10 ms·
Qwen3-Max-Thinking
- siliconc0w 9mo agoI don't see a hugging face link, is Qwen no longer releasing their models?
- tosh 9mo agoafaiu not all of their models are open weight releases, this one so far is not open weight (?)
- sidchilling 9mo agoWhat would a good coding model to run on an M3 Pro (18GB) to get Codex like workflow and quality? Essentially, I am running out quick when using Codex-High on VSCode on the $20 ChatGPT plan and looking for cheaper / free alternatives (even if a little slower, but same quality). Any pointers?
- medvezhenok 9mo agoShort answer: there is none. You can't get frontier-level performance from any open source model, much less one that would work on an M3 Pro. If you had more like 200GB ram you might be able to run something like MiniMax M2.1 to get last-gen performance at something resembling usable speed - but it's still a far cry from codex on high.
- mittermayr 9mo agoat the moment, I think the best you can do is qwen3-coder:30b -- it works, and it's nice to get some fully-local llm coding up and running, but you'll quickly realize that you've long tasted the sweet forbidden nectar that is hosted llms. unfortunately.
- Mashimo 9mo agoA local model with 18GB of ram that has the same quality has codex high? Yeah, nah mate. The best could be GLN 4.7 Flash, and I doubt it's close to what you want.
- atwrk 9mo ago"run" as in run locally? There's not much you can do with that little RAM. If remote models are ok you could have a look at MiniMax M2.1 (minimax.io) or GLM from z.ai or Qwen3 Coder. You should be able to use all of these with your local openai app.
- duffyjp 9mo agoNothing. This summer I set up a dual 16GB GPU / 64GB RAM system and nothing I could run was even remotely close. Big models that didn't fit on 32gb VRAM had marginally better results but were at least of magnitude slower than what you'd pay for and still much worse in quality. I gave one of the GPUs to my kid to play games on.
- Tostino 9mo agoYup, even with 2x 24gb GPUs, it's impossible to get anywhere close to the big models in terms of quality and speed, for a fraction of the cost.
- mirekrusin 9mo agoI'm running unsloth/GLM-4.7-Flash-GGUF:UD-Q8_K_XL via llama.cpp on 2x 24G 4090s which fits perfectly with 198k context at 120 tokens/s – the model itself is really good.
- fsiefken 9mo agoI can confirm, running glm-4.7-flash-7e-qx54g-hi-mlx here, a 22gb model @q5 on m4 max pro and 59 tokens/s.
- jgoodhcg 9mo agoZ.ai has glm-4.7. Its almost as good for about $8/mo.
- margorczynski 9mo agoNot sure if it's me but at least for my use cases (software devl, small-medium projects) Claude Opus + Claude Code beats by quite a margin OpenCode + GLM 4.7. At least for me Claude "gets it" eventually while GLM will get stuck in a loop not understanding what the problem is or what I expect.
- zamalek 9mo agoRight, GLM is close But not close enough. If I have to spend $200 for Opus fallback i may as well not use it always. Still an unbelievable option if $200 is a luxury, the price-per-quality is absurd.
- evilduck 9mo agoThey are spending hundreds of billions of dollars on data centers filled with GPUs that cost more than an average car and then months on training models to serve your current $20/mo plan. Do you legitimately think there's a cheaper or free alternative that is of the same quality? I guess you could technically run the huge leading open weight models using large disks as RAM and have close to the "same quality" but with "heat death of the universe" speeds.
- marcd35 9mo agoantigravity is solid and has a generous free tier.
- tosh 9mo ago18gb RAM it is a bit tight with 32gb RAM: qwen3-coder and glm 4.7 flash are both impressive 30b parameter models not on the level of gpt 5.2 codex but small enough to run locally (w/ 32gb RAM 4bit quantized) and quite capable but it is just a matter of time I think until we get quite capable coding models that will be able to run with less RAM
- adam_patarino 9mo agoahem ... cortex.build Current test version runs in 8GB @ 60tks. Lmk if you want to join our early tester group!
- dust42 9mo agoMax was always closed.
- behnamoh 9mo agoSo the only way to run it is by using Qwen's API? No thanks. At least with Kimi and GLM, I can use Fireworks/whatever to avoid sending data to China.
