Elorian releases Glimpse, a visual reasoning model with a 'visual scratch space'
Elorian, a Palo Alto startup that raised a $55 million seed round at a $300 million valuation before launching a product, released its first model, Glimpse, on Friday as a research preview with API access and a chat interface, TechCrunch reported. Glimpse is designed to revisit the source image mid-reasoning, cropping into a region, pulling specific video frames or running code to measure what it sees — a mechanism Elorian calls a "visual scratch space." Elorian says a small team trained the model on a few hundred GPUs and that it is substantially smaller than frontier systems; its own evaluations claim it outperforms GPT-6 Astra on scientific chart reasoning and short-form video captioning. Pricing is $0.60 per million input tokens and $3 per million output tokens. No independent benchmark results were reported.
Elorian says it built the whole model around this repeated visual inspection and trained it cheaply on a few hundred GPUs, initially targeting video-heavy robotics firms, scientific labs and media companies.
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. 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.
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.”
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