[HN Gopher] Qwen3-Omni-Flash-2025-12-01:a next-generation native...
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Qwen3-Omni-Flash-2025-12-01:a next-generation native multimodal
large model
Author : pretext
Score : 164 points
Date : 2025-12-10 16:13 UTC (6 hours ago)
(HTM) web link (qwen.ai)
(TXT) w3m dump (qwen.ai)
| dvh wrote:
| I asked: "How many resistors are used in fuzzhugger phantom
| octave guitar pedal?". It replied 29 resistors and provided a
| long list. Answer is 2 resistors:
| https://tagboardeffects.blogspot.com/2013/04/fuzzhugger-phan...
| iFire wrote:
| > 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.
| iFire wrote:
| I tend to pick things where I think the answer is in the
| introduction material like exams that test what was taught.
| dvh wrote:
| "I don't know" would be perfectly reasonable answer
| MaxikCZ wrote:
| I feel like theres a time in near future where LLMs will be
| too cautious to answer any questions they arent sure about,
| and most of the human effort will go into pleading the LLM
| to at least try to give an answer, which will almost always
| be correct anyways.
| plufz wrote:
| That would be a great if you could have a setting like
| temperature 0.0-1.0 (Only answer if you are 100% to guess
| as much as you like).
| littlestymaar wrote:
| It's not going to happen as the user would just leave the
| platform.
|
| It would be better for most API usage though, as for
| business doing just a fraction of the job with 100%
| accuracy is often much preferable than claiming to do
| 100% but 20% is garbage.
| kaoD wrote:
| > as someone not having a database of the web, I wouldn't be
| able to calculate either result
|
| And that's how I know you're not an LLM!
| esafak wrote:
| This is just trivia. I would not use it to test computers -- or
| humans.
| parineum wrote:
| Everything is just trivia until you have a use for the
| answer.
|
| OP provided a we link with the answer, aren't these models
| supposed to be trained on all of that data?
| esafak wrote:
| There is nothing useful you can do with this information.
| You might as well memorize the phone book.
|
| The model has a certain capacity -- quite limited in this
| case -- so there is an opportunity cost in learning one
| thing over another. That's why it is important to train on
| quality data; things you can build on top of.
| DennisP wrote:
| Just because it's in the training data doesn't mean the
| model can remember it. The parameters total 60 gigabytes,
| there's only so much trivia that can fit in there so it has
| to do lossy compression.
| littlestymaar wrote:
| It's good way to assess the model with respect to
| hallucinations though.
|
| I don't think a model should know the answer, but it must be
| able to know that it doesn't know if you want to use it
| reliably.
| esafak wrote:
| No model is good at this yet. I'd expect the flagships to
| solve the first.
| brookst wrote:
| Where did you try it? I don't see this model listed in the
| linked Qwen chat.
| strangattractor wrote:
| Maybe it thinks some of those 29 are in series:)
| mettamage wrote:
| I wonder if with that music analysis mode, you can also make your
| own synths
| sosodev wrote:
| Does Qwen3-Omni support real-time conversation like GPT-4o?
| Looking at their documentation it doesn't seem like it does.
|
| 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.
| dsrtslnd23 wrote:
| it seems to be able to do native speech-speech
| sosodev wrote:
| It does for sure. I did some more digging and it does real-
| time too. That's fascinating.
| red2awn wrote:
| None of inference frameworks (vLLM/SGLang) supports the full
| model, let alone non-nvidia.
| sosodev wrote:
| That's unfortunate but not too surprising. This type of model
| is very new to the local hosting space.
| AndreSlavescu wrote:
| We actually deployed working speech to speech inference that
| builds on top of vLLM as the backbone. The main thing was to
| support the "Talker" module, which is currently not supported
| on the qwen3-omni branch for vLLM.
|
| Check it out here:
| https://models.hathora.dev/model/qwen3-omni
| red2awn wrote:
| Nice work. Are you working on streaming input/output?
| AndreSlavescu wrote:
| Yeah, that's something we currently support. Feel free to
| try the platform out! No cost to you for now, you just
| need a valid email to sign up on the platform.
| sosodev wrote:
| Is your work open source?
| ivape wrote:
| 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.
| sosodev wrote:
| I did find this app: https://github.com/gabber-dev/gabber
|
| 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
| potatoman22 wrote:
| 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.
| binsquare wrote:
| Does anyone else find that there's hard to pin down reason of
| life-lessness in the speech of these voice models?
|
| 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?
| colechristensen wrote:
| I'm perfectly ok with and would prefer an AI "accent".
| esafak wrote:
| > Sounds completely normal but I can immediately tell it is ai.
|
| Maybe that's a good thing?
| sosodev wrote:
| 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.
| Lapel2742 wrote:
| 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.
| nunodonato wrote:
| I hate with a passion the over-americanized "accent" of
| chatgpt voices. Give me a bland one any day of the week
| vessenes wrote:
| 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.
| rarisma wrote:
| GPT4o in the charts is crazy.
| BoorishBears wrote:
| 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.
| banjoe wrote:
| Wow, crushing 2.5 Flash on every benchmark is huge. Time to move
| all of my LLM workloads to a local GPU rig.
