Use one fixed representative configuration
Configurations are selected by a fixed reasoning-profile priority (Max, xHigh, High, Medium, Low), never by choosing whichever score looks best.
Independent model intelligenceFind the right AI model,
Find the right AI model,
not just the loudest one.
Compare current model capability, context, modalities, public preference and API economics in one continuously refreshed decision surface.
LMArena models.dev OpenRouter Artificial Analysis when available
How ranking works
Different evaluations use incompatible units and difficulty levels. AITopic converts every result to one capability scale: 50 is the current reference-set average and 15 points is approximately one standard deviation. The calculation runs locally from direct third-party evidence.
Put unlike evaluations on one capability scale
Each evaluation is standardized across the current model set. A score of 50 is the source-set average and 15 points is approximately one standard deviation.
Combine related evidence once
Repeated runs of the same benchmark are grouped so one test cannot gain extra weight. Extreme readings are capped and missing evaluations add neither points nor penalties.
Score domains, then apply public weights
Overall score = Coding 40% + Research 20% + Professional work 20% + Knowledge 15% + Human preference 5%. Prices and speed never affect rank.
Coding 40%Research 20%Professional 20%Knowledge 15%Human preference 5%
Ranking qualification
A numbered rank requires evidence from at least two independent institutions, at least three coding evaluation classes, and at least two evaluation classes in each of research, professional work and knowledge. Models without enough breadth remain visible but are marked “Not yet ranked”. Deprecated and retired releases never receive a current rank; up to 100 qualified models remain numbered.
Frequently asked questions
Why can a difficult benchmark produce a high reading from a modest raw score?
The reading estimates the capability needed to obtain that raw result on that benchmark. Thirty percent on a hard benchmark can carry more information than ninety percent on a saturated one.
How large must a score gap be?
Treat gaps below one point as effectively tied. A gap of roughly three points is usually stable when the evidence mix is resampled.
Why does a model have a score but no rank?
It has usable evidence but has not yet met the independent-institution and domain-breadth requirements above.
Do price and speed change the score?
No. Cache, input and output prices are displayed for product decisions but never enter the capability calculation.
What happens when a source is temporarily unavailable?
The last verified result remains visible with its verification time. A failed synchronization never replaces a valid result with an empty value.