Can you instruction-train a duplex model like this? How would that look? I also wonder, if a model trained only on speech can develop world knowledge like this, could you train an image model only on images of text to essentially act as an LLM?
Using it as part of an agentic CAD workflow, where the model needs to be able to selectively segment from parts of a user's image as it works on a project. Using OpenSCAD so being a general purpose model is very useful to me.
If openscad is the thing Google models has consistently outpermormed the comparable competition in my experiences. Few other models has better physical spatial reasoning skills.
I've found OpenAI models the best (although I haven't tried Argon) for OpenSCAD.
I have found a couple of problems Opus could fix that Sol couldn't (smoothing a three way join between curved edges and a bezier curve face is one I can remember). But it's much slower than Sol and generally doesn't produce as nice results.
There's no reason that an agentic workflow has to use the primary agent model for every intuitive computing task; in fact, that would've sounded absurd before the singularity. Maybe an old flash model is the best in class for this, but A) I doubt it and B) it's still gonna be wildly more expensive.
Assuming I'm not just misunderstanding something critical (happens often!) and you haven't explored this already (very possible!), pointing your agent at this link and asking for some prototypes might be illuminating: https://huggingface.co/models?pipeline_tag=image-segmentatio...
I've had pretty good luck having Gemini or Claude use SAM as a tool. Gemini is really good at identifying things in images, but it doesn't do a great job doing segmentation per se, but SAM on its own can't really decide what you need to segment.
Even though they're technically trained for music, it might be worth testing Suno/Lyria to see if they're able to do this. You might have to isolate it afterwards, but seems like it could be viable
I've been working on an agentic graphic design tool, so vision is quite useful for having the model check its own work. I'm already seeing improvements with this model vs the text-only one.
This has been a pretty big topic of discussion recently with Prime Intellect's new harness making leaps on ARC 3's public dataset. This is probably a bit overstated considering that a big focus of the benchmark is to test the model without custom harnesses, but you can imagine how for some tasks, especially long running ones, this kind of on-the-fly context management + tool generation could be quite useful. It gets better over time, it can build shorthand and tools for token efficiency, like a loose approximation of continual learning.
I enabled all data sharing settings but still don’t have a message about free use on that screen - the help page says free tokens are available to “some” users - is that 1% of users, 40% of users, etc?
Does your screen have the message that you’re getting free tokens?
Yeah, I have it enabled for just one of my projects, and it says "You're enrolled for complimentary daily tokens." Haven't been billed for any usage with this. Their tooltip doesn't mention the newer models, but it works for those too. I used them in a recent project that I knew wouldn't hit the rate limits. Not sure how eligibility is decided though.
I had no idea about this before. I just enabled it.
You're eligible for free daily usage on traffic shared with OpenAI.
Up to 250 thousand tokens per day across gpt-5.4, gpt-5.2, gpt-5.1, gpt-5.1-codex, gpt-5, gpt-5-codex, gpt-5-chat-latest, gpt-4.1, gpt-4o, o1, and o3
Up to 2.5 million tokens per day across gpt-5.4-mini, gpt-5.4-nano, gpt-5.1-codex-mini, gpt-5-mini, gpt-5-nano, gpt-4.1-mini, gpt-4.1-nano, gpt-4o-mini, o1-mini, o3-mini, o4-mini, and codex-mini-latest.
Usage beyond these limits, as well as usage for other models, will be billed at standard rates. Some limitations apply. Learn more.
I tried following this page, and it's certainly a lot more complex than what Meta is offering. Different price tiers, opt-in configurations, usage based availability.. I'll take the 10x discount for flipping a param switch over this all day long.
Seems pretty clear to me: enable it for the projects you want, and there’s a 1M / 10M token limit per day, depending on the model you use. Assuming an average context size of 100k tokens, that is 10 to 100 requests, which is not a lot. Reason enough to prefer actually paying for Meta as well.
1 million free tokens per day might sound like a lot. But that equates to something like 20 minutes of actual coding usage, because cached inputs are counted towards that limit. It's still great for running big singular requests, like solving some math problem with GPT 5.6 Sol Pro max reasoning effort.
Also, in a desktop browser at the page's [1] lower left it says "Personal organization", so if the 'organization' term is the concern, OpenAI still seems to use the term 'organization' even for personal accounts.
In my experience using DeepSeek v4 Flash free tier (context size limited to ~200k), the model isn't nearly as good as the paid one for agentic coding tasks. Unsure, if that's the case with these other providers too, though I wouldn't be surprised if it indeed is.
If you have the link for the page handy, I'd be curious to find the original revision on the wayback machine. That's too funny, especially if they've silently walked it back since.
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