11 ms·
Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving
- xmly 6mo agoVery impressive!
- jjice 6mo agoWith them comparing to Opus 4.5, I find it hard to take some of these in good faith. Opus 4.7 is new, so I don't expect that, but Opus 4.6 has been out for quite some time.
- Someone1234 6mo agoIf money is no object, then nothing else is worth considering if it isn't Codex 5.4/Opus 4.7/SOTA. But for many to most people, value Vs. relative quality are huge levers. Even many people on a Claude subscription aren't choosing or able to choose Opus 4.7 because of those cost/usage pressures. Often using Sonnet or an older opus, because of the value Vs. quality curve.
- wahnfrieden 6mo agoCodex subscription is very generous at pro tiers
- dd8601fn 6mo agoAlso us weirdos with local model uses. But your point stands.
- seplite 6mo agoUnfortunately, like with the release of Qwen3.6-Plus, this model also isn’t released for local use. From the linked article: “Qwen3.6-Max-Preview is the hosted proprietary model available via Alibaba Cloud Model Studio”
- CamperBob2 6mo agoCost may or may not be a factor in my choice of model, but knowing the capabilities and knowing they will remain consistent, reliable, and available over time is always a dominant consideration. Lately, Anthropic in particular has not been great at that.
- jpfromlondon 6mo agoanecdotally the quality of output isn't significantly different, the speed seems to be what you're really paying for, and since the alternative is free I'll stick to local.
- paprikanotfound 6mo agoWhat are the best models to run locally?
- jpfromlondon 6mo agoright now Gemma 4 and Qwen 3.6, I've found the latter to have the slight edge but your results may vary.
- elAhmo 6mo agoCodex 5.4 is not out?
- hirako2000 6mo agoYou compare with what's most comparable. In any case a benchmark provided by the provider is always biased, they will pick the frameworks where their model fares well. Omit the others. Independent benchmarks are the go to.
- alex_young 6mo agoQuite some time is a little over 2 months. I understand this is actually true right now, but it’s still a bit hard to accept.
- deleted 6mo ago[deleted]
- oidar 6mo agoOpus 4.6 performance has been so wildly inconsistent over the past couple of months, why waste the tokens?
- bluegatty 6mo agoI think its only been like 10 weeks. I meant that's forever in AI time, but not a long time in normie people time.
- deleted 6mo ago[deleted]
- vidarh 6mo agoWhen Sonnet 4.6 was released, I switchmed my default from Opus to Sonnet because it was about en par with Opus 4.5. While 4.6 and 4.7 are "better", the leap is too small for most tasks for me to need it, and so reducing cost is now a valid reason to stay at that level. If even cheaper models start reaching that level (GLM 5.1 is also close enough that I'm using it at lot), that's a big deal, and a totally valid reason to compare against Opus 4.5
- jasonjmcghee 6mo agoWow I couldn't disagree more. For me, Opus 4.5 and 4.6 feel so different compared to sonnet. Maybe I'm lazy or something but sonnet is much worse in my experience at inferring intent correctly if I've left any ambiguity. That effect is super compounding.
- SwellJoe 6mo agoThe thing is, Opus 4.5 is where the model reached Good Enough, at least for a wide variety of problems I use LLMs for. Before that, I almost never thought it was a more productive use of my time to use AI for development tasks, because it would always hallucinate something that would waste a bunch of my time. It just wasn't a good trade. But, if for some reason everything stopped at Opus 4.5 level and we never got a better model (and 4.6/4.7 are better, if only marginally so and mostly expanding the kind of work it can do rather than making it better at making web apps), we could still do a lot of real work real fast with Opus 4.5, and software development would never go back to everyone handwriting most of the code. A model as good as Opus 4.5 (or slightly better according to the mostly easily gamed benchmarks) at a 10th the price is probably a worthwhile proposition for a lot of people. $100 a month, or more, to get Opus 4.7 is well worth it for a western developer...the time the lower-end models waste is far more expensive than the cost of using the most expensive models. For the foreseeable future, I'll keep paying a premium for the models that waste less of my time and produce better results with less prodding. But, also, it's wild how fast things move. Open models you can run on relatively modest hardware are competitive with frontier models of two years ago. I mean, you can run Qwen 3.6 MoE 35B A3B or the larger Gemma 4 models on normal hardware, like a beefy Macbook or a Strix Halo or any recentish 24GB/32GB GPU...not much more expensive than the average developer laptop of pre-AI times. And, it can write code. It can write decent prose (Qwen is maybe better at code, Gemma definitely has better prose), they can use tools, they have a big enough context window for real work. They aren't as good as Opus 4.5, yet. Anyway, I use several models at this point, for security and code reviews, even if Claude Code with Opus is still obviously the best option for most software development tasks. I'll give Qwen a try, too. I like their small models, which punch well above their weight, I'll probably like the big one, too.