- cmrdporcupine 9mo agoWhen I looked earlier, Qwen claims to have DCs in Singapore and (I think?) the US but now I can't seem to find where I saw that. Whether that means anything, I dunno.
- imrebuild 9mo ago[dead]
- throwaw12 9mo agoAghhh, I wished they release a model which outperforms Opus 4.5 in agentic coding in my earlier comments, seems I should wait more. But I am hopeful
- wyldfire 9mo agoBy the time they release something that outperforms Opus 4.5, Opus 5.2 will have been released which will probably be the new state-of-the-art. But these open weight models are tremendously valuable contributions regardless.
- wqaatwt 9mo agoQwen 3 Max wasn’t originally open, or did they realease?
- OGEnthusiast 9mo agoCheck out the GLM models, they are excellent
- khimaros 9mo agoMinimax m2.1 rivals GLM 4.7 and fits in 128GB with 100k context at 3bit quantization.
- lofaszvanitt 9mo agoLike these benchmarks mean anything.
- frankc 9mo agoOne of the ways the chinese companies are keeping up is by training the models on the outputs of the American fronteir models. I'm not saying they don't innovate in other ways, but this is part of how they caught up quickly. However, it pretty much means they are always going to lag.
- aurareturn 9mo ago
- Mashimo 9mo agoI tried to search, could not find anything, do they offer subscriptions? Or only pay per tokens?
- esafak 9mo agoI think they don't. I'd wait for the Cerebras release; they have a subscription offering called Cerebras Code for $50/month. https://www.cerebras.ai/pricing https://www.cerebras.ai/pricing
- isusmelj 9mo agoI just wanted to check whether there is any information about the pricing. Is it the same as Qwen Max? Also, I noticed on the pricing page of Alibaba Cloud that the models are significantly cheaper within mainland China. Does anyone know why? https://www.alibabacloud.com/help/en/model-studio/models?spm=a2ty_o06.30285417.0.0.1ef4c9219pMcnN#4c74a7d1841hr https://www.alibabacloud.com/help/en/model-studio/models?spm...
- epolanski 9mo agoI guess they want to partially subsidize local developers? Maybe that's a requirement from whoever funds them, probably public money.
- segmondy 9mo agoSeriously? Does Netflix or Spotify cost the same everywhere around the world? They earn less and their buying power is less.
- epolanski 9mo agoSure so do professional tools like Microsoft teams or compute in different places of the world.
- vineyardmike 9mo agoThe costs of Netflix and Spotify are licensing. Offering the subscription at half price to additional users is non-cannibalizing and a way to get more revenue from the same content. The cost of LLMs are the infrastructure. Unless someone can buy/power/run compute cheaper (Google w/ TPUs, locales with cheap electricity, etc), there won't be a meaningful difference in costs.
- storystarling 9mo agoThat assumes inference efficiency is static, which isn't really the case. Between aggressive quantization, speculative decoding, and better batching strategies, the cost per token can vary wildly on the exact same hardware. I suspect the margins right now come from architecture choices as much as raw power costs.
- arendtio 9mo ago> By scaling up model parameters and leveraging substantial computational resources So, how large is that new model?
- naji_alazhar 9mo ago[dead]
- marcd35 9mo agoWhile Qwen2.5 was pre-trained on 18 trillion tokens, Qwen3 uses nearly twice that amount, with approximately 36 trillion tokens covering 119 languages and dialects. https://qwen.ai/blog?id=qwen3 https://qwen.ai/blog?id=qwen3
- arendtio 9mo agoThanks for the info, but I don't think it answers the question. I mean, you could train a 20-node network on 36 trillion tokens. Wouldn't make much sense, but you could. So I was asking more about the number of nodes / parameters or GB of file size. In addition, there seem to be many different versions of Qwen3. E.g. here the list from ollama library: https://ollama.com/library/qwen3/tags https://ollama.com/library/qwen3/tags
- gunalx 9mo agoThis is the Max series models with unreleased weights, so probably larger than the largest released one. Also when refering to models, use huggingface or modelscope (wherever it is published) ollama is a really poor source on model info. they have some some bad naming (like confusing people on the deepseek R1 models), renaming, and more on model names, and they default to q4 quants, witch is a good sweet-spot but really degrades performance compared to the raw weigths.
