OpenAI has recently launched a new model series called the GPT 4.1, which aims to address the shortcomings of the previous GPT-4 models. The GPT 4.1 series includes three models: GPT 4.1, GPT 4.1 Mini, and GPT 4.1 Nano.

These new models outperform the GPT-4 Omni and GPT-4 Omni Mini across various benchmarks, including coding, instruction following, and long-context performance. They now support up to 1 million tokens of context and utilize it effectively, addressing the "lost in the middle" issues faced by previous models.

The GPT 4.1 model excels in coding tasks, achieving a 54.6% score on the Swaybench verify test, which is a 22% improvement over the GPT-4 Omni. It also outperforms the GPT-4.5 in instruction following and long-context performance, making it a powerful tool for complex coding tasks.

The GPT 4.1 Mini model is particularly impressive, as it beats the GPT-4 Omni in several benchmarks while offering nearly 50% lower latency and 83% cheaper pricing, making it a more appealing option for many use cases.

The GPT 4.1 Nano is the budget-friendly model in the series, providing full support for a 1 million token context window at a very low cost. This makes it well-suited for tasks such as autocomplete, classification, and large document processing.

Overall, the GPT 4.1 series represents a significant improvement in OpenAI's coding capabilities, offering reliable and efficient models that can handle large context windows. These models are poised to be valuable tools for developers and researchers working with complex coding tasks and large-scale document processing.

The new GPT-4.1 models from OpenAI offer a range of pricing options and impressive capabilities:

• GPT-4.1: Priced at $2 per 1 million input tokens and $8 per 1 million output tokens, this model is a solid upgrade over previous versions, with major improvements in coding, instruction following, and long-context performance.
• GPT-4.1 Mini: This lighter model is priced at $0.40 per 1 million input tokens and $1.80 per 1 million output tokens, offering nearly 50% lower latency and 83% cheaper pricing compared to GPT-4 Omni.
• GPT-4.1 Nano: The most budget-friendly option, this model costs $0.10 per 1 million input tokens and $0.40 per 1 million output tokens, making it a powerful and cost-effective choice for tasks like autocomplete, classification, and large document processing.

These GPT-4.1 models excel across the board, outperforming GPT-4 Omni and GPT-4 Omni Mini in key areas:

• Coding: The GPT-4.1 crushes the Swaybench verify test with a 54.66% score, a 22% approximate increase over GPT-4 Omni.
• Instruction Following: The GPT-4.1 demonstrates state-of-the-art performance in this area.
• Long-Context: These models support up to 1 million tokens of context and utilize it effectively, addressing the "lost in the middle" issues of previous versions.

The GPT-4.1 series offers a reliable and versatile solution for a wide range of tasks, from processing large code bases and legal documents to general use cases. With their impressive capabilities, competitive pricing, and support for extensive context, these models present a compelling option for developers and users alike.

To compare the performance of GPT-4.1 and Gemini 2.5 Pro on coding tasks, we conducted several tests:

1. Responsive Frontend Generation: Both models were tasked with generating a responsive frontend for a monthly income and expense tracking app. The Gemini 2.5 Pro model produced a visually appealing design, but it did not function correctly. The GPT-4.1 model, on the other hand, generated a working frontend, although it was not as visually polished as the Gemini 2.5 Pro output.

2. Channel Switching TV Simulation: The models were asked to create a simulation of a TV screen with different channels that could be switched using number keys. The Gemini 2.5 Pro model generated a more visually impressive and responsive simulation, while the GPT-4.1 model also produced a functional and creative channel lineup.

3. Butterfly SVG Generation: Both models were challenged to create an SVG representation of a symmetrical butterfly. The Gemini 2.5 Pro model produced a more accurate and balanced butterfly shape, while the GPT-4.1 model's output had a slightly inward-leaning shape.

4. Tetris Game in 3.js: The final test involved generating a functional Tetris game in 3.js. The Gemini 2.5 Pro model produced a partially working game, with some issues in the game mechanics. The GPT-4.1 model, however, generated a fully functional and visually appealing Tetris game.

Overall, the Gemini 2.5 Pro model demonstrated stronger visual design capabilities, while the GPT-4.1 model excelled in generating functional and creative coding solutions. The GPT-4.1 model's strengths lie in its larger context window, faster response times, and reliable function calling, making it a potentially better choice for processing large code bases and documents. However, the Gemini 2.5 Pro model remains a powerful reasoning-focused model that may be more suitable for certain complex coding tasks.

Both the Gemini 2.5 Pro and the GPT 4.1 models were able to generate SVG representations of a butterfly with symmetrical wings and simple styling.

The Gemini 2.5 Pro generated a butterfly SVG that had a good overall symmetry, but the shape of the wings was slightly too inward, needing a bit more spacing.

On the other hand, the GPT 4.1 model generated a butterfly SVG that had a more balanced and symmetrical shape, with the wings and body proportions looking more natural. The symmetry in the antenna and body was also well-executed.

Overall, while both models passed the test, the GPT 4.1 butterfly SVG was slightly preferred due to its more refined and symmetrical representation of the butterfly shape.

Both the Gemini 2.5 Pro and the GPT 4.1 models were tasked with generating a Tetris game in 3.js within a single HTML file. Here's how they performed:

The Gemini 2.5 Pro model was able to generate the main interface for the Tetris game, but the game itself did not seem to be fully functional. While the game could be opened and played, there were some issues with the game mechanics, such as the shapes not dropping properly throughout the frame.

On the other hand, the GPT 4.1 model was able to generate a fully functional Tetris game in 3.js. The main interface was correctly implemented, and the game mechanics, such as the dropping shapes, were working as expected. This is a remarkable achievement, showcasing the capabilities of the GPT 4.1 model in generating complex, interactive applications.

Overall, the GPT 4.1 model outperformed the Gemini 2.5 Pro in this task, demonstrating its strong abilities in coding and game development. The generated Tetris game was a fully functional and well-designed application, which is a testament to the model's capabilities.

The GBT 4.1 series from OpenAI appears to be a solid lightweight upgrade over previous models, offering several key improvements. While it may not be as affordable or performant as the Gemini 2.5 Pro in some areas, it still presents a compelling option for developers.

The GBT 4.1's strengths lie in its large context window, making it well-suited for processing large codebases and documents. It also boasts faster response times and no rate limits, which can be advantageous in certain use cases. Additionally, its strong function calling and tool use reliability make it a reliable choice for complex coding tasks.

When compared to other models like Cloud 3.5 and Sonnet, the GBT 4.1 outperforms them across various benchmarks and is more cost-effective. This makes it a viable alternative for developers seeking a balance between performance and affordability.

While the Gemini 2.5 Pro may still be the preferred choice for deep research tasks, the GBT 4.1 could be the better pick for developers who prioritize speed, long-context processing, and zero throttling. As with any model, it's essential to thoroughly test and evaluate the GBT 4.1 against your specific requirements before making a decision.

Overall, the GBT 4.1 series from OpenAI represents a promising step forward in the world of AI-powered coding and document processing, and it will be interesting to see how it evolves and compares to future model releases.