OpenAI launched GPT-4.1 as a new series of AI models that specialize in coding and instruction-following operations. The GPT-4.1 lineup consists of three models, including GPT-4.1 and its smaller versions, GPT-4.1 mini and GPT-4.1 nano, which have a 1-million-token context window. The models can handle text input of 750,000 words simultaneously, which exceeds the word count of War and Peace.
These models are only accessible through OpenAI’s API, and developers specifically need them. GPT-4.1 launched into the market as both Google’s Gemini 2.5 Pro and Anthropic’s Claude 3.7 Sonnet experienced growing competition in coding benchmark performance.
OpenAI seeks to develop AI systems that will achieve complete autonomy in software engineering tasks. As per CFO Sarah Friar, the company seeks to develop software engineering agents who will manage all stages of development until final documentation is ready.
The GPT-4.1 model received developer recommendations to improve front-end coding performance and minimize formatting errors while maintaining document structure consistency, according to OpenAI. The full GPT-4.1 model achieves superior performance compared to GPT-4o on SWE-bench, scoring between 52% and 54.6%. However, it still trails behind Google and Anthropic’s models.
The mini and nano versions of GPT-4.1 emphasize operational speed and operational efficiency instead of precise accuracy. GPT-4.1 Nano, the cheapest and fastest, costs just $0.10 per million input tokens.
Despite its capabilities, GPT-4.1 isn’t flawless. The system becomes less accurate when dealing with longer inputs while demanding exact instructions because of its literal interpretation. The model proved its strong multimodal aptitude by achieving 72% accuracy in Video-MME evaluations of the longest videos without subtitles.
GPT-4.1 advances AI-assisted coding, although it lacks absolute perfection as major technological companies compete to shape the future of programming technology.
