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I am glad they released it because I think we need a competitive culture of open weights that isn't just geopolitics.

But I have to say, I quite like the way Muse Glimmer thinks and talks. It's a cocky bastard in tone, but it's quite good, and its thinking traces are relatively terse.

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Do AI companies make release plans based on upcoming other models like this? I would think all the processes that go into the repository and weight infrastructure pre-training, checkpointing, knowledge distillation, model compression, post training pipeline, ecosystem integrations, inference API, benchmarking, human eval/safety/alignment, docs, etc... all that dictates the release schedule.
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Any company working in a competitive industry is generally aware of what their competitors are doing. PR is an important aspect to market success, so it factors into release schedule. It may not be the dominant factor given engineering constraints, but yea, it’s certainly a factor, and a large one at that.
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Yes, not every model release is reactionary to other labs. Either they had hints for the release of other models or they cut efforts in late stage testing of the models to hit these earlier release dates. There’s always some flexibility. And there’s certainly the incentive to cannibalize the news cycles for competitor models.
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I could imagine pulling out all the stops to get a release over the finish line a week early if you're worried about being surpassed by another release
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Yeah but you can probably have everything ready and then accelerate as necessary. Meta itself did this when releasing Llama 4, it was a really botched release right when they were feeling the heat from DeepSeek and others.
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> ...all that dictates the release schedule.

AI companies release models when they are ready - not on a set schedule. The steps are required to produce a release candidate, so a company can choose to bless the RC with the best metrics at any time - or alternatively continue (post/)training newer RCs if they feel they can do better, and they have time. When a model is ready for release is subjective, and can take into consideration what the competition is doing.

An obvious counter-example to schedules driving releases is the still unreleased Gemini 3.6 Pro.

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There has been a long history of AI model releases made shortly before or after a major planned release by another company. Almost always to upstage or steal thunder.

Just recently, Minimax H3 released as open weights on the eve of Seedance 2.5 global availability. It's not as good, but it's good enough and it's completely open.

Flux 3, which is nowhere near as good as either, suddenly announced their release once news of these other two became public. They knew if they waited they'd be ignored. It didn't really help them much, unfortunately.

The LLM releases are even more rivalrous.

And don't forget all of the competing launches planned before Google IO or major release events.

Companies like to eat into the news and press cycle of their rivals.

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I've seen it here on HN (it's particularly noticeable via the /active page) multiple times. If Google, OpenAI or Anthropic release something significant, odds are good you'll see a headline from one of the others.
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BFL is in a rough spot here too. It’s pretty much looking like a repeat of the exact same situation they had when they released Flux2 at the same time Z Image Turbo came out and completely overshadowed their launch.

Minimax H3 can run exceptionally fast (10 minutes for a 15 second 0.5mp video and that's stock cuda 13), works on 16 GB VRAM GPUs, etc. If Flux3 is anything like Flux2, it’s going to require an absolute monster truck of a machine and still run significantly slower. Even if it’s a better model, that won’t matter as much if nobody releases any LoRAs or fine-tunes for it.

Not to mention BFL licensing often feels deceptively confusing and restrictive.

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>long history

Seems a bit premature of a statement lol

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If you start counting since WaveNet or BERT, it's been ages. Especially when it feels like decades of advancements happen every single year, and rival labs are always trying to one up each other.
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I don’t start counting since we WaveNet or BERT so there you go!

Even if I did, we’re talking barely a decade

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the last few items there (benchmarking, human evaluation, docs) can be rushed or skipped by leadership if they want to beat comp. they probably spend a few weeks on those things normally
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One window that can be shortened is working with software ecosystem and upstream partners; think day 0 on together, fireworks, Unsloth, etc. That obviously happens from partners getting embargoed weights early.
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It will also be very interesting to see some direct head to head benchmarks between qwen 3.6 27B (let's say all at Q8 XK quantization, using the GGUF that unsloth publishes as a baseline) vs 3.8 27B. Particularly in tool use, terminal use.

The whole class of what can reasonably fit in a single GPU is an interesting category of LLM, and based on the results I've seen from 3.6 35B A3B and 27B versus what existed a year prior, it seems there's a lot of room for advancement.

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I would hope that Qwen 3.8 is better. It's been 4 months, and we've seen almost no progress in this space.

As people have called out, Glimmer appears to be a trade-off rather than a clear winner.

