Based on the author's testing and analysis, GPT-4.1 is an impressive coding model, but may not be the best choice as a daily driver for coding tasks. Here's a summary of the key points:

• On the Ader Polyglot coding benchmark, GPT-4.1 scores around 52%, just behind models like DeepSeq 3 and Gro 3 Beta.
• However, the total cost of running GPT-4.1 is $9.86, while the preview version of Gemini 2.5 Pro, which scores 66% on the same benchmark, is significantly cheaper, especially for outputs under 200,000 tokens.
• Models like Gemini 2.5 Pro and DeepSeq 3 offer better performance-to-cost ratios compared to GPT-4.1, making them more suitable options for many coding tasks.
• For smaller use cases, even models like Gemini Flash and DeepSeq 1 may be better alternatives to GPT-4.1 in terms of cost-effectiveness.
• While GPT-4.1 is a significant upgrade over GPT-4.0, the author believes that OpenAI has not made a strong case for its use given the availability of more cost-effective and similarly performant models.
• The author plans to continue testing GPT-4.1 for more agentic tasks, as benchmarks may not capture the full picture, and there could be specific use cases where GPT-4.1 excels.

The author's testing of GPT-4.1's coding capabilities reveals a mixed performance. While the model excels at simple coding tasks, it struggles with more complex requirements.

For simple tasks, such as creating a modern landing page or a website with a button that displays a random joke, GPT-4.1 was able to generate functional code quickly. The author noted that the model's speed of generation was significantly faster compared to GPT-4.0, and the resulting websites were visually appealing and met the basic requirements.

However, when the author introduced more complex tasks, such as creating an encyclopedia of legendary Pokémon with working image links, GPT-4.1 faced some challenges. Although the model was able to generate the initial code, the image links were non-functional. When the author provided a web search tool, the model was able to update the code with working links, demonstrating its ability to utilize external resources.

The author further tested the model's capabilities by asking it to create a TV channel with number keys, each representing a different genre-inspired animation, and a JavaScript animation of falling letters with realistic physics. In these cases, GPT-4.1 performed well, generating creative and visually impressive results.

The most challenging task for the model was creating an HTML program that simulates 20 balls bouncing inside a spinning heptagon. Despite the author's detailed requirements, GPT-4.1 was unable to produce a working solution, with the balls failing to exhibit the expected collision detection and realistic movements.

Overall, the author's testing suggests that GPT-4.1 is a capable coding model, particularly for simple and creative tasks. However, when faced with more complex requirements and specific instructions, the model's performance may be inconsistent, highlighting the need for further development and testing in more agentic frameworks or workflows.

Despite its impressive coding capabilities, the testing revealed some limitations of GPT-4.1 when it comes to using external tools and modifying existing code:

1. Tool Usage: When provided with a web search tool, GPT-4.1 did not always utilize it effectively. In one instance, it failed to use the tool to find accurate information about the "Model Context Protocol" and "Agent-to-Agent Protocol", instead hallucinating responses. This suggests that the model may struggle to properly leverage external tools and resources when needed.

2. Code Modifications: The tests showed that GPT-4.1 excels at single-shot code generation from scratch, but it had more difficulty with code editing and modifications. For example, the model struggled to create a program that simulates 20 balls bouncing inside a spinning heptagon, even when provided with detailed requirements. This indicates that GPT-4.1 may not be as adept at working with and modifying existing codebases as it is at generating new code from prompts.

These limitations highlight the need for further development and testing of GPT-4.1's abilities in more complex, real-world coding scenarios that involve the use of external tools and the modification of existing code. While the model's single-shot coding capabilities are impressive, its performance in these areas suggests that there is still room for improvement before it can be considered a reliable daily driver for coding tasks.

Here is the body of the "Conclusion" section in Markdown format:

In conclusion, while GPT-4.1 is an impressive coding model, it may not be the best choice for daily coding tasks. The official results on the Ader Polyglot coding benchmark show that GPT-4.1 is behind models like DeepSeq 3 and Gro 3 Beta in terms of performance, and the total cost of running GPT-4.1 is significantly higher at $9.86 compared to more cost-effective options like Gemini 2.5 Pro.

The author suggests that for most use cases, models like Gemini 2.5 Pro or even smaller models like DeepSeq 3 or Gemini Flash may be better options due to their superior performance and lower cost. While GPT-4.1 is a significant upgrade from GPT-4.0, the author believes that there are better alternatives available in the market.

The author also notes that the benchmarks may not capture the full picture, and there could be specific use cases where GPT-4.1 might be a better fit. The author plans to continue testing the model for more agentic tasks to explore its capabilities further.

Overall, the conclusion is that while GPT-4.1 is an impressive coding model, it may not be the most practical choice for most coding tasks due to its high cost and the availability of more cost-effective and performant alternatives.