What Is the Best LLM?

Many companies ask themselves this question when considering the use of generative AI. I can actually keep it short and anticipate the answer: THE universally best large language model (LLM) does not exist. If you’re now thinking “Great, why am I even reading this?”, I can tell you that there may be the best LLM for you. But there are certainly the best AI models for your specific tasks. Together we’ll find out which one.

Diversity Rules Not Only Apply to People

The current LLM landscape is characterized by an incredible variety of specialized models. There are over 700,000 models available on Hugging Face alone and we at moresophy now also work with over a dozen models.

How to Find the Right AI Model Without Searching for It

The hunt for the one “super LLM” often ends in flying blind. Because benchmarks show: Every top model only wins on a single race track – sometimes in solving code errors, sometimes in high school math, sometimes in logical thinking. The real leverage for companies therefore lies not in model shopping, but in three supposedly simple questions:

1. Which task is to be solved and which goal is to be supported?
2. The blind spot: Is my data even prepared for this? Read here why this is important.
3. Who will end up using the application?

Everything else is just the technical doormat of a clearly defined use case.

The Fairy Tale of the All-Rounder

Long context windows and trillions of parameters sound impressive, but real comparative studies always show the same pattern: Claude shoots ahead in coding, Gemini dominates math olympiads, OpenAI o3 comes out on top in complex reasoning. At the same time, hallucination rates increase as soon as models enter unfamiliar territory. This is due to the different training approaches and architectures of the models.

In short: size creates reach, but not universal competence.

Three Questions to Help Identify the Best LLM

Clarify Tasks Instead of Listing Models

Before a prompt is written, it must be clear whether the task requires creative text variety, hard numerical logic or strictly regulated technical terms. The more precise the problem description, the smaller the number of seriously suitable models. We also only work with AI models that meet the requirements of the EU AI Act. These are both in-house developments and open source models that we optimize for the task at hand.

Data as the Pivotal Point

Large cloud LLMs work with global knowledge, not with your specific company knowledge. If the AI is supposed to work with your data, but information is stored in silos and cannot be compared with each other due to its nature, even the most expensive model will hallucinate. An AI can only be as good as the data it is fed with. For us at moresophy, this is the most important point in the whole process – after all, the data forms the foundation.

A clean database is therefore far more decisive for the quality of results than the choice between GPT and Claude.

One LLM, Many Users: How to Find the Right LLM for Each Target Group

A chatbot with assistants for internal specialist audiences needs different answers, tonalities and risk limits than a public self-service app. Governance requirements (AI Act, GDPR, DORA or BaFin) set additional guidelines for model selection and deployment. Sales also asks different questions than the HR team, and management needs a 10,000-foot view of all relevant data rather than the detailed reports that Controlling works with. Different user groups therefore have different requirements that need to be taken into account and different data that needs to be accessed.

Hybrid AI: Because Generative Alone Is Not Enough

Pure generative models deliver creativity and often too much of it. Hybrid approaches combine analytical ML processes with LLM output and control the dialog in a data-driven manner. A few months ago, Gartner predicted that by 2027, three times more domain-specific models will be productive than generalists.

DAPHY^® – The Hybrid Modular System from MORESOPHY

In CONTEXTSUITE, we combine generative and analytical AI models with DAPHY^® as the orchestrator in the middle. DAPHY^® generates data-driven, controlled prompts on the fly during the process (keyword: data-driven prompting), transfers them to the generative AI together with the data relevant for the answer and finally delivers the answer including the underlying sources. This significantly reduces the hallucination rate while increasing the precision of the answers.

Audits Instead of Anecdotes: How to Choose Models Objectively

Public leaderboards and benchmarking are a good start, but rarely reflect industry-specific and almost never company-specific requirements.

In the AI Model Audit, we at moresophy compare various AI models against gold standard question sets that we develop together with our customers. Precision, latency and costs are measured, translated into an evaluation and deviations are presented transparently.

This provides companies with a reliable basis for decision-making without having to delve deep into prompt engineering.

Maybe something for you too?