- DeathArrow 9mo agoMandatory pelican on bicycle: https://www.svgviewer.dev/s/U6nJNr1Z https://www.svgviewer.dev/s/U6nJNr1Z
- kennykartman 9mo agoAh ah I was curious about that! I wonder if (when? if not already) some company is using some version of this in their training set. I'm still impressed by the fact that this benchmark has been out for so long and yet produce this kind of (ugly?) results.
- saberience 9mo agoBecause no one cares about optimizing for this because it's a stupid benchmark. It doesn't mean anything. No frontier lab is trying hard to improve the way its model produces SVG format files. I would also add, the frontier labs are spending all their post-training time on working on the shit that is actually making them money: i.e. writing code and improving tool calling. The Pelican on a bicycle thing is funny, yes, but it doesn't really translate into more revenue for AI labs so there's a reason it's not radically improving over time.
- simonw 9mo ago+1 to "it's a stupid benchmark".
- esafak 9mo agoYou can always suggest a new one ;)
- lofaszvanitt 9mo agoIt shows that these are nowhere near anything resembling human intelligence. You wouldn't have to optimize for anything if it would be a general intelligence of sorts.
- 9mo ago
- airstrike 9mo ago2026 will be the year of open and/or small models.
- acessoproibido 9mo agoWhat makes you say that? This is neither open nor small
- deleted 9mo ago[deleted]
- lysace 9mo agoI tried it at https://chat.qwen.ai/ https://chat.qwen.ai/. Prompt: "What happened on Tiananmen square in 1989?" Reply: "Oops! There was an issue connecting to Qwen3-Max. Content Security Warning: The input text data may contain inappropriate content."
- asciii 9mo agoThis is what I find hilarious when these articles assess "factual" knowledge.. We are at the realm of semantic / symbolic where even the release article needs some meta discussion. It's quite the litmus test of LLMs. LLMs just carry humanities flaws
- lysace 9mo ago(Edited, sorry.) Yes, of course LLMs are shaped by their creators. Qwen is made by Alibaba Group. They are essentially one with the CCP.
- lifetimerubyist 9mo agoWhat happens when you run one of their open-weight models of the same family locally?
- lysace 9mo agoLast time I tried something like that with an offline Qwen model I received a non-answer, no matter how hard I prompted it.
- cmrdporcupine 9mo agoThey will often try to negotiate you out of talking about it if you keep pressing. Watching their thinking about it is fascinating. It is deep deep deeply programmed around an "ethical system" which forbids it from talking about it.
- tekno45 9mo agoask who was responsible for the insurrection on january 6th
- xcodevn 9mo agoI'm not familiar with these open-source models. My bias is that they're heavily benchmaxxing and not really helpful in practice. Can someone with a lot of experience using these, as well as Claude Opus 4.5 or Codex 5.2 models, confirm whether they're actually on the same level? Or are they not that useful in practice? P.S. I realize Qwen3-Max-Thinking isn't actually an open-weight model (only accessible via API), but I'm still curious how it compares.
- miroljub 9mo agoI don't know where your impression about benchmaxxing comes from. Why would you assume closed models are not benchmaxxing? Being closed and commercial, they have more incentive to fake it than the open models.
- orangebread 9mo agoI haven't used qwen3 max yet, but my gut feeling is that they are benchmaxxing. If I were to rate the open models worth using by rank it'd be: - Minimax - GLM - Deepseek
- segmondy 9mo agoYour ranking is way off, Deepseek crushes Minimax and GLM. It's not even a competition.
- orangebread 9mo agoYeah, I get there's nuance between all of them. I ranked Minimax higher for its agentic capabilities. In my own usage, Minimax's tool calling is stronger than Deepseek's and GLM.
- deleted 9mo ago[deleted]
- segmondy 9mo agoYou are not familiar, yet you claim a bias. Bias based on what? I use pretty much just open-source models for the last 2 years. I occasionally give OpenAI and Anthropic a try to see how good they are. But I stopped supporting them when they started calling for regulation of open models. I haven't seen folks get ahead of me with closed models. I'm keeping up just fine with these free open models.
- diblasio 9mo ago[flagged]
- torginus 9mo agoMan, the Chinese government must be a bunch of saints that you must go back 35 years to dig up something heinous that they did.
