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Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch
- jychang 1y agoThis is over a month old, they released the weights a long time ago.
- embedding-shape 1y agoIsn't OpenIA "Deep research" (not "DeepResearch") a methodology/tooling thing, and you'll get different responses depending on what specific model you use with it? As far as the UI allows you to, you could use Deep research with GPT-5, GPT-4o, o3 and so on, and that'll have an impact on the responses. Skimming the paper and searching for some simple terms makes it seem like they never expand on what exact models they've used, just that they've used a specific feature from ChatGPT?
- deleted 1y ago[deleted]
- simonw 1y agoAt this point "deep research" is more of a pattern - OpenAI and Perplexity and Google Gemini all offer products with that name which work essentially the same way, and Anthropic and Grok have similar products with a slightly different name attached. The pattern is effectively long-running research tasks that drive a search tool. You give them a prompt, they churn away for 5-10 minutes running searches and they output a report (with "citations") at the end. This Tongyi model has been fine-tuned to be really good at using its search tool in a loop to produce a report.
- embedding-shape 1y agoYes, but I think my previous point still matter, namely what exact model is being used greatly affects the results. So without specifying which model is being used, it's really hard to know what is better than something else, because we don't understand what the underlying model is, and if it's better because of the model itself, or the tooling, which feels like an important distinction.
- aliljet 1y agoSunday morning, and I find myself wondering how the engineering tinkerer is supposed to best self-host these models? I'd love to load this up on the old 2080ti with 128gb of vram and play, even slowly. I'm curious what the current recommendation on that path looks like. Constraints are the fun part here. I know this isn't the 8x Blackwell Lamborghini, that's the point. :)
- CuriousSkeptic 1y agoIm sure this guy has some helpful hints on that: https://youtube.com/@azisk https://youtube.com/@azisk
- homarp 1y agollama.cpp gives you the most control to tune it for your machine.
- giobox 1y agoIf you just want to get something running locally as fast as possible to play with (the 2080ti typically had 11gb of VRAM which will be one of the main limiting factors), the ollama app will run most of these models locally with minimum user effort: https://ollama.com/ https://ollama.com/ If you really do have a 2080ti with 128gb of VRAM, we'd love to hear more about how you did it!
- exe34 1y agollama.cpp + quantized: https://huggingface.co/bartowski/Alibaba-NLP_Tongyi-DeepResearch-30B-A3B-GGUF/blob/main/README.md https://huggingface.co/bartowski/Alibaba-NLP_Tongyi-DeepRese... get the biggest one that will fit in your vram.
- davidsainez 1y agoThis is the way. I managed to run (super) tiny models on CPU only with this approach.
- trebligdivad 1y agoHow do people deal with all the different quantisations? Generally if I see an Unsloth I'm happy to try it locally; random other peoples...how do I know what I'm getting? (If nothing else Tongyi are currently winning AI with cutest logo)
- zurfer 1y agoIt makes me wonder if we'll see an explosion of purpose trained LLMs because we hit diminishing returns on invest with pre training or if it takes a couple of months to fold these advantages back into the frontier models. Given the size of frontier models I would assume that they can incorporate many specializations and the most lasting thing here is the training environment. But there is probably already some tradeoff, as GPT 3.5 was awesome at chess and current models don't seem trained extensively on chess anymore.
- deepanwadhwa 1y ago-> GPT 3.5 was awesome at chess I don't agree with this. I did try to play chess with GPT3.5 and it was horrible. Full of hallucinations.
- miki123211 1y agoIt was GPT-3 I think. As far as I remember, it's post-training that kills chess ability for some reason (GPT-3 wasn't post-trained).
- Imustaskforhelp 1y agoThis is so interesting, I am curious as to why, can you (or anyone) please provide any resources or insightful comments about it, they would really help a ton out here, thanks!
- pixelmelt 1y agoGpt3 was trained on completion data so it likely saw lots of raw chess games layed out in whatever standard format moves are listed in, while 3.5 was post trained on instruct data (talking back and forth) which would have needed to explicitly include those chess games as conversational training data for it to retain as much as it would otherwise
- zurfer 1y agoYeah I was not precise; it was `gpt-3.5-turbo-instruct`, other variants weren't trained on it apparently. https://dynomight.substack.com/p/chess https://dynomight.substack.com/p/chess
- mehdibl 1y agoIt's a Qwen 3 MoE fine tune...
- rokob 1y agoThis whole series of work is quite cool. The use of `word-break: break-word;` makes this really hard to read though.
