Elorian launches Glimpse and DecoyCount benchmark
Elorian launched its first visual reasoning model, Glimpse, and co-released the DecoyCount benchmark with Mercor. The benchmark tests whether models genuinely count objects or rely on visual shortcuts and substitution heuristics. According to the accompanying paper, all evaluated frontier models scored below 50%, with GPT-6 Astra highest at 43.17%. Elorian CEO Andrew Dai said Glimpse scored 50.48% on the benchmark’s 1,660 items in an internal test that was not included in the paper. He said the evaluation images and text were withheld from Glimpse’s training and that a contamination check found no overlap in the data checked. Dai also said Glimpse’s result came from a single run without an agent or tool harness, while the six models in the paper were averaged across three runs. Elorian describes Glimpse as a visual thinking system that generates and edits images as part of its reasoning process, distinguishing the company’s approach from world models.
DecoyCount provides a focused test of whether models count visible objects reliably or rely on learned cues. Its results also show a protocol difference: the paper averaged three runs for six models, while Glimpse’s claimed 50.48% result came from one internal run.
This month Elorian launched its first visual reasoning model, Glimpse. It’s being promoted as “a new category of visual thinking model: a system that reasons about the physical world from what it sees.”
So how does Glimpse compare to both frontier models and world models in vision tests? A new benchmark called DecoyCount, co-released by Elorian,provides some answers.
We quizzed CEO Andrew Dai on the benchmark, and also talked about Elorian’s positioning in a crowded models landscape.
DecoyCount results
Elorian emerged from stealth in April with $55 million in seed funding, co-led by Striker Venture Partners, Menlo Ventures and Altimeter Capital.Now, in partnership with AI data company Mercor, Elorian is announcing the DecoyCount benchmark, which tests whether language models genuinely count objects or rely on visual shortcuts and substitution heuristics.
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