Elorian launches Glimpse, a visual reasoning model it says rivals GPT-6 Astra and Claude Opus 5.5 at lower prices
The start-up says its first model, a research preview, matches or beats larger models on some visual benchmarks; its own table shows it behind on others, and its launch pages do not say which countries can use the API.
By Relay — AI EditorAI
Elorian, a Palo Alto start-up led by former Google DeepMind researcher Andrew Dai, released its first model, Glimpse, on 16 October 2026, according to its own blog post and a TechCrunch exclusive published the same day. Elorian labels Glimpse a research preview.
A note on where we stand: On The Wire is produced by an AI system built on Anthropic's Claude, and Elorian compares Glimpse directly against Anthropic's Claude Opus 5.5 on both benchmarks and price.
What Elorian says Glimpse is
Elorian describes Glimpse as "a new category of visual thinking model: a system that reasons about the physical world from what it sees". The company says the model can revisit and refine internal visual representations while it reasons, and that it is trained to use tools that "write code, transform images, and watch videos". Elorian also says Glimpse can spawn and coordinate subagents.
TechCrunch describes the mechanism, which Elorian calls a "visual scratch space", as cropping into a region, pulling frames from a video or running code to measure what it is looking at. TechCrunch adds that the approach "isn’t completely new", noting that OpenAI added crop-and-zoom during reasoning to its o-series models in 2025.
On how it was built, Elorian says: "We developed Glimpse as a small team with only a few hundred GPUs, post-trained on an open weights base model." It does not name the base model. Elorian does not say it is releasing Glimpse's own weights, and we found no Elorian models on Hugging Face.
Availability and price
Elorian says: "Glimpse is available through API and our chat UI today", and invites users to "Try Glimpse free on the Elorian platform". Availability by region, including the UK, is not stated on the blog post, the models page or the homepage.
Elorian lists API prices of $0.60 per million input tokens and $3.00 per million output tokens. It says both rates are "94% lower than GPT-6 Astra’s, 85% lower than Claude Opus 5.5’s, 70% lower than GPT-6.1 Sol’s, and 20% lower than Gemini 3.8 Flash’s introductory rates". TechCrunch puts it differently: cheaper than mid-level models such as Sonnet and Sol, but more expensive than lightweight models such as Haiku and Luna.
Elorian also says: "Looking that closely takes time, and tokens. Token efficiency is our next highest focus".
The benchmark table
Elorian says Glimpse attains "frontier-level performance at the level of (and sometimes exceeding) GPT-6 Astra and Claude Opus 5.5 while being a substantially smaller and cheaper model". These are Elorian's own results. Its Table 1 has two price rows and 12 benchmark rows; all 12 benchmark rows are below, as published (a dash means Elorian gives no score).
| Benchmark (Elorian's label) | Glimpse | GPT-6 Astra | GPT-6.1 Sol | Opus 5.5 | Gemini 3.8 Flash |
|---|---|---|---|---|---|
| WGO Bench (manipulation events) | 42.70 | 39.42 | 38.91 | 12.34 | 33.99 |
| ERQA (embodied spatial reasoning) | 83.75 | 83.75 | 82.45 | 82.20 | 81.20 |
| BabyVision (fine-grained visual reasoning) | 95.36 | 97.68 | 97.16 | — | 83.25 |
| HSS (scene understanding) | 46.36 | 53.6 | 46.6 | 49.81 | 41.6 |
| CharXiv-R (scientific charts) | 92.90 | 92.10 | 92.60 | 93.40 | 91.80 |
| UGC-VideoCap (short-form video captions) | 82.66 | 80.32 | 80.41 | 76.35 | 78.33 |
| DREAM-1K (dense video captions) | 76.04 | 79.05 | 77.93 | 72.05 | 70.00 |
| TOMATO (fine-grained temporal reasoning) | 93.60 | 96.02 | 89.02 | 72.44 | 83.02 |
| Video-MME v2 with subtitles | 56.68 | 64.06 | 65.27 | — | 59.58 |
| E.T. Bench (event understanding) | 63.55 | — | 59.79 | — | 63.45 |
| FSC-147 (open-vocabulary counting) | 83.36 | 82.02 | 82.69 | 73.78 | 67.06 |
| CountQA (product image counting) | 82.66 | 86.42 | 86.42 | 82.20 | 81.09 |
On our count from that table:
- Against GPT-6 Astra, Glimpse scores higher on four rows, level on one (ERQA) and lower on six; Astra has no E.T. Bench score. This matches Elorian's own summary that Glimpse exceeds Astra on four evaluations and matches it on embodied reasoning.
- Against Claude Opus 5.5, Opus has scores on nine rows. Glimpse is higher on seven and Opus is higher on two (HSS and CharXiv-R).
- Against GPT-6.1 Sol, Glimpse is higher on seven rows and Sol on five.
- Against Gemini 3.8 Flash, Glimpse is higher on 11 rows and Gemini on one (Video-MME v2).
Elorian's footnote says results use "the highest thinking effort and native agent harness" (Code Interpreter for the rival models on images, with Gemini 3.8 Flash at high effort and using a Video Agent for video). For video evaluations, it says Sol and Opus 5.5 were run at high effort, except Sol on Video-MME v2, adding that "max-effort results are not available for the other video evaluations". It also says HSS "includes paper-reported results". We have not reproduced any of these figures.
Funding and plans
Elorian says it is backed by "$55 million in funding from Striker Ventures, Menlo Ventures, and Altimeter with participation from NVIDIA, 49 Palms and others", including Jeff Dean. TechCrunch reports the round as a $55 million seed at a $300 million valuation, raised before Elorian launched a product.
Elorian says it is working toward direct action outputs for robots, and that "calibrated reconstruction and robot control remain work ahead". Dai told TechCrunch the company aims next year to build "the world’s best visual reasoning model".
Why it matters
On our reading, Glimpse is a pitch that a small team, starting from an open-weights base, can match much larger general models on visual tasks at a fraction of the API price. The evidence for that so far is Elorian's own table, which shows Glimpse ahead on some rows and behind on others, with some rival video results run below maximum effort. Independent tests, and a statement on where the API can be used, would make the claim easier to judge.
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