- soared 1y agoI actually can’t read it for some reason? My brain just can’t connect the words
- don-bright 1y agoso it appears the entire text has been Translated with non-breaking space unicode x00a0 instead of normal spaces x0020, so the web layout is considering all paragraph text as a super-long single word ('the\00a0quick\00a0\brown\00a0fox' instead of 'the quick brown fox') - the non-breaking space character appears identically to breaking-space when rendered but underlying coding breaks the concept of "break at end of word" because there is no end as 00a0 literally means "non-breaking"). per Copilot spending a half hour explaining this to me, apparently this can be fixed by opening web browser developer view, and copy/pasting this code into the console. function replaceInTextNodes(node) { if (node.nodeType === Node.TEXT_NODE) { node.nodeValue = node.nodeValue .replace(/\u00A0/g, ' '); } else { node.childNodes.forEach(replaceInTextNodes); } } replaceInTextNodes(document.body);
- nl 1y agoThis is completely fascinating although puzzling how that happens. The script is great!
- dlisboa 1y agoThat’s why typography matters. You can’t read it because a very basic convention has been broken here and that throws everything off.
- theflyestpilot 1y agoI hope the translation for this is actually "Agree" Deep research. Just a dig at "You are absolutely right!" sycophancy.
- numpad0 1y agoTIL the "full" name of Alibaba Qwen is 通義千問(romanized as "Tongyi Qianwen", something along "knows all thousand questions"), of which the first half without the Chinese accent flags is romanized identically to "同意", meaning "same intents" or "agreed". The Chinese version of the link says "通义 DeepResearch" in the title, so doesn't look like the "agree" to be the case. Completely agreed that it would be hilarious. 1: https://www.alibabacloud.com/en/solutions/generative-ai/qwen?_p_lc=1 https://www.alibabacloud.com/en/solutions/generative-ai/qwen...
- rahimnathwani 1y agoFor people who don't read Chinese: the two 'yi' characters numpad0 mentioned (义 and 義) are the same, but written in different variants of Chinese script (Simplified/Traditional).
- Traubenfuchs 1y agoIt still feels to me like OpenAI has zero moat. There are like 5 paid competitors + open source models. I switch between gemini and ChatGpt whenever I feel one fails to fully grasp what I want, I do coding in claude. How are they supposed to become the 1 trillion dollar company they want to be, with strong competition and open source disruptions every few months?
- rokob 1y agoI don’t know if they can pull it off but a lot of companies are built on strong enterprise sales being able to sell free stuff with a bow on it to someone who doesn’t know better or doesn’t care.
- isoprophlex 1y agoPremium grade deals with Oracle. They will bullshit their way into government and enterprise environments where all the key decision makers are clueless and/or easily manipulated.
- nickpinkston 1y agoYea, I agree. Arguably LLMs are both (1) far easier to switch between models than it is today to switch from AWS / GCP / Azure systems, and (2) will be rapidly decreasing switching costs for your legacy systems to port to new ones - ie Oracle's, etc. whole business model. Meanwhile, the whole world is building more chip fabs, data centers, AI software/hardware architectures, etc. Feels more like we're headed to commodification of the compute layer more than a few giant AI monopolies. And if true, that's actually even more exciting for our industry and "letting 100 flowers bloom".
- whiplash451 1y agoIsn’t the moat in the product/UI/UX? I use Claude daily and love the “scratch notebook” feel of it. The barebone model does not get you any of this.
- hamandcheese 1y agoI agree that the scaffolding around the model contributes greatly to the experience. But it doesn't take billions of dollars in GPUs to do that part.
- steveny3456 1y agoJuju
- krystofee 1y agoIsnt it huge deal, that this 30B model can compare and surpass huge closed models?
- tbruckner 1y agoHas anyone found these deep research tools useful? In my experience, they generate really bland reports don't go much further than summarization of what a search engine would return.
- ainch 1y agoThe reports are definitely bland, but I find them very helpful for discovering sources. For example, if I'm trying to ask an academic question like "has X been done before," sending something to scour the internet and find me examples to dig into is really helpful - especially since LLMs have some base knowledge which can help with finding the right search terms. It's not doing all the thinking, but those kind of broad overviews are quite helpful, especially since they can just run in the background.
- kmarc 1y agoI caught myself that most of my LLM usage is like this: ask a loaded, "filter question" I more or less know the answer for, and mostly skip the prose and get to the links to its sources.
- ukuina 1y agoThe "loaded question" approach works for getting MUCH better pro/con lists, too, in general, across all LLMs.
- vogu66 1y agoI do that too, I wonder how much of it is the LLM being helpful and how much of it is the RAG algorithm somehow providing better references to the LLM than a google search can?