And from what I've been reading, no one is expecting Qwen 3.8's model in this space to be a clear winner, but just slightly and marginally better.

That's a little concerning as DeepSeek v4 Flash proved at it larger sizes there's a ton of room left to compress knowledge.

If we don't see something that's substantially better in the ~30B param space soon - it would appear we might've saturated that size with knowledge.

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> If we don't see something that's substantially better in the ~30B param space soon - it would appear we might've saturated that size with knowledge.

I wouldn't be quite so pessimistic. We may have saturated the current approach, but I think there's a lot still left in terms of compression, attention, active parameters, caching etc. etc.

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I don’t think four months without a major breakthrough is cause to abandon all hope just yet. ;) The wild pace of LLM development is highly atypical, and we’re still in the ‘initial rush’ phase of development.

For contrast, the Newcomen steam engine (widely considered the first commercially useful engine) was used for over 60 years before the next major improvements. Now, 300 years later, we’re still finding ways to significantly improve heat engines.

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> For contrast, the Newcomen steam engine (widely considered the first commercially useful engine) was used for over 60 years before the next major improvements. Now, 300 years later, we’re still finding ways to significantly improve heat engines.

Off-topic, but I stumbled upon the first Newcomen engine imported into Australia in a museum in Sydney and I was unexpectedly charmed (not an Engine Guy). It's large, but nothing like the awe of "mega-engineering", it's crude, but it clearly has such amazing utility (when compared to a reality without it) and it changed the world

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I honestly expect that major advances in the open 30B dense space will take about a year, but expect incremental advances every couple of months from different developers in the meantime.

Qwen 3.6 27B was already a massive gift to smaller homelabs around the world; anything more is just a delightful surprise.

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For so many non-coding workflows, tool calling is more important.
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Qwen3.6 27B has really punched above its weight for a long time. It’s shockingly good for its size. Very excited to see what 3.8 can do.
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Qwen3.6 27B is the go-to medium sized model for coding, so beating it is not a small achievement
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There is a finetune Qwen3.6-27B-Fable-Fus-711-UnHeretic-NM-DAU-NEO-MAX-NEO which seems to be as good at coding as vanilla Qwen, but way, way better at creative writing than Qwen and even better than Gemma 4 26 and 31b.
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I saw that one in the "Popular models" sort at Hugging Face and tried it on some tasks I do frequently to compare models, and it feels damaged by the fine-tune, to me. It wrote security bugs into the code (probably just sloppy thinking, not intentional), it exhibited looping behavior in some configurations in llama.cpp, configurations I regularly use with the regular 27B, and it failed to write unit tests without being prompted (though the regular Qwen 27B tends to do so if it sees there are unit tests for everything in the repo). They have good benchmarks, but I'm not going to trust it. Also, that name is absolutely crazy.
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> but way, way better at creative writing than Qwen and even better than Gemma 4 26 and 31b.

I suspect this is the only use-case I would consider...and I don't really have a use-case for "creative writing" that I would delegate to an LLM. I suppose for dialogue generation in games?

But yes, hard agree. Why on Earth would you ever want to write code with a model that is supposedly "jailbroken"? So it can put great backdoors into everything it touches? Pass.

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I've noticed most fine-tunes, whether "heretic" models or something else, tend to be over-fitting, or something, at least some of the time, and get kind of chaotic. I want to believe normal folks with normal resources can be involved in this stuff, as I'm working on fine-tuned specialist models as we speak, but it seems like it takes notable investment and time. My first experiment was teaching a little Gemma 4 more to write more like me with a LoRA (like you, I don't want to use a model to write for me, but I did want training data that I could ethically use, and I've written several million words on the internet over the years), and it wasn't what I would call a success. It either wrote like an asshole (which I only do, like, 15% of the time) or it just borrowed a few of my quirks, like too many ellipses, if I applied it less heavily.
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> I suppose for dialogue generation in games?

I use it to write short sci-fi stories. Life is not only about being an SDE.

> Why on Earth would you ever want to write code with a model that is supposedly "jailbroken"?

I need a model which decent enough at coding to write boilerplate for me, yet I do not need to unload in case I want to write an article or enguage in my writing hobby.Exactly why Gemma 4 are so popular.

> So it can put great backdoors into everything it touches?

What a strange take...

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Yeah, this became my main coding model.

It seems to know tooling and coding.

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Yes but it's also about 13 years old in LLM-years and 3.8 drops in a few days.
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