I wish they had a revenue goal to release openly, that way spending money in them would contribute to better open models in the long run.

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.

As a publicity stunt, releasing a 300B open model is pretty smart. You can talk about its strong performance and it being “open” and “available,” but it’s so large that most people can’t use it themselves and might try out the cloud-based offering.

I'm not interested in adopting an inferior closed source weight from a geopolitical rival. The open source weights argument was the one thing China had going and that I was seriously cheering them on for. They could have been our saviors and disrupted the US tech giants - and if it was open, I'd have welcomed it.

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.

Interesting! What is your reasoning behind that? I just learned there where closed models from the team before this so that shouldn’t have been a surprise for the employees? Or do you think the internal communication was: we will release better open models the the existing closed ones to push everything forward and now when they are getting competitive they are becoming proprietary?

Exactly. 3.6 plus in the exact same coding agent harness is notably worse in all of my testing compared to 3.5 plus.

The former gets stuck in ridiculous thought loops on the exact same tasks I’m testing. Fascinating really, I expected more for some reason.

I don’t know why anyone would do the mental backflips to defend this.

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 OpenRouter usage stats indicate the opposite: https://openrouter.ai/rankings?view=month

No. Right now I'm upset that Google has removed (or at least is in the process of removing) the Gemini 2.0 flash model. We use it for some pretty basic functionality because it's cheap and fast and honestly good enough for what we use it for in that part of our app. We're being forced to "upgrade" to models that are at least 2.5 times as expensive, are slower and, while I'm sure they're better for complex tasks, don't do measurably better than 2.0 flash for what we need. Yay. We've stuck with the GCP/Gemini ecosystem up until now, but this is kind of forcing us to consider other LLM providers.

> There isn't, pretty much everyone wants the best of the best.

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.

Not all tasks require models like opus. If they do not, then it is more efficient to use cheaper and faster models. For most of my tasks now I use the big kimi/qwen/glm models because they are cheap and good enough, if not even the smaller locals ones.

I would say that for a significant part of the current market open-source models are good enough to fill a part of it.

Ever hit your daily limit on Claude Code and saw how expensive it is to pay per token?

maybe there isnt, but as understanding grows people will understand that having an orchestration agent delegate simple work to lesser agents is significant not only for cost savings, but also for preserving context window space.

That isn't true. In a Codex or Claude Code instance, sure... but those are not the main users of APIs. If you are using LLMs in a service for customers, costs matter.

For coding I want the best. Both I and $work do lots of things besides coding where smaller models like qwen3.5-27b work great, at much lower cost.

The market for API tokens is bigger than people like you and I (who also want the best) using then for code.

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.

Nope. I get very good results from GLM 5 and 5.1. I’m not working on anything so complex and groundbreaking that I need the best.

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

Everyone may want the best, but the amount of AI-addressable work outstrips the budget available for buying the best by quite a wide margin.

OpenCode allows for free inference tho.

Not really. It depends on the usecase. For private stuff I'm very happy to take what was SOTA a year or 2 ago if I can have it all running in my home and don't have to share any of my data with some sleazy big tech cloud.

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.

That's only because current models don't saturate people's needs. Once they are fast and smart enough people will pick cheaper ones.

I thought Qwen was releasing open-weight because China can't compete with America (because of people's privacy concerns), so the only thing they could do is salt the ground economically with open models, and make sure everybody loses.

Apparently that wasn't actually the play here.

I don't think there's so much naivety. People can be aware of the the plan and still be frustrated and disappointed when it happens.

OP didn't say about confusing Opus with Qwen but rather people being confused about Qwen3.6-Plus not being available as an "open weight" model available for self hosting.

They compared their M-series chips to older Intel Macs for a while, likely to target users who were still on Intel chips. If they released a lower cost iPhone and compared it to a previous gen Android I could see the reasoning for it. It's not deception if it's a valid comparison and people just fail to understand what's being compared.

Now, is it mildly deceptive because all of the companies using incredibly confusing naming conventions for their models? Maybe!

That's because Android phones release every year. If they released every couple months then I doubt it would appear that way.

Why are we so quick to call it deception? Their figure is quite clear. They aren't fiddling with the graph or hiding the labels, they are clearly stating which models it compares against. But I agree on the sentiment that the standard practice should be to bench against the latest SOTA models.

> we will also open-source smaller-scale variants

In other words, like GP said, this Qwen3.6-Plus model is not open-weight unlike the other Qwen models.

I find 4.6 pretty noticeable upgrade, but it might be the 1M context. I'm interested in how the 1M context works out with Qwen.

I think they already are licensing their biggest models to third party inference providers.

Also the Qwen cli, their vibe coding agent, allows a thousand free requests a day. In practice it's about ~300M tokens for me

It's very generous

I know I could certainly pay anthropic $1500 a day for my use and they'd be delighted... I'd rather pay $0 ... Just a personal perfect

I can see reasons, among others that 4.5 was the one established as they were preparing this version. "So long" is merely 2 months ago, and Qwen 3.5 was barely released less than 2 months ago. They were likely already working on finalizing 3.6 before 3.5 official launch, and as 4.6 came out.

In any case, aside Claude fanboyism, having other plays inch closer to similar performance is always useful. Even if they are "6 months behind" as the pace slows down, this guarantees that there's no huge moat and they'll eventually either get to where the SOTA is, or the difference wont be that big.

I'd rather put fewer eggs in 2-3 big player baskets.

3.5-plus was also only available via api. I don’t know what the long term business model for open weights is, I hope there is one, but it seems foolish to assume that companies will be willing to spend millions of dollars of compute on an asset worth zero in perpetuity.

They've always had closed-source variants:

- Qwen3.5-Plus

- Qwen3-Max

- Qwen2.5-Max

etc. Nothing really changed so far.

They always did that. Did they say anywhere they'd open all their models? They still have a business.

Sure, it might try to subtly steer you towards fascism, but other than that, it's great.

What are the reasons for switching? Personally I got into the habit of doing a bit of a round robin with Codex/Claude (CLI) and then DeepSeek and Qwen web chat. And Claude in web chat. I like to switch just to learn the differences, otherwise I'd never know what the other models can do. But I still feel attached to Opus, but this can be fammillarity. If I only had Qwen maybe it would be effectively identical at the end of the day. Hard to say.

so if China has the data good, us has the data bad, got it lol.

us actually has laws around this and they arent sharing very much with thr us gov today. china shares 100% as required by law. and neither care much about "how long do i cook eggs for", but they do care about code generation a lot.

So I guess if it’s your personal data that’s up to you, but if you have private client data and client of your client data and that’s the fundamental reason why you are doing local ai, I can’t imagine moving from qwen local to qwen alibaba after not choosing google/anthropic/openai

As with all arguments equivalent to "I have nothing to hide, so I have nothing to fear," it may be true now, but it may not be true later. The only certainty is that this will not be your call.