GPT-4.1 is a family of three models - GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano - that outperform the previous GPT-4.0 model across various benchmarks. Some key features and improvements include:

1. Larger Context Window: All three GPT-4.1 models have a massive 1 million token context window, a significant improvement over previous models.

2. Coding Capabilities: GPT-4.1 scores 54.6 on the SWE Verified benchmark, a 21.4% improvement over GPT-4.0 and 26.6% over GPT-4.5. It is now the leading model for coding tasks.

3. Instruction Following: On the Instruction Following Scales Multi-Challenge benchmark, GPT-4.1 scores 38.3, a 10.5% increase over GPT-4.0. OpenAI is also releasing their own instruction following benchmark.

4. Multimodal Understanding: On the Long Video MME benchmark, GPT-4.1 sets a new state-of-the-art result, scoring 72% on the long no subtitles category, a 6.7% improvement over GPT-4.0.

5. Latency and Cost: GPT-4.1 Mini offers a significant leap in small model performance, matching or exceeding GPT-4.0 in intelligence while reducing latency by nearly half and cost by 83%.

6. Enterprise Use Cases: Benchmarks from Box AI Studio show GPT-4.1 significantly outperforming GPT-4.0 in extracting data from complex documents like earnings reports and insurance documentation.

7. Deprecation of GPT-4.5: OpenAI will be deprecating the GPT-4.5 preview in favor of GPT-4.1, which offers improved or similar performance at a much lower cost and latency.

Overall, the GPT-4.1 family of models represents a substantial improvement over the previous generation, with a focus on real-world utility for developers and enterprises.

According to the announcement, OpenAI will begin deprecating the GPT-4.5 preview in the API as the new GPT-4.1 models offer improved or similar performance on many key capabilities at a much lower cost and latency. GPT-4.5 preview will be turned off in 3 months on July 14th, 2025 to allow time for developers to transition.

OpenAI explains that GPT-4.5 was introduced as a research preview to explore and experiment with a large, compute-intensive model, and they have learned a lot from developer feedback. However, they now need the GPUs that were powering GPT-4.5 for the new API-based GPT-4.1 models, which are designed to be more usable and cost-effective.

While GPT-4.5 may be deprecated, it is likely that OpenAI will continue to build upon and refine this large model in the future. The company suggests that the GPT-4.1 models, including the GPT-4.1 mini, are the natural successors to GPT-4.0 and offer substantial improvements in areas like coding, instruction following, and long-context understanding, often matching or exceeding the performance of larger, more compute-intensive models like GPT-4.5 and GPT-3.

Developers are advised to start transitioning to the new GPT-4.1 models, which are available exclusively through the API, as the GPT-4.5 preview will be turned off in the coming months.

According to the transcript, the new GPT-4.1 model from OpenAI outperforms the previous GPT-4.0 and GPT-4.5 models across various benchmarks:

• Coding: On the SWE Verified coding benchmark, GPT-4.1 scores 54.6, a 21.4% improvement over GPT-4.0 and a 26.6% improvement over GPT-4.5. OpenAI claims it is the leading model for coding tasks.

• Instruction Following: On the Scales Multi-Challenge instruction following benchmark, GPT-4.1 scores 38.3, a 10.5% increase over GPT-4.0. OpenAI is also releasing their own instruction following benchmark, on which GPT-4.1 performs well.

• Long-Context Comprehension: On the Long-Video MME benchmark for multimodal long-context understanding, GPT-4.1 sets a new state-of-the-art result, scoring 72% on the "long no subtitles" category, a 6.7% improvement over GPT-4.0.

The transcript also highlights the GPT-4.1 family of models, including the GPT-4.1 Mini and GPT-4.1 Nano, which offer significant improvements in latency and cost compared to the previous models, while maintaining similar or better performance on various benchmarks.

