Posted by pretext 12/10/2025
You can expect this model to have similar performance to the non-omni version. [2]
There aren't many open-weights omni models so I consider this a big deal. I would use this model to replace the keyboard and monitor in an application while doing the heavy lifting with other tech behind the scenes. 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.
1. https://huggingface.co/Qwen/Qwen2.5-Omni-7B
2. https://artificialanalysis.ai/models/qwen3-30b-a3b-instruct
- 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.
I've seen it in their online materials too but can't seem to find it now.
last i checked (months ago) claude used to do this
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?
Was it being closed weight obvious to you from the article? Trying to understand why I was confused. Had not seen the "Flash" designation before
Also 30B models can beat a semi-recent 235B with just some additional training?
For the evals it's probably just trained on a lot of the benchmark adjacent datasets compared to the 235B model. Similar thing happened on other model today: https://x.com/NousResearch/status/1998536543565127968 (a 30B model trained specifically to do well in maths get near SOTA scores)
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.
The benchmark table shows this Flash model beating their Qwen3-235B-A22B. I dont see how that is possible if it is a 30B-A3B model.
I don't see a mention of a parameter count anywhere in the article. Do you? This may not be an open weights model.
This article feels a bit deceptive
I wish I could delete the comment.
Are there any open weight models that do? Not talking about speech to text -> LLM -> text to speech btw I mean a real voice <-> language model.
edit:
It does support real-time conversation! Has anybody here gotten that to work on local hardware? I'm particularly curious if anybody has run it with a non-nvidia setup.
“He’s on record saying he broke the record for spinning a record.”
OTOH my point that the thing being suggested to be tested is not testable by seeing whether or not the system is capable of distinguishing homophones, but might be by seeing whether or not it distingishes heteronyms still stands. (The speculation that the record/record distinction intended was one that is actually a pair of heteronyms and that the error was merely the use of the word “homophone" in place of “heteronym”, rather than the basic logic of the comment is somewhat tangential to the main point.)
However, It still doesn't seem capable of producing any of the sounds, like laughter, that I would expect from a native voice model.
Check it out here: https://models.hathora.dev/model/qwen3-omni
https://github.com/vllm-project/vllm-omni
I have not yet tested out if this does full speech to speech, but this seems like a promising workspace for omni-modal models.
In our deployment, we do not actually tune the model in any way, this is all just using the base instruct model provided on huggingface:
https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct
And with the potential concern around conversation turns, our platform is designed for one-off record -> response flows. But via the API, you can build your own conversation agent to use the 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.
The creator posted a little demo of it working with Qwen3 Omni that is quite impressive: https://www.youtube.com/watch?v=5DBFVe3cLto
He didn't include any details regarding how the model was running though
Qwen usually provides example code in Python that requires Cuda and a non-quantized model. I wonder if there is by now a good open source project to support this use case?
https://github.com/QwenLM/Qwen3-Omni#vllm-usage
https://github.com/QwenLM/Qwen3-Omni?tab=readme-ov-file#laun...
I'm curious how anyone has solved this
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?
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.
Especially in the fruit pricing portion of the video for this model. Sounds completely normal but I can immediately tell it is ai. Maybe it's intonation or the overly stable rate of speech?
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 ChatGPT has the most lifelike speech with their voice models. They seem to have invested heavily in that area while other labs focused elsewhere.
Maybe that's a good thing?