{"id":14613,"date":"2025-07-01T21:09:21","date_gmt":"2025-07-01T19:09:21","guid":{"rendered":"https:\/\/www.bayootec.com\/?p=14613"},"modified":"2026-05-28T12:48:06","modified_gmt":"2026-05-28T10:48:06","slug":"coding-with-copilot-new-models-new-features-in-check","status":"publish","type":"post","link":"https:\/\/www.bayootec.com\/en\/blog-en\/coding-with-copilot-new-models-new-features-in-check\/","title":{"rendered":"Coding with Copilot – New models, new features in check"},"content":{"rendered":"

New AI models at a glance<\/h2><\/div>

Software engineering is undergoing fundamental change. Not gradually, not theoretically – but noticeably in everyday working life. The new language models such as Claude Sonnet 4, GPT-4o or Gemini 2.5 Pro are changing the way code is created. They automate the repetitive and create space for the essentials. Combined with GitHub Copilot, they create workflows that do more than just save time: They redefine quality standards. <\/p>\n

But hand on heart: how much of it is hype and what really works? Our developers put the new models through their paces. The results? Surprisingly practical. <\/p>\n<\/div>

Claude Sonnet 4:<\/h3><\/div>

Particularly fast and efficient, it is suitable for everyday tasks such as commit analyses or the generation of activity reports.<\/p>\n<\/div>

GPT-4o:<\/h3><\/div>

Strong in the multimodal area – ideal for processing images, UI mockups or visual documentation.<\/p>\n<\/div>

Gemini 2.5 Pro:<\/h3><\/div>

The context king among the current models. With a window of up to 2 million tokens, even huge code bases or entire projects can be analyzed at once. <\/p>\n<\/div>

Claude Opus 4:<\/h3><\/div>

Not yet integrated into GitHub Copilot, but promising in terms of long-term planning and logical coherence.<\/p>\n<\/div>

All in all, these models offer a strong arsenal for use in productive development environments – provided they are used in a targeted and context-aware manner.<\/p>\n<\/div>

The practical test: Booking entries from
Git commits<\/h2><\/div>

A question that has kept our developers busy: Can booking entries be generated automatically from commit data? The idea behind it: If developers commit regularly anyway, it should be possible to use this information to generate activity records. <\/p>\n

This is exactly what was tested – with success. Claude Sonnet 4 was instructed by prompt to extract all commits from Git repositories within a certain date range. The answer came in the form of structured JSON files with commit descriptions, changed files and timestamps. <\/p>\n

The quality of the results was impressive: the automatically generated texts matched the actual work process with astonishing precision. In many cases, the result was even better structured than manually generated reports. <\/p>\n<\/div>

Claude shows what modern AI can do<\/h2><\/div>

Compared to earlier models, Claude showed clear advantages Here<\/p>\n