This is a stack of models:

- 650M Audio Encoder

- 540M Vision Encoder

- 30B-A3B LLM

- 3B-A0.3B Audio LLM

- 80M Transformer/200M ConvNet audio token to waveform

This is a closed source weight update to their Qwen3-Omni model. They had a previous open weight release Qwen/Qwen3-Omni-30B-A3B-Instruct and a closed version Qwen3-Omni-Flash.

You basically can't use this model right now since none of the open source inference framework have the model fully implemented. It works on transformers but it's extremely slow.

Looks like it's not open source: https://www.alibabacloud.com/help/en/model-studio/qwen-omni#...

I can't find the weights for this new version anywhere. I checked modelscope and huggingface. It looks like they may have extended the context window to 200K+ tokens but I can't find the actual weights.

> There is also a reasoning version, which might be a bit amusing in an interactive voice chat if it pronounces the thinking tokens while working through to a final answer.

last i checked (months ago) claude used to do this

I dont think the Flash model discussed in the article is 30B

Their benchmark table shows it beating Qwen3-235B-A22B

Does "Flash" in the name of a Qwen model indicate a model-as-a-service and not open weights?

Haha, you could hear how it’s mind thinks, maybe by putting a lot of reverb on the thinking tokens or some other effect…

> This is a 30B parameter MoE with 3B active parameters

Where are you finding that info? Not saying you're wrong; just saying that I didn't see that specified anywhere in the linked page, or on their HF.

From what I can tell, their official chat site doesn't have a native audio -> audio model yet. I like to test this through homophones (e.g. record and record) and asking it to change its pitch or produce sounds.

None of inference frameworks (vLLM/SGLang) supports the full model, let alone non-nvidia.

That's exciting. I doubt there are any polished voice chat local apps yet that you can easily plug this into (I doubt the user experience is "there" yet). Even stuff like Silly Tavern is near unusable, lots of work to be done on the local front. Local voice models are what's going to enable that whole Minority Report workflow soon enough (especially if commands and intent are determined at the local level, and the meat of the prompt is handled by a larger remote model).

This is part of programming that I think is the new field. There will be tons of work for those that can build the new workflows which will need to be primarily natural language driven.

it seems to be able to do native speech-speech

You can probably follow the vLLM instructions for omni here, then use the included voice demo html to interface with it:

https://github.com/QwenLM/Qwen3-Omni#vllm-usage

https://github.com/QwenLM/Qwen3-Omni?tab=readme-ov-file#laun...

Yes - there is a way: https://github.com/ggml-org/whisper.cpp

A simple way is to split the model’s output stream before TTS. Reasoning/structured tokens go into one bucket, actual user-facing text into another. Only the second bucket is synthesized. Most thinking out loud issues come from feeding the whole stream directly into audio.

I see that their HuggingFace link goes to some Qwen3-Omni-30B-A3B models that show a last updated date of September

The benchmark table in their article shows Qwen3-Omni-Flash-2025-12-01 (and the previous Flash) as beating Qwen3-235B-A22B. How is that possible if this is only a 30B-A3B model? Also confusing how that comparison column starts out with one model but changes them as you descend down the table.

I don't see any FLASH variant listed on their Hugginface. Am i just missing it or are these specifying a model only used for their API service and there are no open weights to download?

I run these on a 48gb Mac because of the universal ram.

It seems to be available on Qwen chat? https://chat.qwen.ai/settings/model?id=qwen3-omni-flash-2025...

The models are right here, one of the first links in the post: https://huggingface.co/collections/Qwen/qwen3-omni

edit: Nevermind, in spite of them linking it at the top, they are the old models. Also, the HF demo is calling their API and not using HF for compute.

IMHO it's not lifeless. It's just not overly emotional. I definitely prefer it that way. I do not want the AI to be excited. It feels so contrived.

On the video itself: Interesting, but "ideal" was pronounced wrong in German. For a promotional video, they should have checked that with native speakers. On the other hand its at least honest.

I think it's because they've crammed vision, audio, multiple voices, prosody control, multiple languages, etc into just 30 billion parameters.

I think ChatGPT has the most lifelike speech with their voice models. They seem to have invested heavily in that area while other labs focused elsewhere.

I'm not convinced its end-to-end multimodal - in that case, you'll have a speech synthesis section and this will be some of the result. You could test by having it sing or do some accents, or have it talk back to you in an accent you give it.

> Sounds completely normal but I can immediately tell it is ai.

Maybe that's a good thing?

I'm perfectly ok with and would prefer an AI "accent".

Just remember to benchmark it yourself first with you private task collection, so you can actually measure them against each other. Pretty much any public benchmark is unreliable at this moment, and making model choices based on other's benchmarks is bound to leave you disappointed.

Why would you use an Omni model for text only workload... There is Qwen3-30B-A3B.

Except the image benchmarks are compared against 2.0, which seems suspicious that they would casually drop to an older model for those.

> How many resistors are used in fuzzhugger phantom octave guitar pedal?

Weird, as someone not having a database of the web, I wouldn't be able to calculate either result.

This is just trivia. I would not use it to test computers -- or humans.

Where did you try it? I don’t see this model listed in the linked Qwen chat.

Lol I asked it how many rooms I have in my house and it got that wrong. Llms are useless amirite

Maybe it thinks some of those 29 are in series:)

Why? gpt-realtime is finalized gpt-4o. Gemini Live is still 2.5.

Not their fault frontier labs are letting their speech to speech offerings languish.

> No idea how to check if this is actually deployed on qwen.com right now.

Assuming you mean qwen.ai, when you run a query it should take you to chat.qwen.ai with the list of models in the top left. None of the options appear to be the -Omni variant (at least when anonymously accessing it).

For what it's worth, that's not a reliable way to check what model you're interacting with.