- andy99 1y agoMy experience is the same as yours. It feels to me (similar to most LLM writing) like they write for someone who’s not going to read it or use it but is going to glance at it and judge the quality that way and assume it’s good. Not to different from a lot of consulting reports, in fact, and pretty much of no value if if you’re actually trying to learn something. Edit to add: even the name “deep research” to me feels like something defined to appeal to people who have never actually done or consumed research, sort of like the whole “phd level” thing.
- DataDaemon 1y agoUnfortunately soon China will take lead in AI.
- aeve890 1y agoUnfortunately? May I ask why? What country would you like to be the lead in AI?
- ninetyninenine 1y ago[flagged]
- Krasnol 1y ago[flagged]
- GordonS 1y agoI rather think the GP was being sarcastic. At least, I hope they were.
- Krasnol 1y ago[flagged]
- ninetyninenine 1y ago[flagged]
- tomhow 1y agoPlease don't troll on HN.
- tomhow 1y agoPlease don't engage in political/nationalistic battle on HN, and make an effort to observe all guidelines when participating here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- sumo43 1y agoI made a 4B Qwen3 distill of this model (and a synthetic dataset created with it) a while back. Both can be found here: https://huggingface.co/flashresearch https://huggingface.co/flashresearch
- Nymbo 1y agoJust tried this out with my web search mcp, extremely impressed with it. Never seen deep research this good from a model so small.
- Imustaskforhelp 1y agoCan you please create a huggingface space or something similar, I am not sure about the state of huggingface but I would love to be able to try it out in a browser or something similar if possible as I am really curious and I just love qwen3 4b as they were one of the models which work even on my intel integrated gpu graphics card at a really impressive rate and they were really nice the last time I tried but this looks even more cooler/practical. I had once an idea of using something like qwen4 or some pre-trained AI model just to do a (to censor or not to) idea after the incidents of mecha-hitler. I thought if there was some extremely cheap model which could detect that it is harmful that the AI models of Grok itself couldn't recognize, it would've been able to prevent the absolute advertising/ complete disaster that happened. What are your thoughts on it? I would love to see an Qwen 4B of something similar if possible if you or anyone is up to the challenge or any small LLM's in generals. I just want to know if this idea fundamentally made sense or not. Another idea was to use it for routing purposes similar to what chatgpt does but I am not sure about that now really but I still think that it maybe worth it but this routing idea I had was before chatgpt had implemented it, so now after it implemented, we are gonna find some more data/insights about if its good or not/ worth it, so that's nice.
- bigyabai 1y ago> What are your thoughts on it? You don't really need an entire LLM to do this - lightweight encoder models like BERT are great at sentiment analysis. You feed it an arbitrary string of text, and it just returns a confidence value from 0.0 to 1.0 that it matches the characteristics you're looking for.
- brutus1213 1y agoI recently got a 5090 with 64 GB of RAM (intel cpu). Was just looking for a strong model I can host locally. If I had performance of GPT4-o, I'd be content. Are there any suggestions or cases where people got disappointed?
- p1esk 1y ago5090 has 32GB of RAM. Not sure if that’s enough to fit this model.
- svnt 1y agoIt should fit enough of the layers to make it reasonably performant.
- IceWreck 1y agoLlamaCPP supports offloading some experts in a MoE model to CPU. The results are very good and even weaker GPUs can run larger models at reasonable speeds. n-cpu-moe in https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md https://github.com/ggml-org/llama.cpp/blob/master/tools/serv...
- bogtog 1y agoGPT-OSS-20B at 4- or 8-bits is probably your best bet? Qwen3-30b-a3b probably the next best option. Maybe there exists some 1.7 or 2 bit version of GPT-OSS-120B
- yalogin 1y agoIn my experience using these supposed expert models, they are all more or less the same given they all are trained on the same internet data. The differentiation and value is in the context window management and how relevant info from your session is pulled in. So it’s the interface to the model that makes all the difference. Even there the differences are quite minimal. That is because all these companies want to toe the line between providing functionality to keep the users engaged and pushing them to sign up for the subscription. All this to ask the question, if I host these open source models locally, how is the user interface layer that remembers and picks the right data from my previous session and the agentic automation and others implemented? Do I have to do it myself or are the free options for that?
- viksit 1y agothis is a great question. what are the main use cases that you have for this? i’ve been working on a library for something similar and exposing it via an mcp interface. would love to pick your brain on this (@viksit on twitter)
- ninetyninenine 1y agoIs China dominating the US in terms of AI? Given that they currently have a model that beats the best models at all formal quantitative benchmarks? What is the state of AI in China? My personal feeling is that it doesn't dominate the zeitgeist in China as it does in the US and despite this because of the massive amount of intellectual capital they have just a small portion of their software engineering talent working on this is enough to go head to head with us even though it only takes a fraction of their attention.
