OpenAI launched the next generation of general-purpose models, the GPT-4.1 family, on April 14, 2025. This series includes three models focused on developers: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano.
OpenAI is one of the best-known providers in the generative AI era.
The cornerstone of the company’s AI work is the GPT series of models, which also power the ChatGPT service. ChatGPT was initially powered by GPT-3 and has steadily evolved as OpenAI has developed new GPT models, including GPT-4 and GPT-4o.
OpenAI faces increasing competition in the genAI market from multiple competitors, including Google Gemini, Anthropic Claude, and Meta Llama. This competition has spurred the rapid release of new model technologies. These models compete on different performance aspects, including accuracy, coding performance, and the ability to follow instructions correctly.
On April 14, 2025, OpenAI released GPT-4.1, a new general-purpose model family. With a strong focus on developers, the new GPT 4.1 models are initially only available via API.
What is GPT-4.1?
GPT-4.1 is a series of Transformer-based Large Language Models (LLMs) developed by OpenAI, serving as the company’s flagship general-purpose model. It builds upon the architecture of previous GPT-4 era models, incorporating advancements in reliability and information processing.
The GPT-4.1 family includes three models: the main GPT-4.1 model, GPT-4.1 mini, and GPT-4.1 nano. For all three models in the series, OpenAI used an advanced training methodology that the company claims was designed based on direct developer feedback.
GPT-4.1 is useful as a general-purpose LLM, but it boasts a number of optimizations focused on the developer experience. One such improvement is optimized front-end coding capabilities. For example, in a live announcement that OpenAI released for the new model, the company demonstrated how GPT-4.1 could build an application from a single prompt and a fairly friendly user interface.
The GPT-4.1 models have also been optimized to improve instruction following capabilities. Compared to previous models, GPT-4.1 will more closely and accurately follow instructions from complex, multi-step prompts. In OpenAI’s internal instruction-following benchmarks, GPT-4.1 scored 49%, significantly outperforming GPT-4o, which scored only 29%.
Like GPT-4o, GPT-4.1 is a multimodal model that supports both text and image analysis. OpenAI has extended GPT-4.1’s context window to support up to 1 million tokens, enabling the analysis of longer datasets. To support the longer context window, OpenAI has also improved the attention mechanisms in GPT-4.1 so that the model can correctly parse and retrieve information from long datasets.
Regarding pricing, GPT-4.1 is priced at $2 per million input tokens and $8 per million output tokens, making it the premium offering in the GPT-4.1 family.
What is GPT 4.1 Mini?
Similar to GPT-4o, GPT-4.1 also has a mini version. The basic concept behind the mini version is that it is a smaller LLM that can be run at a lower cost.
GPT-4.1 mini is a scaled-down model that maintains performance comparable to GPT-4o while reducing latency by approximately 50%. According to OpenAI, it matches or exceeds GPT-4o in multiple benchmarks, including visual tasks involving charts, diagrams, and visual math.
Despite being smaller than the flagship GPT-4.1 model, GPT-4.1 mini still supports the same 1 million token context window for use in a single prompt.
At launch, GPT-4.1 mini is priced at $0.40 per million input tokens and $1.60 per million output tokens, making it cheaper than the full GPT-4.1 model.
What is GPT 4.1 Nano?
GPT-4.1 nano is the first nano-level LLM released by OpenAI. Nano-level is smaller and more cost-effective than OpenAI’s mini-level LLMs.
GPT-4.1 nano is the smallest and most economical model in OpenAI’s newly released GPT-4.1 family. Its smaller size makes it the fastest, with lower latency than either GPT-4.1 or GPT-4.1 mini. Despite being a smaller model, the nano model maintains the 1 million token context window of its larger siblings, enabling it to process large documents and datasets.
OpenAI is positioning GPT-4.1 nano as well-suited for specific applications where processing speed is prioritized over comprehensive reasoning capabilities. The nano model has been optimized for fast, targeted tasks such as autocomplete suggestions, content categorization, and information extraction from large documents.
At launch, GPT-4.1 nano is priced at $0.10 per million input tokens and $0.40 per million output tokens.
GPT Model Family Comparison
The following table shows a comparison of some key parameters for GPT-4o, GPT-4.5, and GPT-4.1:
| Item | GPT-4o | GPT-4.5 | GPT-4.1 |
|---|---|---|---|
| Release Date | May 13, 2024 | February 27, 2025 | April 14, 2025 |
| Focus | Multimodal Integration | Large-Scale Unsupervised Learning | Developer and Coding Improvements |
| Modalities | Text, Image, and Audio | Text and Image | Text and Image |
| Context Window | 128,000 tokens | 128,000 tokens | 1,000,000 tokens |
| Knowledge Cutoff | October 2023 | October 2024 | June 2024 |
| SWE-bench Verified (Coding) | 33% | 38% | 55% |
| MMMU | 69% | 75% | 75% |
A Deep Dive into the Technical Features of GPT-4.1
To better understand the power of GPT-4.1, let’s delve into the technical details behind it. As OpenAI’s flagship general-purpose model, GPT-4.1’s core lies in its Transformer-based Large Language Model (LLM) architecture. This architecture enables it to process and generate complex text and images, excelling in a variety of tasks.
Advantages of the Transformer Architecture
The Transformer architecture has been a breakthrough technology in the field of Natural Language Processing (NLP) in recent years. Through its self-attention mechanism, it can capture the relationships between different words in the text, thereby better understanding the meaning of the text. Compared to traditional Recurrent Neural Networks (RNNs), the Transformer architecture has the following advantages:
- Parallel Computation: The Transformer architecture can process all words in the text in parallel, greatly improving computational efficiency.
