Unlock Enhanced Performance With GPT-4.1 API
OpenAI has unveiled a powerful new family of models through its API: GPT‑4.1, GPT‑4.1 mini, and GPT‑4.1 nano. Each of these models represents a leap forward in performance, cost efficiency, and instruction handling. While previous models like GPT‑4o served as milestones, GPT‑4.1 offers significant enhancements across the board particularly in coding accuracy, long-context understanding, and instruction adherence.
Importantly, these models support up to 1 million tokens of context and come with an updated knowledge cutoff of June 2024. This boost allows them to digest large inputs and respond with improved coherence. As a result, developers can now rely on GPT‑4.1 for more practical, real-world applications, whether that’s building agents or handling complex tasks independently.
Performance metrics highlight the transformation. On SWE-bench Verified, GPT‑4.1 scored 54.6%, compared to GPT‑4o’s 33.2%, showcasing a marked improvement in software engineering capabilities. Furthermore, in the Aider polyglot coding benchmark, GPT‑4.1 achieved 52–53%, substantially outperforming both GPT‑4o and GPT‑4.5. These models were specifically trained to follow diff formats reliably, which helps reduce token usage and latency two key considerations for developers working with large files.
Faster, Smarter, and More Cost-Effective
Beyond performance, latency and cost improvements also define this release. GPT‑4.1 mini, for instance, matches or outperforms GPT‑4o on intelligence benchmarks while cutting latency nearly in half and reducing cost by 83%. For developers who prioritise speed and affordability, GPT‑4.1 nano emerges as the ideal choice. Despite its compact size, it boasts remarkable scores 80.1% on MMLU and 50.3% on GPQA—making it perfect for tasks like classification, search, or autocompletion.
Transitioning to GPT‑4.1 offers a clear value proposition. OpenAI will phase out GPT‑4.5 Preview by July 14, 2025. This shift allows developers time to migrate to the newer models, which deliver comparable or better results at lower cost and latency. This phase-out reflects OpenAI’s commitment to continuous improvement without compromising affordability.
Real-World Impact and Agentic Potential
While benchmarks matter, real-world effectiveness speaks louder. GPT‑4.1 consistently produces more relevant and precise outputs. Developers using tools like Windsurf and Qodo observed fewer unnecessary edits and improved tool usage. For example, GPT‑4.1 outperformed competing models in generating code suggestions that were accepted on first review. Not only did it demonstrate accuracy, but it also excelled in efficiency—an essential trait for engineering teams seeking faster iteration.
Instruction-following is another area where GPT‑4.1 excels. On Scale’s MultiChallenge benchmark, it achieved a score of 38.3%, representing a 10.5% improvement over GPT‑4o. Furthermore, IFEval results showed a jump to 87.4% accuracy, reinforcing the model’s ability to adhere to even the most complex directives. These improvements are key for applications that demand high reliability, especially those in regulated industries or requiring precision formatting.
Blue J, for instance, reported a 53% increase in accuracy for real-world tax scenarios. Likewise, Hex experienced nearly double the performance on SQL evaluations. This illustrates GPT‑4.1’s readiness to support mission-critical systems across domains.
As developers continue to push the boundaries of AI applications, GPT‑4.1’s enhanced long-context understanding and more reliable instruction handling ensure it can keep up with evolving demands. Whether it’s powering agents or generating full applications, this model family represents OpenAI’s most capable offering yet, one that is faster, smarter, and more accessible than ever.
