Who this is for
Consultants, developers and teams that need to choose AI models based on actual results for Scandinavian-language work.
Access benchmark data
Daily AI test results for Norwegian, Swedish and Danish — for anyone who needs to know which models actually work for Scandinavian languages.
What the data is used for
Model selection, internal AI policy, cost control, vendor evaluation and routing between multiple models.
FreeAvailable now
- ✓Daily results — at hvilkenai.eu/ai-benchmark — updated every day
- ✓Weekly email report — with highlights — top 5, key metrics and observations
- ✓Open benchmark methodology — run daily against the same models with identical tests
- ✓API access (free) — yesterday's top 3 models, delayed by 24 hours, 100 calls per hour. Perfect for following trends.
Extended access
Extended data access
Real-time data and full access for businesses and developers. We agree on scope and access directly.
- ✓All models, real time — no 24h delay, full ranking
- ✓Full history — via API
- ✓All languages — Norwegian, Danish and Swedish together
- ✓Extended analysis — price, response time, consistency and value signals per model
- ✓1,000 API calls per hour — compared with 100 on the free tier
How benchmark data can be used
The data is most useful when connected to a concrete decision: which model should be used, when should it change, and what does quality cost in practice?
Model selection
Use history and daily results to choose models for writing, research, coding or customer dialogue.
Cost control
Compare quality against price and response time before locking into one provider.
AI routing
Let an internal system choose a fast, low-cost or strong model based on task requirements.
What you do not get
We do not share proprietary weights, test prompts or raw internal scoring in public access. That makes the data useful without making the benchmark easy to manipulate. Extended access is therefore about more history, broader coverage and better integration — not publishing the full internal method.
When data should be used carefully
Benchmark data should not be the only basis for legal, medical, financial or other high-consequence decisions. Use the data to choose candidates, monitor changes and ask better questions. Combine it with your own tests, expert judgement and internal requirements before production use.
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How to get more value from this data basis
Use this data basis as a practical decision aid, not as an advertising list. HvilkenAI is built to make AI selection easier when the market changes quickly and model names alone say little about real quality.
Start with the task you actually need to solve. A model that is good at short chat is not automatically best for long documents, code, customer-facing text, research or work involving sensitive information. Consider language quality, price, speed, stability and how much editing the answer requires before choosing a subscription or API.
We evaluate over 350 AI models and present the most useful signals in a format that works for everyday users, students, businesses and developers. The exact calculation is proprietary, but the assessment is based on standardised tasks, updated benchmark data and editorial review of whether the recommendations make sense in practical use.
For important decisions, run a small test of your own: use the same prompt, language and expected format in two or three relevant models. Look at precision, source handling, tone, speed and how easy the answer is to verify. That makes the benchmark a time-saving starting point, not a final answer you must follow blindly.
Use the links on the page as well. One page rarely gives the whole picture: the benchmark shows today’s numbers, the AI test translates your need into concrete recommendations, and the privacy/terms pages explain which data and limitations you should consider. Used together, these sources make the choice more robust than following a model name from social media.
If the result will be used at work, document why the tool was chosen: which task it solves, which alternatives were considered, which data should not be shared and how the answer will be quality-checked. That makes it easier to switch model later if price, quality or provider terms change.
When a page is included in the sitemap, it should also stand on its own as a useful explanation. That is why we emphasise practical choices, concrete control questions and clear limits for how data can be used.
The goal is that the reader can move on with a safer choice after a few minutes, without already knowing every model or provider.
Pay particular attention to whether the recommendation fits the language, price and risk in your own situation.
In short: choose what you can verify.
When free is enough
Choose free or low-cost first when the task is simple, risk is low and you can verify the answer yourself.
When paid is worth it
Paid models can be worth the cost when documents are long, precision matters or you save real working time.
When to wait
If the need is unclear, start with the test and benchmark before committing to a provider.