Right, they state that they'll release "smaller" variants openly at some point, with few details as to what that means. Will there be a ~300B variant as with Qwen 3.5? The blog post doesn't say.
This is how I view that the public can fund and eventually get free stuff, just like properly organized private highways end up with the state/society owning a new highway after the private entity that built it got the profits they required to make the project possible.
Groups of smaller countries could build coalitions to fund models (and research), managed by a non-partisan entity and combining taxes and licensing.
Models would likely to have to be open weight to reduce the risks associated with power concentration: businesses/researchers/individuals can self-host for privacy and scrutiny, tax-payers and local businesses access funded infra for free, other use cases require a paid license.
Now they show their true colors. They want to train models on our engineering to replace us, while simultaneously giving nothing back? No thanks. I'd rather fund the shitty US hyperscalers. At least that leads to jobs here.
If there's a company willing develop and foster large scale weights in the open, I'll adopt their tooling 100%. It doesn't matter if they're a year behind. Just do it open and build an entire ecosystem on top of it.
The re-AOLization of the internet into thin clients is bullshit, and all it takes is one player to buck the rules to topple the whole house of cards.
Qwen is not the only Chinese lab, and the others have shown no change in their commitment to open source. Allegedly Qwen hasn't either if their recent statements are to be believed. They're just hoping to capture market share with *-claw customers before releasing an open weights version. We'll have to wait and see how before they decide to release that.
I wouldn't call this totally accurate, especially as of late. What's closer to the truth however is that there's lots of second-rate players in China doing open models, that will be getting a lot more attention from local AI proponents if the big names seriously slow down their AI releases. The local AI scene as a whole is quite healthy.
That's a very reasonable stance. It doesn't change the fact that we do have plenty of local models (up to and including Qwen 3.5) that are still quite useful.
I'm USian myself, but I don't think the site should be very US-centric.
I'd prefer them to be open weight, but I'd love to sub a decent competitive coding plan from a European or Chinese provider. Right now they're not quite there. If closing it and charging for it brings them closer to competitive, that's ok.
If the US tech and AI industry long term wants customers and a broad market outside of their own domestic base, they need to reconsider who they are bending the knee to, and how they are defining their policies in relation to the Trump administration.
Bring on the Chinese competition.
So expect every now and then a open model burp from the trailing frontiers. Afterall, its all sunk cost so once you have it and no customers, theres zero reason not to spike your competition and try again or exit.
Sure they are not cheap to train. But if open weight models continue to be trained and continue to become available on cheaper hardware, how do dedicated AI companies protect their margins?
There's nothing really strange about not competing directly with the best, but rather showing whom you are as good as.
They posted charts with logos for Claude and others. You had to read the fine details to realize they weren’t comparing to the latest offerings from those companies. They were counting on you not noticing.
There’s zero reason to compare to old models unless you’re trying to mislead.
The naivety around this has been staggering quite frankly. All of a sudden, people thinking that meta etc are releasing free models because they believe in open access and distribution of knowledge. No, they just suck comparatively. There is nothing to sell. Using it to recruit and generate attention is the best play for them.
Apparently that wasn't actually the play here.
There isn't, pretty much everyone wants the best of the best.
I would say that for a significant part of the current market open-source models are good enough to fill a part of it.
At least from my experience and friends of mine, we use OpenRouter for cases where we want to use smaller LLMs like Qwen, but when I've used ChatGPT and Claude, I use those APIs directly.
products entirely disappearing or significantly changing will be more and more common in the llm arena as things move forward towards companies shutting down, bubbles deflating, brand priorities drastically reshifting, etc...
i think, we're at or at least close to a time to really put some thought into which pieces of your flow could be done entirely with an open/local model and be honest with ourselves on which pieces of our flow truly needs sota or closed models that may entirely disappear or change. in the long run, putting a little bit of thought into this now will save a lot of headache later.
But with LLMs, how do you know switching from one to another won’t change some behavior your system was implicitly relying on?
I have no affiliation with DeepInfra. I use them, because they host open-source models that are good.
For direct user interaction or coding problems, perhaps. But as API calls get cheaper, it becomes more realistic to use them for completely automated workflows against data-sets, or as sub-agents called from expensive SOTA models.
For example, in Claude, using Opus as an orchestrator to call Sonnet sub-agents, is a popular usage "hack." That only gets more powerful, as the Sonnet equivalent model gets cheaper. Now you can spawn entire teams of small specialized sub-agents with small context windows but limited scope.
I did create my own MCP with custom agents that combine several tools into a single one. For example, all WebSearch, WebFetch, Context7 exposed as a single "web research" tool, backed by the cheapest model that passes evaluation. The same for a codebase research
Use it with both Claude and Opencode saves a lot of time and tokens.
Seems like a huge waste of money and electricity for processes that can be implemented as a traditional deterministic program. One would hope that tools would identify recurrent jobs that can be turned into simple scripts.
For example: "Here our dataset that contains customer feedback comment fields; look through them, draw out themes, associations, and look for trends." Solving that with a deterministic program isn't a trivial problem, and it is likely cheaper solved via LLM.
There are many simpler tasks that would work fine with a simpler, local model.
There are a lot of data science problems that benefit from running the dataset through an LLM, which becomes bottlenecked on per-token costs. For these you take a sample subset and run it against multiple providers and then do a cost versus accuracy tradeoff.
The market for API tokens is not just people using OpenCode and similar tools.
Coding is a rung on the ladder of model capability. Frontier models will grow to take on more capabilities, while smaller more focused models start becoming the economical choice for coding
The price is a concern too of course. But privacy is a bigger one for me. I absolutely don't trust any of their promises not to use data for training purposes.