OpenAI has recently unveiled a trio of new models accessible through their API: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano. These models represent a significant advancement over their predecessors, GPT-4o and GPT-4o mini, showcasing substantial improvements in coding capabilities and instruction following. Furthermore, they boast expanded context windows, capable of handling up to 1 million tokens, and demonstrate enhanced proficiency in utilizing this extended context through improved long-context comprehension. Notably, these models feature an updated knowledge base, incorporating information up to June 2024. This article delves into the specifics of these models, examining their performance benchmarks, pricing structures, and the implications for developers.
Introducing GPT-4.1: Revolutionizing Coding in OpenAI’s New Models
The GPT-4.1 model introduces a paradigm shift in several key areas, specifically excelling in coding, instruction following, and long-context handling. Its architecture is designed to tackle complex problems more efficiently and accurately, establishing it as a leading model in various applications.
Performance Benchmarks
Coding: GPT-4.1 achieves a score of 54.6% on the SWE-bench Verified benchmark, marking a substantial improvement of 21.4% compared to GPT-4o and 26.6% compared to GPT-4. This achievement underscores its superior capabilities in handling coding tasks, positioning it as an industry leader. The SWE-bench Verified benchmark is a rigorous evaluation that assesses the performance of models on real-world software engineering tasks. The dramatic increase in performance highlights GPT-4.1’s improved ability to generate, understand, and debug code, making it an invaluable tool for software developers. It suggests that GPT-4.1 is not only better at writing code but also at understanding the nuances of complex software systems, allowing it to contribute meaningfully to collaborative coding projects.
Instruction Following: On Scale’s MultiChallenge benchmark, GPT-4.1 attains a score of 38.3%, an increase of 10.5% over GPT-4o. This improvement highlights its enhanced ability to understand and execute complex instructions, making it more reliable for intricate applications. The Scale’s MultiChallenge benchmark is designed to test a model’s ability to follow various types of instructions, including those that are ambiguous, contradictory, or require a deep understanding of the context. GPT-4.1’s improved score on this benchmark demonstrates its greater capacity to interpret and respond to complex prompts, making it more suitable for tasks that require precise and nuanced instruction following. This capability is particularly important in applications such as virtual assistants, robotic control, and automated decision-making.
Long Context: In the Video-MME benchmark, which assesses multimodal long-context understanding, GPT-4.1 sets a new state-of-the-art result with a score of 72.0% in the long, no subtitles category, surpassing GPT-4o by 6.7%. This demonstrates its capability to process and comprehend extensive and diverse data streams. The Video-MME benchmark evaluates a model’s ability to understand and interpret video content, even when subtitles are not available. GPT-4.1’s impressive performance on this benchmark highlights its capability to analyze and synthesize information from long, complex video streams, making it suitable for applications such as video summarization, scene understanding, and content moderation. The model’s ability to handle multimodal data, specifically video and audio, allows it to extract insights that would be difficult or impossible to obtain through traditional text-based analysis.
While benchmarks offer quantitative insights, OpenAI emphasizes that these models were developed with a strong emphasis on real-world applications. This strategic focus, combined with close collaboration with the developer community, has allowed OpenAI to refine the models for the tasks that are most relevant and valuable to users. This iterative process, involving continuous feedback and improvement, ensures that the GPT-4.1 models are not just impressive in terms of benchmarks but also highly practical and user-friendly. OpenAI’s commitment to bridging the gap between theoretical performance and real-world utility is a key factor in its ongoing success and leadership in the AI field.
Real-World Utility
GPT-4.1 models have been optimized to provide exceptional performance at a reduced cost, representing a significant advancement across the entire latency curve. This not only makes AI more accessible but also propels innovation across a wide range of applications. For developers, this means creating more efficient and cost-effective solutions without sacrificing performance. Specifically, the reduction in latency allows for real-time interactions and immediate feedback, enhancing the user experience in applications such as chatbots, virtual assistants, and interactive simulations. The reduced cost of using the GPT-4.1 models democratizes access to advanced AI capabilities, enabling smaller businesses and individual developers to leverage state-of-the-art technology without incurring prohibitive expenses. This combination of improved performance, reduced latency, and lower cost is a game-changer for the AI industry, paving the way for wider adoption and more diverse applications.
GPT-4.1 Mini: A Significant Leap in Small Model Performance
GPT-4.1 mini introduces a significant leap in the performance of small models. This model surpasses GPT-4o in numerous benchmarks, achieving faster results at a reduced cost, making it an appealing choice for developers aiming for efficiency.
