Models / unbiased/ Pareto

Pareto

unbiased · released Sep 17, 2026

Input: text and images. Output: text.InputOutput
Type
Closed
Input
None held
Output
None held
Cached
None held

We don't hold a list price for this model yet · hosted only — we have no record of published weights

Our take

Written Sep 20, 2026

Pareto is a hosted-only model that takes text and images and can hold a long document in one request. Nothing in our data measures how good its answers are, so treat it as a candidate to trial rather than a proven pick.

Who should pick it

Reach for it through a host when your questions arrive with a screenshot or a diagram, or when a long report needs to go in without being split up first. Skip it if you need measured coding, reasoning or chat quality, or if you meant to run the model on your own machine.

The case for it

  • A long report or a stack of documents fits beside the question in one request, though room to hold it is not a guarantee of accurate recall.
  • Images go into the same request as the text, so a screenshot or a diagram does not have to be described in words first.

The case against it

  • No benchmark scores are supplied, so the only way to judge the answers is to trial it on work you can check yourself.
  • We list no download for it, so using it means choosing a host rather than running it yourself.
  • No licence is supplied, so permissions for commercial use, changes or redistribution are unverified in our data.
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How good is it?

We hold no score for this model.

So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.

Where these scores come from →

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Where to rent it

Prices checked between 55 min and 58 min ago — each listing carries its own date.

Cheapest published offer

Unbiased, through OpenRouter

Cheapest of 2 live listings.

per 1M tokens
$2.50 in / $7.50 out
Context served
262K
Throughput
~26 tok/s
Current provider offers with price, context and prompt-privacy answers
ProviderIn / out per 1M tokensContextThroughputTrains on promptsLogs promptsZero retention
OpenRouterOpenRouter's own listing$2.50 / $7.50checked 58 min ago262Knot measuredUnknownUnknownUnknown
UnbiasedThrough OpenRouter$2.50 / $7.50checked 55 min ago262K131K max reply26 tok/sNoYes30 daysUnknown

Across the 2 listings we hold: 1 says it does not train on prompts, 0 say they do and 1 does not say. 0 appear in the zero-retention registry we check; the rest are unknown to us.

What each host's API supports

From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.

API features per host
ProviderTool callingJSON outputStrict schema
OpenRouterOpenRouter's own listing✓✓✗
UnbiasedThrough OpenRouter✓✓✗

Tool calling: 2 of 2 listings say yes. JSON output: 2 of 2 listings say yes. Strict schema: 0 of 2 listings say yes, 2 say no.

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When we formed this view

Recent changes

Sep 18, 2026ListedListed on LLMap
What movedfirst indexed by our pipeline
Sep 17, 2026ReleasePareto listed
Sep 17, 2026AnnouncedPareto announced by unbiased

Each date is the day we first saw the change, or the day the maker announced it.

What we do not know about this model yet

  • No independent board has scored it, so we hold no quality figures at all.
  • Nothing we hold says whether an endpoint streams, so we do not show it either way.
  • 1 of 2 listings does not say whether it trains on prompts.
  • We don't hold a list price for this model yet — the gap is ours, not the lab's.
  • We hold no batch or off-peak rate for any of its listings.
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Licence and identifiers

What the licence allowsWe hold no licence record for this model. Inside are the identifiers you need to pull it — its Hugging Face repo where we have one, our slug and a machine-readable card.

Licence

We hold no licence record for this model, and no record of published weights either — so we can neither summarise its terms nor point you at the weights.

Identifiers

Takes in, gives back
Text and images in, text out
Catalogue slug
unbiased-pareto

Machine-readable model card (omc.json) →

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