OpenAI has just launched GPT-4.1, a new AI model specifically targeting programming tasks, challenging established favorites like Claude 3.7 and Google’s Gemini 2.5 Pro. With a massive 1 million token context window and improved coding capabilities, could this new release finally make OpenAI competitive in the developer tools space?
OpenAI’s New Family of Models: GPT-4.1, Mini, and Nano
OpenAI has expanded its lineup with three new models – GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano. Each offers different balances between intelligence and speed, with the larger models providing higher capabilities but slower processing times.
The most significant upgrade across all three models is the implementation of a 1 million token context window. This places OpenAI’s offerings ahead of Claude’s 200,000 token limit but still behind Gemini’s impressive 2 million token capacity.
Notably, GPT-4.1 outperforms its predecessor GPT-4o, showing a 22% improvement on the SW Bench Verified benchmark. Even more surprisingly, it actually surpasses the previously released GPT-4.5 in several tests, despite the seemingly confusing version numbering.
API-Only Access: OpenAI’s Strategy Shift
In a significant shift from previous releases, GPT-4.1 will only be available through OpenAI’s API – not through the ChatGPT interface that many users are familiar with. This strategic decision targets developers who typically access AI models through programming tools and IDEs like Cursor or Winsurf.
Simultaneously, OpenAI has announced they’re discontinuing GPT-4.5, which was released in February 2025. The company labeled it an “experiment” and is reallocating GPU resources to other projects, effectively acknowledging that the model didn’t gain the traction they had hoped for.
Context Window Performance: The Real-World Test
To evaluate how effectively GPT-4.1 uses its 1 million token context window, we tested it with two challenging tasks. First, we asked it to find a deliberately inserted anachronism hidden deep within the Bible text. The model quickly identified the phrase “My computer and GPUs are melting” within seconds.
In a more complex test, we created a multi-step puzzle requiring the model to find interconnected clues scattered throughout the document. Both GPT-4.1 and Gemini 2.5 Pro solved this challenge efficiently, demonstrating that these large context windows aren’t just marketing figures but provide practical utility.
Coding Capabilities: Head-to-Head Comparison
We tested GPT-4.1 against Claude 3.7 and Gemini 2.5 Pro on two programming tasks: creating a CNN visualizer for MNIST and developing a Snake game with specific complex requirements.
For the CNN visualizer, GPT-4.1 produced significantly better results than GPT-4o, generating functional code with interactive visualization of activation maps. Claude 3.7 created an aesthetically pleasing interface but with some functionality issues, while Gemini 2.5 Pro’s attempt encountered errors loading the model weights.
The Snake game challenge proved difficult for all models, with Gemini 2.5 Pro coming closest to a fully functional implementation. GPT-4.1 showed improvement over GPT-4o but still fell short of a complete solution.
Which Model Should Developers Choose in 2025?
Based on our testing, GPT-4.1 represents a substantial improvement over GPT-4o for programming tasks, making OpenAI competitive again in this space. However, it doesn’t definitively outperform Claude 3.7 in all scenarios, and Gemini 2.5 Pro’s reasoning capabilities give it an edge in complex implementation tasks.
The choice between these models will likely depend on specific use cases, with GPT-4.1 showing strengths in detailed documentation processing and Claude 3.7 continuing to excel in certain coding implementations. For developers working with extremely large codebases, Gemini’s 2 million token context window may provide additional advantages.
OpenAI’s focus on the API market signals recognition that they’ve been losing ground to competitors in the developer tools space. With more AI model releases expected in the coming week, including specialized reasoning models, the landscape for AI-assisted programming continues to evolve rapidly.