How do we actually measure how “intelligent” an AI model is?
One possible answer is provided by the Artificial Analysis Intelligence Index (AAII) — a combined evaluation framework that compares the performance of so-called frontier models across multiple benchmarks. Version 2.2 of the index currently integrates eight test formats that assess different capabilities:
- MMLU-Pro – broad knowledge across more than 50 subject areas
- GPQA Diamond – complex multiple-choice questions with justified reasoning
- Humanity’s Last Exam – linguistic and logical reasoning in an academic context
- LiveCodeBench – practical coding tasks with executable code
- SciCode – scientific reasoning in STEM disciplines
- AIME – mathematical problem-solving with a focus on logical thinking
- IFBench – accurate instruction-following in complex task scenarios
- AA-LCR – logic-based chain-of-thought reasoning tasks
The resulting score shows how well a model performs across all tasks and provides a consistent basis for comparison over time, vendors, and model generations. Looking at recent trends, one thing becomes clear: progress is dramatic, and the performance leaps between generations are huge.
Between GPT-4o, o1-preview, and OpenAI’s current o3 model, only a few months have passed — yet the performance differences are substantial. Models are increasingly unlocking new competence areas, particularly in reasoning. Multistep thinking, problem analysis, and coding are moving into focus, with results that now rival — and in some cases surpass — professional human capabilities.
Multiple models from different providers are now reaching performance levels that would have been unthinkable just a short time ago. Behind them, additional competitive models are emerging — some with minor trade-offs, others sparking controversial debates.
Reasoning as the New Benchmark
Language processing has become the baseline. The real differentiators today lie in the ability to grasp complex relationships, draw logical conclusions, and develop structured solution paths. This is exactly where the more demanding benchmarks apply — and where the largest performance gaps emerge.
This shift has practical consequences. While earlier LLMs were primarily used for text generation and assistant tasks, current models are increasingly suited for highly complex analytical, code-intensive, and strategic workloads.
Architecture & Interchangeability: Flexibility as a Design Principle
With the pace at which new models are released, pressure on technical infrastructures continues to grow. Systems that are tightly coupled to a single model or difficult to adapt quickly fall behind — especially when existing integrations no longer align with the state of the art.
AI systems should be modular, interchangeable, and as model-agnostic as possible from the very beginning. Only then can new releases be integrated quickly without fundamental changes to logic or infrastructure. This level of flexibility not only improves maintainability, but also ensures long-term viability.
Robustness against model changes therefore becomes a core architectural requirement — not an optional feature, but a prerequisite for sustainable scaling.
The current market situation makes one thing clear: there is no longer a single “best” model. OpenAI continues to play a leading role, but providers such as Google (Gemini) and Anthropic (Claude) are on par in many areas. Other players like xAI or Mistral are delivering increasingly competitive results — often with specific strengths in certain benchmarks or use cases.
In this environment, long-term commitment to a single model or ecosystem carries strategic risk. Technological flexibility becomes the key to continuously benefiting from progress while avoiding dependency.
Conclusion
We are not witnessing a linear progression. The development of foundation models follows an accelerated, stepwise pattern — driven by competing vendors, evolving benchmarks, and high dynamics across all dimensions.
The decisive advantage lies not in choosing a specific model, but in the ability to keep pace with model evolution. Organizations that fail to prepare their systems for this reality risk quickly falling behind in a world of continuous AI iteration.