Introduction of the GPT-4.1 Model Family and Strategic Shift Towards API-First Development
#DailyNews
OpenAI has announced and released the GPT-4.1 family of models (4.1, 4.1 mini, and 4.1 nano), exclusively available via the API. These models represent a significant leap forward in performance, particularly in coding, instruction following, and long context comprehension, while being more cost-effective and faster than previous generations. Notably, the release of GPT-4.1 coincides with the deprecation of GPT-4.5 Preview in the API, signaling a strategic shift towards prioritizing API-based developer tools and optimizing GPU resource allocation. The 4.1 family features a 1 million token context window across all models and a refresh knowledge cutoff of June 2024. Early benchmarks and real-world testing demonstrate substantial improvements in various tasks, making GPT-4.1 a powerful tool for developers and enterprises building agentic systems and working with complex documents.
Key Themes and Important Ideas:
API-First Strategy: The GPT-4.1 family is exclusively available through the API. This is a deliberate decision by OpenAI to tailor these models specifically for developers and programmatic use cases. This strategy allows for greater flexibility, scalability, and integration into various applications. The source states, "this model was specifically tailored for developers to use and GPT4.5 is actually getting deprecated in favor of 4.1 more on that later and so there's a bunch of exciting announcements i'm going to go over them all i'm going to show you some demos and let's get into it here's the blog post introducing GPT4.1 in the API and I found it very interesting that it's literally only available in the API." While it's currently only available via the API, the improvements are being gradually incorporated into the latest version of GPT-4 available in ChatGPT.
Significant Performance Improvements: GPT-4.1 models outperform GPT-4o and 4o Mini across the board, with major gains observed in key areas:
Coding: GPT-4.1 scores 54.6% on SWE-Bench Verified, a 21.4% improvement over GPT-4o and a 26.6% improvement over GPT-4.5, making it the leading model for coding. The ability to generate code diffs (editing specific lines) with high accuracy is a notable improvement for efficiency and cost-saving. Real-world examples from Windsurf and Qodana show significant increases in coding accuracy and efficiency. Windsurf saw a 60% higher score on their internal benchmark and users reported being "30% more efficient in tool calling and about 50% less likely to repeat unnecessary edits or read code in overly narrow incremental steps." Qodana found 4.1 produced the "better suggestion in 55% of cases" across real-world pull requests.
Instruction Following: GPT-4.1 scores 38.3% on Scale's MultiChallenge benchmark, a 10.5% increase over GPT-4o. OpenAI's internal instruction following evaluation on the "hard subset" shows 4.1 achieving 49% accuracy compared to 29% for 4o. This highlights the model's improved ability to strictly adhere to complex instructions, including formatting, ranking, and multi-turn conversations. The source emphasizes, "it now strictly follows all the instructions that you provided."
Long Context Understanding: All 4.1 models feature a 1 million token context window. More importantly, they are highly effective at utilizing this large context. On Video-MME, a benchmark for multimodal long context, GPT-4.1 sets a new state-of-the-art with 72% accuracy on the long no subtitles category, a 6.7% improvement over GPT-4o. The "needle in a haystack" demo successfully retrieves a single distinct line from a 450,000 token log file, demonstrating the model's ability to find specific information within massive documents. The source states, "needle in a hay stack if you're going to have a massive context window a million token context window being able to actually utilize that context window effectively is just as important as having it to begin with and what do we see here we have a perfect score successful retrieval up to a million tokens and 100% successful retrieval".
Multimodal Understanding: GPT-4.1 is a multimodal model. On MMU accuracy (answering questions on charts and multimodal data), 4.1 beats 4o by about 6%.
Cost and Latency Advantages: The 4.1 family offers significantly improved performance at a lower cost and with reduced latency, particularly the 4.1 Mini model.
Pricing: GPT-4.1 is priced at a blended total of $1.84 per million tokens. GPT-4.1 Mini is significantly cheaper at $0.42 per million tokens, while GPT-4.1 Nano is the most cost-effective at $0.12 per million tokens. The source highlights, "now the cost is especially important when you're talking about giving an API to a developer because then you are not limited by how fast a human can type out a prompt and get the response it is programmatic and so the potential cost of using these models is much much higher exponentially higher". They are also not charging extra for the long context window.
