GPT-4.1: Everything You Need to Know (+ What OpenAI Didn’t Say)
Updated: April 19, 2025
Summary
OpenAI released GPT 4.1, focusing on improved coding instructions with a 1 million token context window. The model includes Nano and Mini versions and boasts enhanced capabilities like intelligence, benchmark comparisons, and cost efficiency. GPT 4.1 offers benefits for developers, can solve coding tasks, and shows promising performance in reasoning and multimodal tasks. Pricing stands at $150 per million tokens with a Flash model release on the horizon, making GPT 4.1 a notable advancement in AI applications.
TABLE OF CONTENTS
Introduction of GPT 4.1 in the API
Comparison with Other Providers
Improved Performance Metrics
Coding and Instruction Following Features
Research Preview and Pricing
Model Performance and Benchmarks
Needle Retrieval Benchmark
Multimodal Reasoning Benchmarks
Cost Comparison and Recommendations
Conclusion and Future Outlook
Introduction of GPT 4.1 in the API
OpenAI released GPT 4.1 in the API, an improved version focusing on coding instruction following. It is the first OpenAI model with a 1 million token context window.
Comparison with Other Providers
Discussing the naming conventions of GPT 4.1 models like Mini and Nano. Analyzing the performance of the model compared to other providers.
Improved Performance Metrics
Highlighting the enhanced capabilities of GPT 4.1, including intelligence versus latency, benchmark comparisons, and cost efficiency. Exploring the benefits of the Nano model.
Coding and Instruction Following Features
Exploring the coding and instruction following aspects of GPT 4.1, its use cases for building agentic benchmarks, and the availability for developers.
Research Preview and Pricing
Discussing GPT 4.1 as a research preview, its computational intensity, and the pricing at $150 per million tokens.
Model Performance and Benchmarks
Examining the agentically solving coding tasks, benchmark verification, and model comparisons with existing versions and other models in the field.
Needle Retrieval Benchmark
Analyzing the benchmark tests for retrieval tasks, co-reference challenges, and the performance of reasoning models in handling multiple facts retrieval.
Multimodal Reasoning Benchmarks
Discussing the benchmarks on multimodal reasoning, including MMU and MME benchmarks. Comparing GPT 4.1 performance with other models.
Cost Comparison and Recommendations
Comparing the pricing of GPT 4.1 with other models and providing recommendations based on token usage and cost efficiency. Mentioning the upcoming Flash model release.
Conclusion and Future Outlook
Summarizing the key points discussed in the video, highlighting performance, pricing, and the relevance of GPT 4.1 in various applications. Expressing gratitude to the audience.
FAQ
Q: What is the focus of GPT 4.1's improved version?
A: GPT 4.1's improved version focuses on coding instruction following.
Q: What is the significance of GPT 4.1 being the first model with a 1 million token context window?
A: The 1 million token context window in GPT 4.1 is significant as it allows for processing a larger amount of text for better understanding.
Q: What are the naming conventions of GPT 4.1 models like Mini and Nano?
A: The naming conventions of GPT 4.1 models include Mini and Nano, each denoting different variations or sizes of the model.
Q: What are the enhanced capabilities of GPT 4.1 compared to other providers?
A: GPT 4.1 boasts enhanced capabilities in terms of intelligence versus latency, benchmark comparisons, and cost efficiency compared to other providers.
Q: What benefits are associated with the Nano model of GPT 4.1?
A: The Nano model of GPT 4.1 offers benefits such as optimized performance for specific use cases and tasks.
Q: What are some use cases of GPT 4.1 for building agentic benchmarks?
A: GPT 4.1 is utilized for building agentic benchmarks, especially in the context of coding tasks and instruction following.
Q: What is the pricing of GPT 4.1 per million tokens?
A: The pricing of GPT 4.1 stands at $150 per million tokens.
Q: How does GPT 4.1 perform in agentically solving coding tasks and benchmark verification?
A: GPT 4.1 excels in agentically solving coding tasks and performing well in benchmark verification compared to existing versions and other models.
Q: What benchmark tests has GPT 4.1 been analyzed for?
A: GPT 4.1 has been analyzed for benchmark tests related to retrieval tasks, co-reference challenges, and the performance of reasoning models in handling multiple facts retrieval.
Q: What are MMU and MME benchmarks in relation to GPT 4.1?
A: MMU and MME benchmarks are used to gauge GPT 4.1's performance in multimodal reasoning scenarios.
Q: How does the pricing of GPT 4.1 compare with other models in the field?
A: The pricing of GPT 4.1 is compared with other models, allowing for recommendations based on token usage and cost efficiency considerations.
Q: What is the upcoming model release mentioned in the file?
A: The upcoming model release mentioned is the Flash model.
Q: Can you summarize the key points discussed about GPT 4.1 in the file?
A: The key points discussed about GPT 4.1 include its performance, pricing, and relevance in various applications.
Q: What types of benchmarks were examined for GPT 4.1 in the file?
A: GPT 4.1 was examined in benchmarks for multimodal reasoning and handling of reasoning tasks.
Q: What are some of the aspects related to GPT 4.1 as a research preview?
A: GPT 4.1 is discussed as a research preview, considering its computational intensity and advanced capabilities.
Q: What are the attributes of GPT 4.1 that make it beneficial for developers?
A: GPT 4.1 offers benefits to developers in terms of its availability and ability to assist in coding tasks and instruction following.
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