The new GPT-4.1 models from OpenAI offer significant improvements over their predecessors, particularly in the areas of coding, instruction following, and long-context understanding. Here are some key demos and use cases showcased for these models:

Coding Demos

• GPT-4.1 scored a 54.6% on the SWE Verified coding benchmark, a 21.4% improvement over GPT-4.0.
• In a demo with Verun, the CEO of Windsurfer, the new model was shown to be 60% better than GPT-4.0 on Windsurfer's internal coding benchmark, leading to 30% more efficient tool usage and 50% fewer unnecessary edits.
• Kodo also tested GPT-4.1 head-to-head against other leading models and found it produced better code suggestions in 55% of cases, excelling at both precision and comprehensiveness.

Instruction Following

• On OpenAI's internal instruction following evaluation, GPT-4.1 scored 49% accuracy on the "hard" subset, a substantial improvement over the 29% of GPT-4.0.
• For multi-turn instructions, GPT-4.1 showed a significant boost in performance compared to GPT-4.0.
• In a demo, the model was able to successfully retrieve a single line of non-log code hidden in a 450,000-token log file, demonstrating its ability to effectively utilize the 1 million token context window.

Multimodal and Long-Context Understanding

• On the long-context video MME benchmark, GPT-4.1 scored 72%, a 6.7% improvement over GPT-4.0.
• On the MMLU benchmark testing multimodal and long-context understanding, GPT-4.1 matched or exceeded the performance of GPT-4.5 at a much lower cost and latency.

Enterprise Use Cases

• Benchmarks from Box AI Studio showed GPT-4.1 significantly outperforming GPT-4.0 in extracting key information from complex enterprise documents like earnings reports, insurance documentation, and legal contracts.

Overall, the demos and use cases highlight the substantial improvements in GPT-4.1's capabilities, making it a powerful tool for developers, enterprises, and a wide range of applications that require advanced language understanding and generation.

The pricing and cost efficiency of the new GPT-4.1 models are a significant part of their appeal. OpenAI has made a concerted effort to make these models more accessible and affordable for developers.

The pricing breakdown is as follows:

• GPT-4.1: $2 per million tokens for input, $0.50 for cached input, and $8 for output, with a blended total of $1.84 per million tokens.
• GPT-4.1 Mini: $0.40 per million tokens for input, $0.10 for cached input, and $1.60 for output, with a blended total of $0.42 per million tokens.
• GPT-4.1 Nano: $0.10 per million tokens for input, $0.025 for cached input, and $0.40 for output, with a blended total of $0.12 per million tokens.

These prices are significantly lower than the previous GPT-4.5 model, which was considered too expensive and slow for many developers. The introduction of the GPT-4.1 Mini and Nano models, in particular, offers a much more cost-effective solution for developers who need to use these language models in their applications.

The ability to leverage the 1 million token context window without additional charges is also a notable advantage of the GPT-4.1 models. Many of OpenAI's competitors charge extra for access to long context windows, but that is not the case here.

Overall, the pricing and cost efficiency of the GPT-4.1 models make them a highly attractive option for developers who need to incorporate powerful language models into their applications, especially for use cases that require high-volume or programmatic access.

The announcement of GPT-4.1 is a significant development in the world of large language models. This new family of models, including GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano, offer substantial improvements over their predecessors, GPT-4.0 and GPT-4.5.

The key highlights of GPT-4.1 include:

• Outperforming GPT-4.0 and GPT-4.5 across a range of benchmarks, with major gains in coding and instruction following.
• Offering a massive 1 million token context window, a significant improvement over previous models.
• Providing a more efficient and cost-effective solution for developers, with GPT-4.1 Mini being the standout model in terms of performance and pricing.
• Demonstrating impressive capabilities in tasks like multimodal understanding, math reasoning, and chart/document comprehension.
• Introducing a new internal instruction following evaluation, showcasing the model's ability to strictly follow complex sets of instructions.

The decision to make GPT-4.1 exclusively available through the API, rather than the ChatGPT interface, suggests a focus on serving the developer community and providing a more tailored solution for their needs.

While the deprecation of GPT-4.5 may cause some disruption for developers, it appears to be a necessary move to allocate resources towards the more efficient and capable GPT-4.1 models. Overall, the introduction of GPT-4.1 represents a significant step forward in the evolution of large language models, offering improved performance, cost-effectiveness, and real-world utility for developers and enterprises.