-4o while reducing latency by approximately 50%. According to OpenAI, it matches or exceeds GPT-4o on multiple benchmarks, including vision tasks involving charts, diagrams and visual mathematics.

Despite being smaller than the flagship GPT-4.1 model, GPT-4.1 mini still supports the same 1 million token context window in a single prompt.

At launch, the price of GPT-4.1 mini is $0.40 per million input tokens and $1.60 per million output tokens, less expensive than the full GPT-4.1 model.

What is GPT 4.1 nano?

GPT-4.1 nano is the first nano-class LLM from OpenAI. The nano-class is even smaller and more cost-efficient than the mini-class of OpenAI's LLMs.

GPT-4.1 nano is the smallest and most economical model in OpenAI's new GPT-4.1 family. Its smaller size enables it to be the fastest, with lower latency than GPT-4.1 or GPT-4.1 mini. Though a smaller model, the nano model maintains the 1-million-token context window in its larger siblings, allowing it to process extensive documents and datasets.

OpenAI is positioning GPT-4.1 nano as well-suited for specific applications where processing speed takes priority over comprehensive reasoning capabilities. The nano model has been optimized for quick, targeted tasks such as autocomplete suggestions, content classification, and information extraction from large documents.

At launch, the price of GPT-4.1 nano is $0.10 per million input tokens and $0.40 per million output tokens.

Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.

Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.