141–150 of 194 posts
Earlier quoted context omitted.
Did you evaluate any that could be self-hosted (or at least ow models), if so which one is the best you seen?
Take a look here: https://playground.roboflow.com/evals . We have few ~30B.
It seems Qwen is kicking ass, and Fable made me laugh when I saw it all alone on the far right of the graph :))
Does any popular NVR make a good use of LLMs (especially local models) getting decent at vision?
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.
Agreed, this like asking a chainsaw to carve a wooden spoon. Impressive it can, but definitely not the right tech to scale. LLM needs to setup an image classifier to use as a tool call.
I remember around 2013-15 people were scoffing at uses of deep learning CNNs for various things, because why don't you just use an SVM on HOG features? Or face detection is solved, just use Viola-Jones.
What if you give the benefit of doubt and assume the author knows about alternatives and uses VLMs for their strengths? They use it to auto-annotate training data for regular deep learning models.
Earlier quoted context omitted.
Speaking from experience here, flash lite models have amazing price, speed, and perform far above their size, but are susceptible to very bad instruction following and recall when either complexity or context size inch up. They’ll just forget to apply your instructions to portions of the input, and repeat parts of the input that should be returned verbatim as direct quotes but with subtle changes (breaking urls, for…
Yes you have to continuously tune the prompts ever so subtly. 3.5 is a lot better than than 3.1 tho. Important to remember that json schema instructions take precedence over the normal prompt, so move as much into property descriptions as possible.
3.0 flash (not lite) handled it like a champ though, fwiw.
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…
Hi, I’m the author of this blog post. I wrote it about 4 weeks ago, and the VLM world is moving so fast that it’s already kinda outdated. I think Gemini 3.7 Flash might be a better choice now, especially when you factor in the price. Here’s a comparison of the best low-cost models I put together last week. What’s crazy is that Gemini 3.7 Flash is now 50% off on OpenRouter, and this chart doesn’t even account for that…
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…
Hi! I’m the author of this blog. GPT-5.6 is much better at vision than previous GPT versions, but it’s still much weaker than Gemini 3.5 Flash or Gemini 3.7 Flash, which was released last week. One interesting approach is to use Gemini through a tool call.
I actually favor Qwen3.8 and run it locally + use the Token-Plan on AlibabaCloud, when I need faster results. Kind of favor it over GPT5.6 Sol. Also it seems to be more capable, need to test more, but I think it's at least getting on par and it's fully open-source and open-weights. Here's some benchmarks: https://benchlm.ai/compare/gpt-5-6-sol-vs-qwen3-8-max https://qwen.ai/blog?id=qwen3.8#full-benchmark-table (incre…
Earlier quoted context omitted.
Curious why you didn't try Gemini 3 pro? That is the model I've been using for OCR entry of handwritten datasheets (JPGS of datasheets, structured JSON output). At my scale, the cost of 3 pro is basically not an issue, but if there are improvements in quality, I'd definitely be willing to explore other models
3 Pro is quickly approaching one year old. There's almost no reason to benchmark it, especially since a new version of Gemini Pro was supposed to be released mid 2026 and hasn't seen the light of day.
Earlier quoted context omitted.
3 Pro is quickly approaching one year old. There's almost no reason to benchmark it, especially since a new version of Gemini Pro was supposed to be released mid 2026 and hasn't seen the light of day.
I think it definitely makes sense since it's still the best Google has to offer in the "pro" tier.
It's not a technical problem, it's a commercial one. If Google can't ship a model to replace the one they deprecated, that tells you everything you need to know about choosing a Gemini model for whatever you're trying to do.