The key attributes of GPT-4.1 mini include:
Latency reduction by nearly half compared to the previous generation. This improvement allows for faster response times and more seamless interactions, making it ideal for applications where speed is critical. For example, in a customer service chatbot, reduced latency can lead to more natural and engaging conversations, improving customer satisfaction. Similarly, in a real-time language translation app, faster response times can make communication more fluid and efficient. The GPT-4.1 mini’s latency reduction therefore unlocks new possibilities for AI-powered applications that require rapid and immediate responses.
Cost reduction of 83%. This dramatic cost reduction makes GPT-4.1 mini a highly attractive option for developers who are looking to build AI-powered applications at scale. The lower cost allows for more experimentation and development, as well as the deployment of AI solutions in resource-constrained environments. This cost-effectiveness is particularly beneficial for startups, small businesses, and research institutions that may have limited budgets for AI development. The reduced cost also allows for the creation of more accessible AI solutions, making advanced technology available to a wider range of users.
These improvements make GPT-4.1 mini an ideal solution for applications that demand quick responses without compromising on accuracy. Its blend of performance and efficiency fills a crucial gap in the spectrum of available AI models. For example, in mobile applications where processing power and battery life are limited, GPT-4.1 mini can provide a balance between performance and efficiency, allowing developers to create compelling AI-powered experiences without draining device resources. Similarly, in edge computing scenarios, where data is processed closer to the source, GPT-4.1 mini can enable real-time analysis and decision-making without relying on cloud connectivity.
GPT-4.1 Nano: The Fastest and Most Affordable Model Available
GPT-4.1 nano stands out as the fastest and most affordable model in the GPT-4.1 family. This model is particularly suited for low-latency activities such as classification or autocompletion, where quick processing is essential.
Key features of GPT-4.1 nano include:
Fastest processing times among the GPT-4.1 models. This makes it ideal for applications where speed is paramount, such as real-time data analysis, fraud detection, and automated trading. The ability to process information quickly and accurately is crucial in these scenarios, where even small delays can have significant consequences. GPT-4.1 nano’s speed advantage allows developers to build AI-powered solutions that can respond to events in real-time, making them more effective and responsive to changing conditions.
Lowest pricing structure. This makes it a cost-effective option for high-volume tasks, such as automated content moderation, data labeling, and machine translation. The ability to process large amounts of data at a low cost is essential for these applications, where scalability and efficiency are key considerations. GPT-4.1 nano’s low pricing allows developers to build AI-powered solutions that can handle massive datasets without incurring prohibitive expenses, making it a highly attractive option for data-intensive applications.
A 1 million token context window. Despite its small size and low cost, GPT-4.1 nano retains a substantial context window, allowing it to process and understand long and complex inputs. This is particularly important for applications such as text summarization, question answering, and document analysis, where the ability to maintain context is crucial for generating accurate and informative results. The 1 million token context window enables GPT-4.1 nano to handle large amounts of text without losing important information, making it a versatile and powerful tool for a wide range of natural language processing tasks.
This combination makes GPT-4.1 nano a powerhouse for applications that require rapid data processing, offering a cost-effective solution for high-volume tasks.
Performance Metrics
- MMLU: 80.1% MMLU (Massive Multitask Language Understanding) is a benchmark that measures a model’s ability to perform a wide range of language understanding tasks, including reading comprehension, common sense reasoning, and mathematical problem-solving. GPT-4.1 nano’s score of 80.1% demonstrates its strong general-purpose language understanding capabilities, making it suitable for a variety of text-based applications.
- GPQA: 50.3% GPQA (Grade School Question Answering) is a benchmark that assesses a model’s ability to answer grade school-level questions, requiring a combination of common sense knowledge, logical reasoning, and reading comprehension skills. GPT-4.1 nano’s score of 50.3% indicates its ability to understand and reason about simple questions, making it suitable for applications such as chatbots and educational tools.
- Aider polyglot coding: 9.8% Aider polyglot coding refers to a model’s ability to generate code in multiple programming languages. GPT-4.1 nano’s score of 9.8% suggests that it has some basic coding capabilities, although it may not be as proficient as larger and more specialized models.
These benchmarks demonstrate the proficiency of GPT-4.1 nano in various tasks, highlighting its balanced capabilities across language understanding, question answering, and coding. Its performance across these diverse areas indicates that it is a versatile and adaptable model that can be used in a wide range of applications.
Enhanced Reliability and Long Context Comprehension
The GPT-4.1 models provide improved reliability and comprehensive long context understanding, making them well-suited to power agents that can independently perform tasks on behalf of users. Early testers have noted that GPT-4.1 can exhibit a more literal interpretation of prompts, suggesting the need for explicit and specific instructions. This precision allows the model to execute instructions meticulously, guaranteeing intended responses. This characteristic is particularly beneficial in applications where accuracy and consistency are paramount, such as financial analysis, legal research, and scientific modeling. The model’s ability to adhere strictly to instructions ensures that the results are reliable and reproducible, making it a valuable tool for professionals who require precise and dependable information.