- cute_boi 6mo agoComparing it with Opus 4.6 is difficult, since Anthropic may ban accounts and accuse users of state-sponsored hacking.
- culi 6mo agoOpus 4.6 was released in February. It can take quite some time to run all these benchmarks properly
- trvz 6mo agoThe fun thing is, you can be aware of the entire range of Qwen models that are available for local running, but not at all about their cloud models. I knew of all the 3.5’s and the one 3.6, but only now heard about the Plus.
- Alifatisk 6mo agoTheir Plus series have existed since Qwen chat was available , as far as I remember. I can at least remember trying out their Plus model early last year.
- Oras 6mo agoI find it odd that none of OpenAI models was used in comparison, but used Z GLM 5.1. Is Z (GLM 5.1) really that good? It is crushing Opus 4.5 in these benchmarks, if that is true, I would have expected to read many articles on HN on how people flocked CC and Codex to use it.
- esafak 6mo agoI use it and think its intelligence compares favorably with OpenAI and Anthropic workhorses. Its biggest weakness is its speed.
- kardianos 6mo agoYes. GLM 5.1 is that good. I don't think it is as good as Claude was in January or February of this year, but it is similar to how Claude runs now, perhaps better because I feel like it's performance is more consistent.
- __blockcipher__ 6mo agoYeah GLM’s great for coding, code review, and tool use. Not amazing at other domains.
- throwaw12 6mo agomaybe they decided OpenAI has different market, hence comparing only with companies who are focusing in dev tooling: Claude, GLM
- 0xbadcafebee 6mo agoEverybody's out here chasing SOTA, meanwhile I'm getting all my coding done with MiniMax M2.5 in multiple parallel sessions for $10/month and never running into limits.
- Aurornis 6mo agoFor serious work, the difference between spending $10/month and $100/month is not even worth considering for most professional developers. There are exceptions like students and people in very low income countries, but I’m always confused by developers with in careers where six figure salaries are normal who are going cheap on tools. I find even the SOTA models to be far away from trustworthy for anything beyond throwaway tasks. Supervising a less-than-SOTA model to save $10 to $100 per month is not attractive to me in the least. I have been experimenting with self hosted models for smaller throwaway tasks a lot. It’s fun, but I’m not going to waste my time with it for the real work.
- zozbot234 6mo agoYou need to supervise the model anyway, because you want that code to be long-term maintainable and defect free, and AI is nowhere near strong enough to guarantee that anytime soon. Using the latest Opus for literally everything is just a huge waste of effort.
- dandaka 6mo agoWaste of effort... of Opus? If "Opus effort" is cheaper, than dev hours managing yourself more dumb/effective model, what is the point?
- cyanydeez 6mo agorich people dont concern themselves with the cost of tokens.
- dnnddidiej 6mo ago
- JLO64 6mo ago[dead]
- ninjahawk1 6mo agoThe way to develop in this space seems to be to give away free stuff, get your name out there, then make everything proprietary. I hope they still continue releasing open weights. The day no one releases open weights is a sad day for humanity. Normal people won’t own their own compute if that ever happens.
- testbjjl 6mo agoAny reason for them to do this other than altruism? I don’t think this can be regulated.
- Rohansi 6mo agoBake ads into them.
- visarga 6mo agoI think it is in the interest of chip makers to make sure we all get local models
- zozbot234 6mo agoDefinitely. Many big hardware firms are directly supporting HuggingFace for this very reason.
- ninjahawk1 6mo agoTrue, chip companies have the opposite mindset, Nvidia is making their own open weights I believe
- qalmakka 6mo agoI think they're in a win-win situation. Big AI companies would love to see local computing die in favour of the cloud because they are well aware the moment an open model that can run on non ludicrous consumer hardware appears, they're screwed. In this situation Nvidia, AMD and the like would be the only ones profiting from it - even though I'm not convinced they'd prefer going back to fighting for B2C while B2B Is so much simpler for them
- atilimcetin 6mo agoNowadays, I'm working on a realtime path tracer where you need proper understanding of microfacet reflection models, PDFs, (multiple) importance sampling, ReSTIR, etc.. Saying that mine is a somewhat specific use case. And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with wrong, misleading and unnecessary information but on the other hand Qwen always gave me proper, practical and correct information. I’ve almost stopped using Claude and Gemini to not to waste my time anymore. Claude code may shine developing web applications, backends and simple games but it's definitely not for me. And this is the story of my specific use case.
- zozbot234 6mo agoWhat size of Qwen is that, though? The largest sizes are admittedly difficult to run locally (though this is an issue of current capability wrt. inference engines, not just raw hardware).
- atilimcetin 6mo agoI'm directly using https://chat.qwen.ai https://chat.qwen.ai (Qwen3.6-Plus) and planning to switch to Qwen Code with subscription.