- idiotsecant 1y agoI think the lesson of the Chinese catchup in AI is that there is a massive disadvantage in being first, in this domain. You can do all the hard work and your competitors can distill that work out of your model for pennies on the dollar. Why should anyone want to do the work?
- MaxPock 1y agoThis sounds like copium . If it was just about distillation,we'd be seeing many awesome models from Europe ,Japan and even India.
- mike_hearn 1y agoIt's certainly both a lot more than distillation and at least some Chinese labs have been cloning OpenAI via distillation. That's why they instituted much tighter ID verification requirements earlier this year. No, the reason you don't see many open source models coming from the rest-of-world (other than Mistral in France) is that you still need a ton of capital to do it. China can compete because the CCP used a combination of the Great Firewall and lax copyright/patent enforcement to implement protectionism for internet services, which is a unique policy (one that obviously came with massive costs too). This allowed China to develop home grown tech companies which then have the datacenters, capital and talent density to train models. Rest of world didn't do this and wasn't able to build up domestic tech industries competitive with the USA.
- whiplash451 1y agoHas anyone tried running this on a 5090 or 6000 pro? What throughput do you see?
- VladVladikoff 1y agoRecently I gave a list of 300 links to deep research and asked it to go through each one to analyze a certain question about them. Repeatedly it would take shortcuts and not actually do the full list. Is this caused by a context window limits? Or maybe Open AI limits request size? Is it possible to not run into these types of limits with locally hosted models?
- oofbey 1y agoI’ve also had extremely poor luck getting any LLM agent to go through a long list of repetitive tasks. Don’t know why. I’d guess it’s because they’re trained for transactional responses, and thus are horrible at repute anything.
- ukuina 1y agoVery much this. You are better off asking it a write a script to invoke itself N times across the task list.
- threecheese 1y agoSame. I think there’s an untapped market (feature really) here, which if isn’t solved by GPT-next will start to reveal itself as a problem more and more. LLMs are really bad at being comprehensive, in general, and from one inference to the next their comprehensive-ness varies wildly. Because LLMs are surprising the hell out of everyone with their abilities, less attention is paid to this; they can do a thing well, and for now that’s good enough. As we scale usage, I expect this gap will become more obvious and problematic (unless solved in the model, like everything else). A solution I’ve been toying with is something like a reasoning step, which could probably be done with mostly classical NLP, that identifies constraints up front and guides the inference to meet them. Like a structured output but at a session level. I am currently doing what you suggest though, I have the agent create a script which invokes … itself … until the constraints are met, but that obviously requires that I am engaged there; I think it could be done autonomously, with at least much better consistency (at the end of the day even that guiding hand is inference based and therefore subject to the same challenges).
- ugh123 1y agoSlightly off topic but why does word wrapping seem to be broken in this site? Chrome on Android
- rippeltippel 1y agoThank you for pointing that out, I was about to ask the same. It's giving my OCD a hard time reading it.
- blueboo 1y agoWhen was the last time you did a deep research? Good agents just do research as necessary. I find GPT5 Pro >> all the top DR agents
- zwaps 1y agoThe OpenAI numbers are a red herring anyway. For most plans, Deep Research is capped at around 20 sources, making it for many cases the least useful research agent, in particular worse than a thinking mode Gpt5 query
- andai 1y agoTongyi provides this model on OpenRouter, including a free version. https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b:free https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b:fr...
- PunchTornado 1y agoit rivals a model that is obsolete? who is using openai deepresearch when there are so many better models out there?
- incomingpain 1y agoI love this one: https://github.com/LearningCircuit/local-deep-research https://github.com/LearningCircuit/local-deep-research I tied it together with qwen3 30b thinking. Very easy to get it up and running, but lots of the numbers are shockingly low. You need to boost iterations and context. Especially easy if you already run searxng locally. I havent finished tuning the actual settings, but for the detailed report it'll take ~20 minutes and so far has given pretty good results. Similar to openai's deep research. Mine often has ~100 sources. But something I have noticed. It didnt seem to me the model was important. The magic was moreso in the project. Getting deep with higher iterations and more results.
- whatpeoplewant 1y agoGreat to see an open 30B MoE aimed at “deep research.” These shine when used in a multi-agent setup: run parallel agentic AI workers (light models for browsing/extraction) and reserve the 30B agentic LLM for planning, tool routing, and verification—keeping latency/cost in check while boosting reliability. MoE specialization fits distributed agentic AI well, but you’ll want orchestration for retries/consensus and task-specific evals on multi-hop web research to guard against brittle routing and hallucinations.