GPT 5.6 Sol is the best "vision" model OpenAI ever released

weli

Anecdotal, opinion: Gpt is really good in vision stuff, or at least their MoE seems to be really cohesive. From my experience Claude models can be really good at language but the moment they need to look at a picture and decide why the design is not good what parts need improvement it degrades a lot. My easiest benchmark is giving them a screenshot of a feature in my app and tell it "identify non-normative UI blocks and improve readability and consistency". Sol does a great job at re-structuring the page into composable units that build upon each other and the general looks and feels of the app. Claude tends to over-focus one one part while completely forgetting about the rest or the cohesion as a whole.

sscaryterry

My anecdotal evidence says its still as blind as any other model, it has no taste, no attention to any sort of detail.

Razengan

For the last 2 weeks I've been trying to get Codex to "outpaint" a wonderful image it generated as placeholder art for a level background. After I increased the game's resolution, I asked it to increase the image's size while keeping the same scale and existing content, and gosh, it constantly keeps getting something wrong no matter what I tell it, even on Sol Max with the $100 Pro subscription. An organically-grown meat-based pixel-artist could have recreated the image and more within 2-3 days, in exchange for food and shelter.

kzrdude

In the third vision bench result, Sol is 100% correct but the expected has 1 error. Seems like an oversight. In the next bench, Sol looks like it’s correct again but the bboxes are rotated 90 degrees for some reason.

evrimoztamur

Penny sample shown looks like failed EXIF orientation registered by the model/harness. The coins are correctly marked, it's rotated 90 degrees.

bob1029

I've decided it's "good enough" after I saw it properly quote a string of text that was very roughly highlighted within a nested visual context. It also identified the context correctly (modal inside webapp inside screenshot of user desktop).

logicallee

I agree. It did very well on an extremely challenging task. I asked it to recognize and draw the very faint reflection of what I was wearing, visible in only a tiny black part of a very brightly lit poster behind glass. In addition, the poster itself also happened to contain similar clothing. You can see the reference images and its output in my writeup here: https://medium.com/@rviragh/gpt-5-6-sol-very-good-image-reco... While a human can focus on the reflection easily, this is an enormous challenge for a vision model. It's very impressive.

iamniels

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.

5555watch

All of your use cases are very advanced. I recently used it at grocery stores in a foreign country. Photographed the whole aisle and told it to find Y (detergent, softener, glue, sour cream, whatever), at the same time recommend the best Y for whatever reason. Worked marvelously, including the cases where the object wasn't present and it told me there was nothing useful. I asked then, can you crop the exact image of how does the item look like and where is it in the aisle - did that perfectly as well. I will add that all frontier models were fine with such tasks from the early 2024's.

adroitboss

I didn't expect Gemini 3.5 Flash to top basically every metric in this article.

iamleppert

Where are the Qwen benchmarks in this? I would be more interesting to see how Qwen performs.

catigula

Still not quite as good as gemini.

trumbitta2

"Best iPhone ever" vibes.

comboy

Does any popular NVR make a good use of LLMs (especially local models) getting decent at vision?

chasd00

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.

mv4

Ironically, the pill counting example selected to showcase "the best vision model" can be easily solved with OpenCV template matching, a technology created 25 years ago.

prathje

I would love more vision benchmarks! Once I asked the model to inspect a completely black picture and it hallucinated a nice wooden kitchen wall. Took me some time to figure out where the kitchen came from... I usually go to https://arena.ai/leaderboard/vision/pareto for a nice overview of current models.

HarHarVeryFunny

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 1/3 of the cost.

WarmWash

It's vision capabilities poisoned my cucumber bed, misidentifying the malaise and having me spray them down with water, which only spread the fungus that gemini later informed me was actual cause, which I went and checked myself. I hope that whatever was lost at GDM in the last few months, didn't include their extra focus on vision capabilities.

kherud

So far I haven't seen a single model succeeding at transcribing sheet music, but I just tested it again with 5.6 Sol and it nailed the small test case. Fluently reading music requires multiple years of training for most people, but I feel like accurately following the horizontal lines trips up vision models in particular.