- yoz-y 9mo agoTo my knowledge this model is not 35 years old.
- spankalee 9mo agoAre you actually defending the censorship of Tiananmen Square?
- j_maffe 9mo agoPerhaps they're pointing out the level of double standards in condemnation China gets compared to the US, lack of censorship notwithstanding.
- rwmj 9mo agoAre you saying we cannot talk about the bad things the US has done?
- j_maffe 9mo agoNo I'm saying we can, unlike how it is in China. Besides that point, I think GP is arguing that China is villinized more than the US.
- torginus 9mo agoI'm pretty sure if you criticise the US on something they care about, you posts will disappear from social media pretty quickly. Not because of political censorship but because of Trust and Safety violations
- torginus 9mo agoIt just occured to me that it underperforms Opus 4.5 on benchmarks when search is not enabled, but outperforms it when it is - is it possible the the Chinese internet has better quality content available? My problem with deep research tends to be that what it does is it searches the internet, and most of the stuff it turns up is the half baked garbage that gets repeated on every topic.
- exe34 9mo agomaybe they don't have Reddit?
- dsign 9mo agoHm, interesting. I use Kagi assistant with search (by Kagi), and it has a search filter that allows the model to search only academic articles. So far it has not disappointed. Of course the cynic in me thinks it's only a matter of time before there's so much AI-generated garbage even in academic articles that it will eventually become worthless. But when that turns into a serious problem, we will find some sort of solution (probably one involving tons of roller ball pens and in-person meaty handshakes).
- Aurornis 9mo ago> is it possible the the Chinese internet has better quality content available? That’s a huge leap of logic. The simpler explanation is that it has better searching functionality and performance. The models are multi-lingual and can parse results from global websites just fine.
- sciencesama 9mo agowhat ram and what minimum system req do you need to run this on personal systems !
- jen729w 9mo agoIf you have to ask, you don't have it.
- deleted 9mo ago[deleted]
- pier25 9mo agoTried it and it's super slow compared to others LLMs. I imagine the Alibaba infra is being hammered hard.
- ilaksh 9mo agoWell but it's also deliberately doing a ton of thinking right?
- ytrt54e 9mo agoI cannot even open the page; maybe I am blacklisted for asking about Tiananmen Square when their AI first hit the news?
- moffkalast 9mo agoAttention citizen! -10000 social credit
- syntaxing 9mo agoHacker News strongly believes Opus 4.5 is the defacto standard and China was consistently 8+ month behind. Curious how this performs. It’ll be a big inflection point if it performs as well as its benchmarks.
- Flavius 9mo agoBased on their own published benchmarks, it appears that this model is at least 6 months behind.
- spwa4 9mo agoStrange how things evolve. When ChatGPT started it had about 2 years headstart over Google's best proprietary model, and more than 2 years ahead to open source models. Now they have to be lucky to be 6 months ahead to an open model with at most half the parameter count, trained on 1%-2% the hardware US models are trained on.
- rbtprograms 9mo agoit seems they believed that superior models would be the moat, but when deepseek essentially replicated o1 they switched to the ecosystem as the moat.
- rglullis 9mo agoAnd more than that, the need for people/business to pay the premium for SOTA getting smaller and smaller. I thought that OpenAI was doomed the moment that Zuckerberg showed he was serious about commoditizing LLM. Even if llama wasn't the GPT killer, it showed that there was no secret formula and that OpenAI had no moat.
- NitpickLawyer 9mo ago> that OpenAI had no moat. Eh. It's at least debatable. There is a moat in compute (this was openly stated at a meeting of AI tech ceos in china, recently). And a bit of a moat in architecture and know-how (oAI gpt-oss is still best in class, and if rumours are to be believed, it was mostly trained on synthetic data, a la phi4 but with better data). And there are still moats around data (see gemini family, especially gemini3). But if you can conjure up compute, data and basic arch, you get xAI which is up there with the other 3 labs in SotA-like performance. So I'd say there are some moats, but they aren't as safe as they'd thought they'd be in 2023, for sure.