| embedding-shape wrote:
| 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.
| MaxikCZ wrote:
| This. Last benchmarks of DSv3.2spe hinted at beating
| basically everything, yet in my testing even sonnet is miles
| ahead both in terms of speed and accuracy
| red2awn wrote:
| Why would you use an Omni model for text only workload... There
| is Qwen3-30B-A3B.
| gardnr wrote:
| This is a 30B parameter MoE with 3B active parameters and is the
| successor to their previous 7B omni model. [1]
|
| 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
| gardnr wrote:
| 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.
| pythux wrote:
| They link to: https://huggingface.co/collections/Qwen/qwen3-o
| mni-68d100a86... from the blog post but it does seem like
| this redirects to their main space on HF so maybe they didn't
| yet make the model public?
| olafura wrote:
| Looks like it's not open source:
| https://www.alibabacloud.com/help/en/model-studio/qwen-omni#...
| coder543 wrote:
| No... that website is not helpful. If you take it at face
| value, it is claiming that the previous Qwen3-Omni-Flash
| wasn't open either, but that seems wrong? It is very common
| for these blog posts to get published before the model
| weights are uploaded.
| red2awn wrote:
| The previous -Flash weight is closed source. They do have
| weights for the original model that is slightly behind in
| performance
| https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct
| coder543 wrote:
| Based on things I had read over the past several months,
| Qwen3-Flash seemed to just be a weird marketing term for
| the Qwen3-Omni-30B-A3B series, not a different model. If
| they are not the same, then that is
| interesting/confusing.
| red2awn wrote:
| It is an in-house closed weight model for their own chat
| platform, mentioned in Section 5 of the original paper:
| https://arxiv.org/pdf/2509.17765
|
| I've seen it in their online materials too but can't seem
| to find it now.
| tensegrist wrote:
| > 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
| andy_xor_andrew wrote:
| > 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.
| plipt wrote:
| The link[1] at the top of their article to HuggingFace goes
| to some models named Qwen3-Omni-30B-A3B that were last
| updated in September. None of them have "Flash" in the name.
|
| 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
|
| 1: https://huggingface.co/collections/Qwen/qwen3-omni
| red2awn wrote:
| 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.
| plipt wrote:
| 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?
| red2awn wrote:
| Flash is a closed weight version of
| https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct (it
| is 30B but with addtional training on top of the open weight
| release). They deploy the flash version on Qwen's own chat.
| plipt wrote:
| Thanks
|
| 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?
| red2awn wrote:
| They had a Flash variant released alongside the original
| open weight release. It is also mentioned in Section 5 of
| the paper: https://arxiv.org/pdf/2509.17765
|
| 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)
| Aissen wrote:
| Is this a new proprietary model?
| sim04ful wrote:
| The main issue I'm facing with realtime responses (speech output)
| is how to separate non-diegetic outputs (e.g thinking, structured
| outputs) from outputs meant to be heard by the end user.
|
| I'm curious how anyone has solved this
| artur44 wrote:
| 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.
| pugio wrote:
| There is no TTS here. It's a native audio output model which
| outputs audio tokens directly. (At least, that's how the
| other real-time models work. Maybe I've misunderstood the
| Qwen-Omni architecture.)
| artur44 wrote:
| True, but even with native audio-token models you still
| need to split the model's output channels.
| Reasoning/internal tokens shouldn't go into the audio
| stream only user-facing content should be emitted as audio.
| The principle is the same, whether the last step is TTS or
| audio token generation.
| stevenhuang wrote:
| Wayback for those that can't reach
| https://web.archive.org/web/20251210164048/https://qwen.ai/b...
| terhechte wrote:
| Is there a way to run these Omni models on a Macbook quantized
| via GGUF or MLX? I know I can run it in LMStudio or Llama.cpp but
| they don't have streaming microphone support or streaming webcam
| support.
|
| 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?
| mobilio wrote:
| Yes - there is a way: https://github.com/ggml-org/whisper.cpp
| novaray wrote:
| Whisper and Qwen Omni models have completely different
| architectures as far as I know
| tgtweak wrote:
| 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...
| aschobel wrote:
| Looks to be API only. Bummer.
| readyplayeremma wrote:
| 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.
| aschobel wrote:
| It is super confusing. I also thought this initially was open
| weights.
| devinprater wrote:
| Wow, just 32B? This could almost run on a good device with 64 GB
| RAM. Once it gets to Ollama I'll have to see just what I can get
| out of this.
| plipt wrote:
| 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?
| apexalpha wrote:
| I run these on a 48gb Mac because of the universal ram.
| vessenes wrote:
| Interesting - when I asked the omni model at qwen.com what
| version it was, I got a testy "I don't have a version" and then
| was told my chat was blocked for inappropriate content. A second
| try asking for knowledge cutoff got me the more equivocal "2024,
| but I know stuff after that date, too".
|
| No idea how to check if this is actually deployed on qwen.com
| right now.
| zamadatix wrote:
| > 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).
| vessenes wrote:
| Thanks - yes - I did. The blog post suggests clicking the
| 'voice' icon on the bottom right - that's what I did.
| mh- wrote:
| For what it's worth, that's not a reliable way to check what
| model you're interacting with.
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