- Long-Range Dependencies: The Transformer architecture can effectively capture long-range dependencies in the text, which is crucial for understanding long texts.
- Interpretability: The self-attention mechanism of the Transformer architecture can be visualized, helping us understand how the model makes predictions.
GPT-4.1 inherits these advantages of the Transformer architecture and improves upon them, making it perform even better in various tasks.
Diversity of Training Data
The power of GPT-4.1 also lies in its use of a large amount of diverse training data. This data includes:
- Text Data: Various texts from the Internet, including news articles, blogs, books, code, etc.
- Image Data: Various images from the Internet, including photos, charts, diagrams, etc.
By using these diverse training data, GPT-4.1 can learn rich knowledge and skills, enabling it to perform well in various tasks.
Enhanced Multimodal Capabilities
GPT-4.1 can not only process text data but also image data, giving it powerful multimodal capabilities. By combining text and images, GPT-4.1 can better understand the world and generate richer and more useful content.
For example, GPT-4.1 can:
- Generate Descriptions Based on Images: Given an image, GPT-4.1 can generate a text describing the content of the image.
- Generate Images Based on Text: Given a text, GPT-4.1 can generate an image related to the content of the text.
- Answer Questions Related to Images: Given an image and a question, GPT-4.1 can answer the question based on the content of the image.
These multimodal capabilities give GPT-4.1 tremendous potential in various application scenarios.
Optimization of Instruction Following Capabilities
GPT-4.1 has been optimized in terms of instruction following capabilities, enabling it to better understand the user’s intent and generate content that better meets the user’s needs. To achieve this goal, OpenAI used an advanced training methodology based on direct developer feedback.
By using this method, GPT-4.1 can learn how to better understand user instructions and generate more accurate, complete, and useful content.
Potential of GPT-4.1 in Practical Applications
As a powerful general-purpose model, GPT-4.1 has great potential in various practical applications. Here are some potential application scenarios for GPT-4.1:
- Customer Service: GPT-4.1 can be used to build intelligent customer service robots, thereby improving the efficiency and quality of customer service.
- Content Creation: GPT-4.1 can be used to assist in content creation, such as writing news articles, blogs, books, etc.
- Education: GPT-4.1 can be used to build intelligent tutoring systems, thereby improving the personalization and efficiency of education.
- Scientific Research: GPT-4.1 can be used to assist in scientific research, such as analyzing data, generating hypotheses, and writing papers.
- Healthcare: GPT-4.1 can be used to assist in healthcare, such as diagnosing diseases, developing treatment plans, and providing health advice.
With the continuous development of GPT-4.1 technology, its potential in practical applications will become greater and greater.
GPT-4.1 Mini and Nano: Lighter Options
In addition to the flagship model GPT-4.1, OpenAI also launched two lighter models, GPT-4.1 Mini and GPT-4.1 Nano. These two models maintain a certain level of performance while reducing computational costs and latency, making them more suitable for some resource-constrained application scenarios.
GPT-4.1 Mini: Balance of Performance and Efficiency
GPT-4.1 Mini is a scaled-down model that maintains performance comparable to GPT-4o while reducing latency by approximately 50%. This makes GPT-4.1 Mini very suitable for some application scenarios that require fast responses, such as real-time translation, speech recognition, etc.
Despite its smaller size, GPT-4.1 Mini still supports the same 1 million token context window for use in a single prompt. This allows GPT-4.1 Mini to still process large amounts of data and perform well in various tasks.
GPT-4.1 Nano: A Tool for Ultra-Fast Response
GPT-4.1 Nano is the first nano-level LLM launched by OpenAI. Nano-level is smaller and more cost-effective than OpenAI’s mini-level LLMs. This makes GPT-4.1 Nano very suitable for some application scenarios that require ultra-fast responses, such as autocomplete suggestions, content categorization, etc.
Despite its smallest size, GPT-4.1 Nano still maintains the 1 million token context window of its larger siblings. This allows GPT-4.1 Nano to still process large amounts of data and perform well in various tasks.
In summary, GPT-4.1 Mini and GPT-4.1 Nano are two lighter options that maintain a certain level of performance while reducing computational costs and latency, making them more suitable for some resource-constrained application scenarios.
GPT-4.1 Pricing Strategy
OpenAI has adopted different pricing strategies for the GPT-4.1 series of models to meet the needs of different users.
- GPT-4.1: $2 per million input tokens, $8 per million output tokens.
- GPT-4.1 Mini: $0.40 per million input tokens, $1.60 per million output tokens.
- GPT-4.1 Nano: $0.10 per million input tokens, $0.40 per million output tokens.
From the pricing strategy, it can be seen that GPT-4.1 is a premium product, suitable for application scenarios that require high performance and high quality. GPT-4.1 Mini and GPT-4.1 Nano are more economical and affordable, suitable for some resource-constrained application scenarios.
Conclusion
GPT-4.1 is the latest general-purpose model series launched by OpenAI, including three models: GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano. GPT-4.1 has been optimized in terms of performance, multimodal capabilities, and instruction following capabilities, making it have great potential in various application scenarios. GPT-4.1 Mini and GPT-4.1 Nano are lighter and suitable for some resource-constrained application scenarios.
With the continuous development of GPT-4.1 technology, its potential in practical applications will become greater and greater. We look forward to GPT-4.1 bringing us more surprises in the future.