Elorian’s first AI model wants to reason with images, not just about them
Palo Alto startup Elorian has released its first AI model, Glimpse, in a research preview. The model uses a visual scratch space to crop images, pull video frames, and run code mid-reasoning. Elorian reports that Glimpse outperforms GPT-6 Astra on scientific chart reasoning and short-form video captioning.
The Core Development
Elorian , the Palo Alto AI startup that raised a $55 million seed round at a $300 million valuation before it launched a product, released its first model on Friday, TechCrunch has exclusively learned.
The model, Glimpse , is launching as a research preview with API access and a chat interface is waitlist-only, so for now it’s a results table and a thesis. The thesis? Today’s AI models, for all their skill with language and code, still can’t really see.
Most multimodal models look at an image once, convert it to tokens, and then reason about it in text — like describing a photo from memory. Glimpse keeps going back to the source. Mid-reasoning, it can crop into a region, pull specific frames from a video, or run code to measure or compare what it’s looking at, a mechanism Elorian calls a “visual scratch space.” Think of someone pinching to zoom on a phone photo to double-check what they thought they saw.
Strategic Perspective & Ecosystem Impact
For former Google DeepMind researcher and Elorian CEO Andrew Dai, one of the clearest signs that AI still couldn’t really see came on game night.
Last year he photographed a board game he’d just finished with friends and asked a frontier model who was winning. It couldn’t say. Board games “each have their own visual language, each have their own iconography, symbols,” he told TechCrunch. “To an adult, it’s quite easy to understand what’s going on with minimal text, but these models aren’t able to intuit what the symbols mean.”
“Humans can look at Ikea diagrams, with no text, and assemble Ikea furniture, right? Why can’t AI models?” he said.
Why it matters
The release highlights a growing industry focus on native visual reasoning over traditional text-token conversion, aiming to make multimodal AI more capable and cost-effective.