About hvilkenAI
hvilkenAI is an independent benchmark service that evaluates over 350 AI models daily and presents the best results for Norwegian, Swedish and Danish.
Completely independent — no commercial ties
We have no affiliate agreements, sponsors or commercial partnerships with the AI providers we test. We receive no commission, discounts or benefits from any model provider. We are funded by subscription revenue from Pro users and advertising — never by the providers we evaluate.
What we do
Editorial process
The benchmark is updated regularly with results from standardised tasks in Nordic languages.
We explain what the results mean for writing, research, business use, price and API decisions.
If data or methodology needs adjustment, we update the content and correct old conclusions.
Who we are
hvilkenAI was founded by Erik Rundhaug, a Norwegian software developer and AI enthusiast based in Oslo. The motivation was simple: the AI market changes quickly, and it is hard to know which model actually works best for everyday use in Nordic languages.
We fill that gap. Daily, independent and in the languages people actually use.
How we build trust
- We clearly state that over 350 models are evaluated, while the strongest results are presented.
- We label commercial surfaces clearly and do not use affiliate links to model providers.
- We provide a contact point for questions, errors and feedback about the benchmark.
- We explain the method at a high level without publishing proprietary scoring or weighting.
Contact
Questions, feedback or want to report an error? Feel free to get in touch.
Why we exist
Most AI rankings are based on global tests, marketing claims or subjective impressions. That is less useful if you need strong Norwegian, Swedish or Danish output, compare models for Nordic customers or choose a model that works reliably in a Scandinavian workflow. HvilkenAI was built to cover that gap.
We make the results practical by combining benchmarks, guides and explanations. The goal is to help you quickly understand which model fits writing, research, coding, price, privacy or API use — without relying only on provider marketing.
How we judge quality
Quality is not just one impressive answer. A good model must follow instructions, keep language and format, remain stable over time and give value relative to price and speed. That is why we explain both top results and practical trade-offs.
The exact scoring is proprietary, but the principle is simple: recommendations should be useful for real users, not just look good in a table. When we write guides, we try to show when a model is the right choice — and when a cheaper, faster or safer alternative may be better.
See also
How to use this page in practice
This page is written to help you make a concrete decision, not just read general AI information. Start by clarifying the task, the risk and how much you are willing to pay. Then compare the recommendation with today’s benchmark, the AI test and any relevant privacy or data requirements.
For personal use, it is often enough to try a free or low-cost option first. For work, customer content, documents or sensitive information, place more weight on control, source handling, language quality, stability and who processes the information. A good AI model is not only the one that scores highly on one day, but the one that gives reliable answers in your workflow.
HvilkenAI evaluates over 350 AI models and presents the most important signals without sponsored placements or affiliate ranking. Use the page as a starting point: test two or three options on the same task, check how much editing remains and choose only when you see which model actually saves time.
When to be extra careful
Be especially careful when the AI answer will be used in client work, hiring, finance, health, legal assessment or other work where mistakes can have consequences. In those cases, do not look only at price or top score; also consider control, data sharing, internal approval and how easy it is to explain why the tool was chosen.
A good choice is often the one with the lowest total risk: strong enough quality, clear limitations, reasonable cost and a workflow where humans still quality-check the result.