Latency: The 4.1 models, especially 4.1 Nano, are much faster than previous models. The source points out that 4.1 Mini, while having similar intelligence to 4o in many benchmarks, reduces latency by nearly half.
Deprecation of GPT-4.5 Preview: GPT-4.5 Preview will be deprecated in the API on July 14th, 2025. This decision is attributed to the need for GPUs to power the more usable and efficient API-based 4.1 models. While the deprecation might cause inconvenience for developers who recently adopted 4.5, OpenAI states that 4.5 was introduced as a research preview and that they have learned from developer feedback. The source notes, "we will begin deprecating GPT 4.5 preview in the API as 4.1 offers improved or similar performance on many key capabilities at a much lower cost and latency." There is speculation that 4.5, a large compute-intensive model, may be used for distilling smaller models like 4.1 and could potentially reappear in the future after further optimization.
Tailored for Developers and Real-World Utility: OpenAI worked closely with the development community and partners like Windsurf and Box to train the 4.1 models for real-world utility. This collaboration aimed to create models that are not only intelligent but also practical and efficient for various applications. The source states, "what's interesting is they specifically say they trained this model to have realworld utility and they worked with the development community".
Enabled for Agentic Systems: The improvements in instruction following reliability and long context comprehension make the 4.1 models considerably more effective at powering agents and agentic systems. This is seen as a key emerging field and a significant application for these new models.
Differentiation within the 4.1 Family: The 4.1 family consists of three models with varying trade-offs:
GPT-4.1: The flagship model, offering the highest intelligence with improved latency compared to 4o.
GPT-4.1 Mini: A standout model offering a significant leap in small model performance, matching or exceeding 4o in intelligence eval while reducing latency and cost significantly.
GPT-4.1 Nano: The fastest and most inexpensive model, ideal for tasks like classification and autocompletion, offering a 1 million token context window at a very low price.
Refresh Knowledge Cutoff: The 4.1 models have a refreshed knowledge cutoff of June 2024, providing them with more up-to-date information.
Notable Quotes:
"gpt4.1 was just announced and released we have a brand new model it is better in almost every single way to GPT40 and it's significantly cheaper and it is only available via the API and there's a reason for that this model was specifically tailored for developers to use"
"these 4.1 models as it says right here outperform GPT40 and 40 Mini across the board with major gains in coding and instruction following"
"GPT 4.1 scores a 54.6 on the SWE verified improving 21.4% over GPT40 and 26.6% over GPT4.5 which is crazy"
"GPT 4.1 scores 38.3 and a 10.5% increase over GPT40"
"gpt 4.1 sets a new state-of-the-art result scoring 72% on the long no subtitles category a 6.7% improvement over GPT40"
"gpt 4.1 Mini is a significant leap in small model performance even beating GPT40 in many benchmarks it matches or exceeds 40 in intelligence eval while reducing latency by nearly half and reducing cost by 83%"
"every one of these models has a 1 million token context window and speaking of price they're not charging extra for that long context"
"these improvements in instruction following reliability and long context comprehension also make the GPT4.1 models considerably more effective at powering agents"
"we will begin deprecating GPT 4.5 preview in the API as 4.1 offers improved or similar performance on many key capabilities at a much lower cost and latency"
"we have a perfect score successful retrieval up to a million tokens and 100% successful retrieval"
"Other models tend to get very blabby when they're giving you their output in vibe coding scenarios but 4.1 doesn't do that."
Implications:
The release of GPT-4.1 represents a significant step in making advanced AI models more accessible, cost-effective, and practical for developers. The focus on API availability and specific improvements in coding, instruction following, and long context understanding indicates OpenAI's commitment to empowering the developer community and fostering the creation of sophisticated AI-powered applications and agentic systems. While the deprecation of 4.5 may pose short-term challenges, the long-term benefits of the 4.1 family in terms of performance, cost, and usability are substantial. The availability of a 1 million token context window across all models opens up new possibilities for analyzing and interacting with large amounts of data.