Implications for GPT-4.5 Preview
The GPT-4.5 Preview was deprecated on July 14, 2024, as GPT-4.1 offers improved performance with lower costs and latency. OpenAI plans to sustain the creativity, writing quality, humor, and nuance that were enjoyed in GPT-4.5 in future model releases. This decision reflects OpenAI’s commitment to providing the best possible experience for its users, prioritizing performance and cost-effectiveness over maintaining older, less efficient models. By deprecating GPT-4.5 Preview, OpenAI can focus its resources on developing and improving the GPT-4.1 family, ensuring that users have access to the latest and most advanced AI technology. The promise to maintain the creative and nuanced capabilities of GPT-4.5 in future releases suggests that OpenAI is actively working to enhance the emotional intelligence and expressive abilities of its models, making them more engaging and human-like.
Key Improvements in GPT-4.1
GPT-4.1 demonstrates substantial improvements across coding, following instructions, and processing long contexts. It performs exceptionally well in a variety of critical areas:
Coding Tasks: Agentically solving coding tasks, producing reliable code differentials, and excelling in frontend coding. Agentic coding refers to the ability of the model to independently solve complex coding problems, without requiring constant human intervention. This is a significant advancement over previous models, which often required detailed instructions and guidance to complete coding tasks. GPT-4.1’s ability to produce reliable code differentials, which are used to track changes in codebases, ensures that software development projects are accurate and efficient. The model’s excellence in frontend coding, which involves creating user interfaces, makes it a valuable tool for web developers who want to build user-friendly and aesthetically appealing websites and applications.
Instruction Following: Improved abilities in adhering to designated formats, dealing with multi-turn instructions, and reducing unwarranted overconfidence in responses. The ability to adhere to designated formats is crucial for ensuring consistency and accuracy in the model’s outputs. This is particularly important in applications such as report generation, data analysis, and document summarization, where the format of the results is critical. GPT-4.1’s improved ability to handle multi-turn instructions, which involve complex and interactive dialogues, makes it more suitable for applications such as chatbots, virtual assistants, and interactive simulations. The reduction in unwarranted overconfidence ensures that the model is more transparent and reliable, providing users with more accurate and trustworthy information.
Long Context Processing: Efficiently retrieving and processing information from inputs of up to 1 million tokens. This allows the model to handle large amounts of data without losing context or accuracy, making it ideal for applications such as text summarization, document analysis, and knowledge management. GPT-4.1’s ability to efficiently retrieve information from long inputs ensures that users can quickly and easily access the information they need, while its enhanced processing capabilities allow it to analyze and understand complex data patterns.
These improvements make GPT-4.1 an invaluable tool for developers working in diverse fields, as it provides precision, dependability, and efficiency. It is also built to solve the most difficult engineering challenges, ensuring that users obtain the best outcomes in all applications. Whether it’s developing cutting-edge software, analyzing vast amounts of data, or creating engaging user interfaces, GPT-4.1 provides the tools and capabilities that developers need to succeed.
Vision and Multimodal Capabilities
The GPT-4.1 family is excellent at comprehending imagery and processing videos without any subtitles, making it suitable for multimodal applications. This capability opens up new possibilities for AI-powered solutions in areas such as video analysis, image recognition, and multimedia content creation. The ability to process videos without subtitles is particularly valuable, as it allows the model to understand and interpret video content even when audio is not available or when the subtitles are not accurate. This makes it suitable for applications such as video surveillance, content moderation, and automated video editing. The models can analyze visual data to extract information, identify patterns, and generate insights that would be difficult or impossible to obtain through traditional text-based analysis.
Accessibility and Pricing
The GPT-4.1 series models are broadly accessible to all developers, with their efficiency upgrades resulting in lower prices. This makes advanced AI technology more accessible to a wider range of users, enabling smaller businesses and individual developers to leverage the power of GPT-4.1 without incurring prohibitive expenses.
- GPT-4.1 Pricing:
- Input: $2.00
- Cached Input: $0.50
- Output: $8.00
- Blended Pricing: $1.84
- GPT-4.1 Mini Pricing:
- Input: $0.40
- Cached Input: $0.10
- Output: $1.60
- Blended Pricing: $0.42
- GPT-4.1 Nano Pricing:
- Input: $0.10
- Cached Input: $0.025
- Output: $0.40
- Blended Pricing: $0.12
These pricing structures make it easy for developers to choose the model that best fits their needs and budget, ensuring that they can access the AI capabilities they need without overspending. The availability of different pricing options allows developers to experiment with different models and configurations, optimizing their applications for performance and cost-effectiveness.
