Kauz Selection: The Right AI Model for Every Task

The generative AI market is undergoing constant transformation. New models are released every month, existing models are continuously being improved, and companies are faced with a growing range of technologies to choose from. At the same time, cost pressures are mounting. Modern frontier models are becoming more powerful, but are often significantly more expensive than their predecessors. In addition, token consumption for many applications has risen considerably: While simple chat queries require only a few hundred tokens, deep research tasks, multi-stage AI workflows, or the creation of extensive documents can increase resource requirements many times over. This creates a new tension for companies between achieving the highest possible quality of results and ensuring cost-effective use.

The Kauz Selection was developed specifically to meet this challenge.

The Right Model for Every Task

The Kauz Selection is an intelligent routing system within aiWorkplace that automatically selects the optimal model for each request. Instead of requiring users to manually choose between GPT, Claude, Gemini, or other models, Kauz-Selection analyzes the requirements of a task and decides within milliseconds which model offers the best combination of quality, speed, and cost.

Users no longer have to worry about selecting a model themselves. They simply enter their query as usual, and the appropriate model is selected automatically in the background.

For example, a translation or a short email can be processed by a cost-effective model, while complex contract analyses or demanding research tasks are automatically routed to more powerful reasoning models.

Counteracting Hidden Cost Spikes

Since 2024, the costs of leading frontier models have increased significantly. While GPT-4o started at $2.50 per million input tokens and $10 per million output tokens, prices for GPT-5.5 are now $5 for input tokens and $30 for output tokens. This means that input costs have doubled and output costs have even tripled. However, resource consumption per task is rising even more sharply. A typical chat with GPT-4o used to process around 700 tokens. Modern reasoning queries already require about 5,000 tokens. Agent-based tasks operate on an entirely different scale: the automated creation of a presentation or a moderately complex coding task can consume several hundred thousand tokens. The actual cost explosion is therefore driven not only by higher model prices but, above all, by the increasing complexity of modern AI applications.

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However, most business inquiries do not require the full capabilities of a Frontier model. A summary, a translation, or an internal email can often be processed with smaller models while maintaining comparable quality. The situation is different when it comes to contract analysis, multi-step research, or complex reasoning tasks.

The key question, therefore, is no longer: Which model is the best? But rather: Which model is the best choice for this specific task?

Quality First—Cost Optimization as a Result

However, the development of the Kauz Selection was not focused on reducing costs, but rather on ensuring the highest possible quality of responses. The goal was to select, for each task, the model that achieves the desired quality with the least possible use of resources.

To test the performance of the Kauz-Selection, it was evaluated using 105 test questions from ten different application areas and five difficulty levels. The results show that intelligent model selection can deliver nearly the same quality as leading premium models.

Claude Opus 4.6 achieved an average quality score of 4.68 points. GPT-5.4 scored 4.65 points. The Kauz Selection scored 4.64 points, placing it at virtually the same quality level.

The main difference was in the costs. While Claude Opus 4.6 consumed an average of 27.09 AI points per test case and GPT-5.4 consumed 5.18 AI points, Kauz-Selection required only 1.91 AI points per case.

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The result: costs up to 90 percent lower compared to a strategy that consistently relies on top-tier models for all tasks, without compromising on quality.

These results have since been updated using Opus 4.8 and GPT-5.5. The results remain virtually unchanged.

LLM orchestration is the future

The Kauz Selection is therefore more than just another AI feature. It forms the foundation for the sustainable and cost-effective use of AI within a company. After all, the more AI applications are integrated into business processes and the more complex agent-based workflows become, the more important it is to intelligently balance quality, speed, and costs.