- roughly 9mo agoOne thing I’m becoming curious about with these models are the token counts to achieve these results - things like “better reasoning” and “more tool usage” aren’t “model improvements” in what I think would be understood as the colloquial sense, they’re techniques for using the model more to better steer the model, and are closer to “spend more to get more” than “get more for less.” They’re still valuable, but they operate on a different economic tradeoff than what I think we’re used to talking about in tech.
- marcd35 9mo agoi'm no expert, and i actually asked google gemini a similar question yesterday - "how much more energy is consumed by running every query through Gemini AI versus traditional search?" turns out that the AI result is actually on par, if not more efficient (power wise) than traditional search. I think it said its the equivalent power of watching 5 seconds of TV per search. I also asked perplexity to give a report of the most notable ARXIV papers. This one was at the top of the list - "The most consequential intellectual development on arXiv is Sara Hooker's "On the Slow Death of Scaling," which systematically dismantles the decade-long consensus that computational scale drives progress. Hooker demonstrates that smaller models—Llama-3 8B and Aya 23 8B—now routinely outperform models with orders of magnitude more parameters, such as Falcon 180B and BLOOM 176B. This inversion suggests that the future of AI development will be determined not by raw compute, but by algorithmic innovations: instruction finetuning, model distillation, chain-of-thought reasoning, preference training, and retrieval-augmented generation. The implications are profound—progress is no longer the exclusive domain of well-capitalized labs, and academia can meaningfully compete again."
- deleted 9mo ago[deleted]
- roughly 9mo agoI’m… deeply suspicious of Gemini’s ability to make that assessment. I do broadly agree that smaller, better tuned models are likely to be the future, if only because the economics of the large models seem somewhat suspect right now, and also the ability to run models on cheaper hardware’s likely to expand their usability and the use cases they can profitably address.
- mohsen1 9mo agoIs this available on Open Router yet? I want it to go head-to-head against Gemini 3 Flash which is the king of playing Mafia so far https://mafia-arena.com https://mafia-arena.com
- ilaksh 9mo agoI don't think so. Just checked like five minutes ago. Probably before tomorrow though.
- culi 9mo agoSee also * https://lmarena.ai/leaderboard https://lmarena.ai/leaderboard — crowd-sourced head-to-head battles between models using ELO * https://dashboard.safe.ai/ https://dashboard.safe.ai/ — CAIS' incredible dashboard (cited in OP) * https://clocks.brianmoore.com/ https://clocks.brianmoore.com/ — a visual comparison of how well models can draw a clock. A new clock is drawn every minute * https://eqbench.com/ https://eqbench.com/ — emotional intelligence benchmarks for LLMs * https://www.ocrarena.ai/battle https://www.ocrarena.ai/battle — OCR battles, ELO
- treefry 9mo agoAre they likely to take a new strategy that they no longer open source their largest and strongest models?
- pmarreck 9mo agoI asked it about "Chinese cultural dishonesty" (such as the 2019 wallet experiment, but wait for it...) and it probably had the most fascinating and subtle explanation of it I've ever read. It was clearly informed by Chinese-language sources (which in this case was good... references to Confucianism etc.) and I have to say that this is the first time I feel more enlightened about what some Westerners may perceive as a real problem. I wasn't logged in so I don't have the ability to link to the conversation but I'm exporting it for my records.