Applications of GPT-4.1 in Coding Tasks
GPT-4.1 is designed to address a number of crucial areas in coding. These consist of agentically solving coding problems, code differentials, and frontend coding.
Agentic Coding: GPT-4.1 offers improved agentic coding capabilities, meaning it can independently solve complex coding tasks. This enables it to manage big projects and address issues without consistent human intervention. The model can analyze code, identify errors, and suggest solutions, reducing the need for developers to spend hours manually debugging code. This improves productivity and allows developers to focus on more creative and strategic tasks. Agentic coding accelerates the development process, making it easier for developers to build and maintain complex software systems.
Reliable Code Differentials: With the help of GPT-4.1, producing reliable code differentials is simple. This ensures that modifications to codebases are accurate, which lowers the chance of errors and streamlines the version control procedure. Code differentials are an essential part of software development, allowing developers to track changes in codebases and ensure that all modifications are properly documented and tested. GPT-4.1’s ability to generate accurate code differentials simplifies the version control process, making it easier for developers to collaborate and maintain the integrity of their code.
Frontend Coding: GPT-4.1 is extremely good in frontend coding, making tasks like producing user interfaces more effective. Its superior effectiveness in this area speeds up the web development process and generates user-friendly, aesthetically appealing layouts. Frontend coding is a critical aspect of web development, involving the creation of user interfaces that are both visually appealing and easy to use. GPT-4.1’s expertise in frontend coding enables developers to build high-quality websites and applications more quickly and efficiently. The model can generate HTML, CSS, and JavaScript code, automating many of the tedious and time-consuming tasks involved in frontend development.
Instruction Following Excellence
GPT-4.1 improves on instruction following by enhancing formatting, managing multi-turn instructions, and decreasing overconfidence.
Improved Format Compliance: GPT-4.1 is better at complying to required formats, which encourages uniformity across all outputs. This enhances the consistency and dependability of the information it produces. Format compliance is essential for ensuring that the model’s outputs are consistent and easy to understand. This is particularly important in applications such as report generation, data analysis, and document summarization, where the format of the results is critical. GPT-4.1’s improved format compliance ensures that developers can rely on the model to produce consistent and accurate results, regardless of the input.
Multi-Turn Instructions: It skillfully manages multi-turn instructions and accurately understands and carries out requests that need several interaction steps. This is indispensable for interactive applications that need sophisticated discourse. Multi-turn instructions involve complex and interactive dialogues, requiring the model to maintain context and understand the intent of the user. GPT-4.1’s ability to handle multi-turn instructions makes it more suitable for applications such as chatbots, virtual assistants, and interactive simulations, where the ability to engage in natural and engaging conversations is essential.
Reduced Overconfidence: One significant improvement is its enhanced management of overconfidence, when a model delivers responses that are excessively positive about information that is uncertain. With this improvement, GPT-4.1’s confidence is more closely aligned with the facts, which prevents inaccurate or misleading data from being spread. Overconfidence can be a significant problem with AI models, leading them to provide inaccurate or misleading information with unwarranted certainty. GPT-4.1’s reduced overconfidence ensures that the model is more transparent and reliable, providing users with more accurate and trustworthy information.
GPT-4.1 for Long Context Processing
GPT-4.1 optimizes long context management by effectively obtaining from input up to 1 million tokens, which greatly improves its capacity to manage large amounts of data.
Efficient Retrieval: GPT-4.1 assures that information can be rapidly and reliably obtained from extensive datasets by effectively retrieving it from up to 1 million tokens. This is especially helpful in context-heavy applications like text summarization and analysis. Efficient retrieval of information from long inputs is crucial for applications such as text summarization, document analysis, and knowledge management, where the ability to access and process large amounts of data is essential. GPT-4.1’s efficient retrieval capabilities enable it to quickly and easily access the information it needs, regardless of the size of the input.
Enhanced Processing: GPT-4.1 employs innovative mechanisms that promote processing performance and accuracy while managing such a significant context window. Its sophisticated algorithms allow it to properly manage and interpret context, resulting in appropriate and contextually rich insights. Enhanced processing capabilities are essential for handling large amounts of data without losing context or accuracy. GPT-4.1’s sophisticated algorithms enable it to process and understand complex data patterns, providing users with accurate and informative insights. The model’s ability to manage and interpret context is particularly important for applications such as sentiment analysis, topic modeling, and machine translation, where the context of