EDIT: An open weight version of Muse Spark 1.2 is going to be released as well:
https://x.com/alexandr_wang/status/2086756152034066792
https://xcancel.com/alexandr_wang/status/2086756152034066792
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.
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.
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.
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.
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.
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.
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.
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.
Qwen 3.6 27B was already a massive gift to smaller homelabs around the world; anything more is just a delightful surprise.
</div> is four Gemma4 tokens, but one Qwen3.6 token.
In that file you can see all tokens and their corresponding numeric codes.
Considering that Meta distills Qwen[1] (and should!), it'd be hilarious if Muse loses the head-to-head; the "distillation attack!!1!" people claimed distillation on release n-1 is enough to match the intelligence of the latest version.
1. They wrote a paper about it
Each turn is about 45-60 seconds to generate all of the various responses. The GM and director have reasoning on, and the NPCs/Location/Narrator do not.
It's a fairly good "engine" for that. I'm not sure how a denser Qwen would do here regarding speed.
I'm not sure what all is needed to make that work for people.
Back in 2023 I started my own C# LLM library for doing tool calls and structured output, and over the years it has morphed bigger and bigger, and that is the backbone of almost all of my LLM-based projects.
I've never released it, but its easy to understand, and simple to add your own tools:
[AIDescription("Get current weather for a location")]
static string GetWeather(
[AIDescription("The city name")] string city,
[AIDescription("The country name")] string country,
[AIDescription("Temperature unit", ["C", "F"])] string unit = "C")
{
// make some API call to a weather API and return a string to the LLM
return $"The weather in {city}, {country} is 22°{unit} and sunny";
}
var chat = client.StartConversation("You are a helpful assistant with access to weather data.");
var response = await chat.SendAsync<string>("What's the weather in London?", GetWeather);
My issue currently is KV Cache, because I can't keep enough parallel caches running (4 is where I'm at), so TTFT (is that the initialism?) can be long when I have a particularly large scene (basically more than 2 NPCs).
But my harness does let me offload to any OpenAI compatible endpoint, I just prefer local cuz free.
The Qwen models have a habit of going into thought loops where they go in circles for a while.
Try a system prompt requiring it to think in Mandarin, while still delivering the response in the user’s language.
PS: Switching the thinking process from Chinese to English can also significantly circumvent certain self-censorship mechanisms built into the model.
Once you get the agent and message consistent,itll keep moving.
Edit: arXiv:2505.09388 Qwen3 Technical Report
Huh, well... no? Gemma A4B and Qwen A3B are quite popular in fact. I'm sure 3.8 35B A3B will outperform 3.6 27B by all metrics
Qwen3.6 is a definitive, significant downgrade from Qwen3.5 for creative writing and prose for example. Yes, it's better at agentic and coding, but it regresses in many non-coding areas compared to Qwen3.5.
Of course, I do expect the 3.8 ones to perform better for agentic coding.
Tech tends to get boring when you judge current products against the hypothetical capabilities of unannounced products that may never ship. It's like comparing Nikon cameras against Canon camera rumours, or comparing iPhones against unannounced and therefore largely imaginary Samsungs.
- If they do a Qwen 3.8 35B A3B (and I hope they do because I love the 3.6 version)
- and if it beats 3.6 27B by all metrics
… then the local open weights world will be a better place.
But they have said nothing about it and they dropped several weight classes for 3.6, so who is to say they won't drop the 35B? And even if they don't, this is a tall order; why would the MoE tradeoffs no longer be apparent? (Again, I really like both the Qwen and Gemma MoEs)
FWIW I am enjoying testing Muse Glimmer — it's really quite impressive on chat, has nice terse and even amusing thinking traces, a bit of brass to it, and I'm hoping it will be good on agentic stuff.
Surprising that Meta don't host this model, even as rate-limited free-tier.
> open weight version of Muse Spark 1.2
Wait. Is this "version" different from what Meta serves?
UPD. was wrong on smaller, it's actually much larger
- muse glimmer: 15.9GB
- qwen 3.6 27B: 17.6GB
My video card is so close to its limit that these GB thresholds are mattering too much for me :D
UPD, NVM, got misled by comments here. It is actually almost 60 GB so much larger