F.A.Q
What are the key features of the new GPT-4.1 model family?
The GPT-4.1 family consists of three models: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano. A significant feature is the introduction of a 1 million token context window across all models, a major improvement over previous OpenAI models and a step towards competing with frontier models. These models also demonstrate improved performance across the board compared to GPT-4o and GPT-4.5, with particular gains in coding, instruction following, and utilizing the large context window. They also feature a refreshed knowledge cutoff of June 2024.
How do the GPT-4.1 models perform on coding tasks?
GPT-4.1 shows significant improvements in coding benchmarks. On SWE-bench Verified, it scored 54.6%, a 21.4% improvement over GPT-4o and 26.6% over GPT-4.5, positioning it as a leading model for coding. This includes improved ability to generate code patches (diffs) for specific code sections, which is more efficient than rewriting entire files. Internal testing by partners like Windsurf also showed GPT-4.1 scoring 60% higher than GPT-4o on their coding benchmark, correlating strongly with code change acceptance on the first review.
What are the improvements in instruction following for GPT-4.1?
GPT-4.1 is significantly better at following complex instructions. On Scale's Multichallenge benchmark, it scored 38.3%, a 10.5% increase over GPT-4o. OpenAI's internal instruction following evaluation on a hard subset showed GPT-4.1 at 49% accuracy compared to GPT-4o's 29%. This improvement is crucial for developers building applications and agents that require precise control over the model's output, including formatting, handling constraints, and processing multi-turn instructions.
How does GPT-4.1 handle long context and multimodal understanding?
A key advancement is the 1 million token context window, which GPT-4.1 models are specifically trained to utilize effectively. They demonstrate strong performance in long context comprehension, as shown by successful retrieval in "needle in a haystack" evaluations up to 1 million tokens. GPT-4.1 is also a multimodal model and sets a new state-of-the-art result on Video-MME, a benchmark for multimodal long context understanding, scoring 72% on the long no subtitles category, a 6.7% improvement over GPT-4o.
What is the pricing structure for the GPT-4.1 models?
The GPT-4.1 models are significantly more cost-effective than previous models. GPT-4.1 is priced at $2.00 per million tokens for input and $8.00 for output, with a blended total of $1.84 per million tokens. GPT-4.1 mini is even cheaper at $0.40 for input and $1.60 for output, with a blended total of $0.42. The most cost-effective model is GPT-4.1 nano, priced at $0.10 for input and $0.40 for output, with a blended total of $0.12 per million tokens. Importantly, there is no extra charge for using the full 1 million token context window.
Why is GPT-4.5 being deprecated?
GPT-4.5 preview is being deprecated because GPT-4.1 offers improved or similar performance on many key capabilities at a much lower cost and latency. GPT-4.5 was introduced as a research preview to explore a large, compute-intensive model, and while valuable feedback was gained, the demand for GPUs to power such a large model is high. The GPUs needed for GPT-4.5 are being reallocated to power the more "usable" API-based GPT-4.1 models. OpenAI has indicated that GPT-4.5 may not be permanently retired and could be used for future development, possibly for distilling smaller models like GPT-4.1.
Which GPT-4.1 model is considered the "workhorse" or standout?
While all models in the family offer significant improvements, GPT-4.1 mini is highlighted as a true standout. It shows a huge improvement in intelligence compared to GPT-4 mini with roughly the same latency. It even matches or exceeds GPT-4o in intelligence evaluations while reducing latency by nearly half and reducing cost by 83%. GPT-4.1 nano is ideal for low-latency tasks like classification and autocompletion, while GPT-4.1 is the most capable model in the family.
What are the primary use cases and target audience for the GPT-4.1 API models?
The GPT-4.1 API models are specifically tailored for developers and designed for real-world utility. Their strengths in long context, multimodal understanding, instruction following reliability, and coding make them highly effective for building agentic systems and powering enterprise use cases such as extracting data from complex documents and analyzing large datasets. The lower cost and improved latency make them more suitable for programmatic use via API, enabling faster iteration and smoother workflows for developers.