- deleted 9mo ago[deleted]
- elinear 9mo agoBenchmarks pasted here, with top scores highlighted. Overall Qwen Max is pretty competitive with the others here. Capability Benchmark GPT-5.2-Thinking Claude-Opus-4.5 Gemini 3 Pro DeepSeek V3.2 Qwen3-Max-Thinking Knowledge MMLUPro 87.4 89.5 *89.8* 85.0 85.7 Knowledge MMLURedux 95.0 95.6 *95.9* 94.5 92.8 Knowledge CEval 90.5 92.2 93.4 92.9 *93.7* STEM GPQA *92.4* 87.0 91.9 82.4 87.4 STEM HLE 35.5 30.8 *37.5* 25.1 30.2 Reasoning LiveCodeBench v6 87.7 84.8 *90.7* 80.8 85.9 Reasoning HMMT Feb 25 *99.4* - 97.5 92.5 98.0 Reasoning HMMT Nov 25 - - 93.3 90.2 *94.7* Reasoning IMOAnswerBench *86.3* 84.0 83.3 78.3 83.9 Agentic Coding SWE Verified 80.0 *80.9* 76.2 73.1 75.3 Agentic Search HLE (w/ tools) 45.5 43.2 45.8 40.8 *49.8* Instruction Following & Alignment IFBench *75.4* 58.0 70.4 60.7 70.9 Instruction Following & Alignment MultiChallenge 57.9 54.2 *64.2* 47.3 63.3 Instruction Following & Alignment ArenaHard v2 80.6 76.7 81.7 66.5 *90.2* Tool Use Tau² Bench 80.9 *85.7* 85.4 80.3 82.1 Tool Use BFCLV4 63.1 *77.5* 72.5 61.2 67.7 Tool Use Vita Bench 38.2 *56.3* 51.6 44.1 40.9 Tool Use Deep Planning *44.6* 33.9 23.3 21.6 28.7 Long Context AALCR 72.7 *74.0* 70.7 65.0 68.7
- maximgeorge 9mo ago[dead]
- Alifatisk 9mo agoCan't wait for the benchmark at artificial analysis. Qwen team doesn't seem to have updated the information about this new model yet https://chat.qwen.ai/settings/model https://chat.qwen.ai/settings/model. I tried getting an api key from alibabacloud, but the amount of steps from creating an account made me stop, it was too much. It should be this difficult. Incredible work anyways!
- jokab 9mo agoit seems to me that they dont want our money
- ndom91 9mo agoNot released on Huggingface? :sadge:
- jbverschoor 9mo ago"As of January 2026, Apple has not released an iPhone 17 series. Apple typically announces new iPhones in September each year, so the iPhone 17 series would not be available until at least September 2025 (and we're currently in January 2026). The most recent available models would be the iPhone 16 series." Hmmmm ok
- igravious 9mo agoThe title of the article is: “Pushing Qwen3-Max-Thinking Beyond its Limits”
- gcr 9mo agoIs there an open-source release accompanying this announcement or is this a proprietary model for the time being?
- dajonker 9mo agoThese LLM benchmarks are like interviews for software engineers. They get drilled on advanced algorithms for distributed computing and they ace the questions. But then it turns out that the job is to add a button the user interface and it uses new tailwind classes instead of reusing the existing ones so it is just not quite right.
- ezekiel68 9mo agoLast autumn I tried Qwen3-coder via CLI agents like trae to help add significant advanced features to a rust codebase. It consistently outperformed (at the time) Gemini 2.5 Pro and Claude Opus 3.5 with its ability to generate and re-factor code such that the system stayed coherent and improved performance and efficiency (this included adding Linux shared-memory IPC calls and using x86_64 SIMD intrinsics in rust). I was very impressed, but I racked up a big bill (for me, in the hundreds of dollars per month) because I insisted on using the Alibaba provider to get the highest context window size and token cache.
- sacha1bu 9mo agoGreat to see reasoning taken seriously — Qwen3-Max-Thinking exposing explicit reasoning steps and scoring 100% on tough benchmarks is a big deal for complex problem solving. Looking forward to seeing how this changes real-world coding and logic tasks.
- 3ds 9mo agoWhat is the tiananmen massacre? > Oops! There was an issue connecting to Qwen3-Max. > Content Security Warning: The input text data may contain inappropriate content.
- boutell 9mo agoThe most important benchmark: https://boutell.dev/misc/qwen3-max-pelican.svg https://boutell.dev/misc/qwen3-max-pelican.svg I used Simon Willison's usual prompt. It thought for over 2 minutes (free account). The commentary was even more glowing than the image. It has a certain charm.
- deepakkumarb 9mo agoI get that these approaches work, and they’re totally valid engineering trade-offs. But I don’t think they’re the same thing as real model improvements. If we’re just throwing more tokens, longer chains of thought, or extra tools at the problem, that feels more like brute force than genuine progress. And that distinction matters in practice. If getting slightly better answers means using 5–10× more tokens or a bunch of external calls, the costs add up fast. That doesn’t scale well in the real world. It’s hard to call something a breakthrough when quality goes up but the bill and latency go up just as much. I also think we should be careful about reading too much into benchmarks. A lot of them reward clever prompting and tool orchestration more than actual general intelligence. Once you factor in reliability, speed, and cost, the story often looks less impressive.
- johnjames87 9mo ago[dead]