With Vibe Coding, developers delegate the technical implementation – syntax, boilerplate, logic structures – almost entirely to a Large Language Model (LLM). People no longer act as authors of individual lines of code, but as directors. They use natural language to specify the desired behavior and architecture of the application. The focus shifts from the how of programming to the what of the result – a kind of pair programming in which the human takes on the role of the software architect, creating programmatic processes hand in hand with the machine.
An internal web service with six integrations, production quality, and mixed inventory is not an uncommon project for developers. With a top AI model like Claude Opus 4.6, experience shows that the upper limit of the token budget for this project is around $3,800. For comparison: A single senior developer costs around 800 to 1,200 euros per day in Germany. As soon as Vibe Coding saves more than three to five developer days – including reviews, fixes and stabilization – the token position becomes sustainable. When using weaker open source models, the budget quickly increases sevenfold to over $25,000. But how reliable are these numbers and how can they be estimated in advance?
- A token budget model (TBM) translates project parameters such as complexity, integrations and legacy share into a concrete cost cap for Vibe Coding.
- The dominant cost driver is not the token price, but rather the model quality: weaker models cause exponentially higher total costs due to rework cascades.
- In regulated environments with open source requirements, token budgets increase dramatically – hybrid strategies of AI and manual development can be more economical.
- Fast feedback loops favor machines, expensive correctness favors people.
With StefanAI Solutions, Stefan Müller advises medium-sized businesses and administration on AI projects and trains up to a thousand specialists every year.
This article presents a token budget model (hereinafter TBM) to approximate the costs of Vibe coding. The TBM translates the variables of real software projects – complexity, integrations, quality requirements, legacy share – into a token estimate and thus into a budget limit. The model aims to provide a structured heuristic that forces teams to ask the right questions: Under what conditions does machine-generated code make economic sense?
That was the excerpt from our heise Plus article “Estimating token budgets and costs for Vibe coding”. With a heise Plus subscription you can read the entire article.
