51–60 of 194 posts
We have vision models for our pharmacy and I could never imagine taking the latency hit to use a Sol in our robotics, it would be likely 25-50x slower.
I didn't expect Gemini 3.5 Flash to top basically every metric in this article.
Demis had a pretty big interest in vision, more so than text, so I hope they don't lose that with all the recent shuffling.
The summary "There are still clear limits. Gemini 3.5 Flash remains a better practical choice [than GPT 5.6 Sol] for high-volume detection and counting in our benchmark, especially at its price." seems rather understated ! GPT 5.6 Sol was outperformed on all benchmarks by Gemini 3.5 Flash, apart from a single exception (OCR) where Fable was the winner. Gemini 3.5 Flash not only outperformed GPT 5.6 Sol, but did so at…
I understand why you would like to use an LLM for vision. I do it myself often enough. I don't understand however, why the pill detection and counting is included in this benchmark. That is a task which you would perform with OpenCV right? In my personal mini benchmark minicpm-v-4.6 scores amazingly well. Its a 0.8B model which runs fine on many consumer hardware.
I keep waiting for these AI companies to assemble the parts into a great autonomous driving module.
One of my friends (and BIL) own an architecture firm. They use AI to generate and quickly update renderings but they run into the equivalent of the 6 fingered hand problem. I sent him this article I wonder if the updated models can catch and fix mistakes made by previous models.
This article is about vision, not image output.
It is funny to me seeing Sol used for what a "traditional" AI model can do already (counting pills). We have vision models for our pharmacy and I could never imagine taking the latency hit to use a Sol in our robotics, it would be likely 25-50x slower.