- jansan 6mo agoHow "social" does Quen feel? The way I am using LLMs for coding makes this actually the most important aspect by now. Claude 4.6 felt like a nice knowledgeable coworker who shared his thinking while solving problems. Claude 4.7 is the difficult anti-social guy who jumps ahead instead of actually answering your questions and does not like to talk to people in general. How are Qwen's social skills?
- zozbot234 6mo agoQwen feels like wise Chinese philosopher. Talks in very short elegant sentences, but does very solid work.
- DeathArrow 6mo agoI am trying since one week to subscribe Alibaba Coding Plan (to use Qwen 3.6 Plus) but it's always out of stock. They brag about Qwen but don't let people use it.
- deleted 6mo ago[deleted]
- alex7o 6mo agoOk I find it funny that people compare models and are like, opus 4.7 is SOTA and is much better etc, but I have used glm 5.1 (I assume this comes form them training on both opus and codex) for things opus couldn't do and have seen it make better code, haven't tried the qwen max series but I have seen the local 122b model do smarter more correct things based on docs than opus so yes benchmarks are one thing but reality is what the modes actually do and you should learn and have the knowledge of the real strengths that models posses. It is a tool in the end you shouldn't be saying a hammer is better then a wrench even tho both would be able to drive a nail in a piece of wood.
- solomatov 6mo ago>but I have seen the local 122b model do smarter more correct things based on docs than opus Could you please share more about this
- alex7o 6mo agoMaybe a bit misleading. I have used in in two places. One Is for local opencode coding and config of stuff the other is for agent-browser use and for both it did better (opus 4.6) for the thing I was testing atm. The problem with opus at the moment I tired it was overthinking and moving itself sometimes I the wrong direction (not that qwen does overthink sometimes). However sometimes less is more - maybe turning thinking down on opus would have helped me. Some people said that it is better to turn it of entirely when you start to impmenent code as it already knows what it needs to do it doesn't need more distraction. Another example is my ghostty config I learned from queen that is has theme support - opus would always just make the theme in the main file
- OtomotO 6mo agoMany people averted religion (which I can get behind with), but have never removed the dogmatic thinking that lay at its root. As so many things these days: It's a cult. I've used Claude for many months now. Since February I see a stark decline in the work I do with it. I've also tried to use it for GPU programming where it absolutely sucks at, with Sonnet, Opus 4.5 and 4.6 But if you share that sentiment, it's always a "You're just holding it wrong" or "The next model will surely solve this" For me it's just a tool, so I shrug.
- wg0 6mo agoNotice the pattern that Chinese providers are now: 1. Keeping models closed source. 2. Jacking up pricing. A lot. Sometimes up to 100% increase.
- embedding-shape 6mo agoHuh yeah, that's truly a unique trait these Chinese companies don't share with companies in other countries.
- aerhardt 6mo agoNo it is not, but they had a unique positioning around open-source and the parent commenter means that they are losing it.
- esperent 6mo agoAgain, a trait they share with companies in other countries. It's the obvious business model: get known by releasing impressive open models, then pivot to closed for even more impressive models. That's going to be the path for every new company from every country, I assume. They are not releasing open models out of the goodness of their hearts. They are for-profit companies, they don't have hearts, they just have balance sheets.
- cute_boi 6mo agoWell, they can't subsidize forever. And, it is kinda expected?
- gpm 6mo agoConsidering the propaganda value in controlling the inputs to the machine that answers peoples questions, I rather expect them to be subsidized forever.
- bigyabai 6mo agoConsider the propaganda value of a centrally-controlled apparatus like the iPhone, and then reflect on the 100%+ profit margins that product has enjoyed for the past decade.
- marsulta 6mo agoI think the benchmarks and numbers need to be easier to read. Those benchmarks are useless to the regular consumer.
- jdw64 6mo agohttps://www.alibabacloud.com/help/en/model-studio/context-cache https://www.alibabacloud.com/help/en/model-studio/context-ca... I’ve also been testing models like Opus, Codex, and Qwen, and Qwen is strong in many coding tasks. However, my main concern is how it behaves in long-running sessions. While Qwen advertises large context windows, in practice the effectiveness of long-context usage seems to depend heavily on its context caching behavior. According to the official documentation, Qwen provides both implicit and explicit context caching, but these come with constraints such as short TTL (around a few minutes), prefix-based matching, and minimum token thresholds. Because of these constraints, especially in workflows like coding agents where context grows over time, cache reuse may not scale as effectively as expected. As a result, even though the per-token price looks low, the effective cost in long sessions can feel higher due to reduced cache hit rates and repeated computation. That said, in certain areas such as security-related tasks, I’ve personally had cases where Qwen performed better than Opus. In my personal experience, Qwen tends to perform much better than Opus on shorter units like individual methods or functions. However, when looking at the overall coding experience, I found it works better as a function-level generator rather than as an autonomous, end-to-end coding assistant like Claude.
- ezekiel68 6mo agoTBF, it's certainly best practice, advised by the model providers themselves, to cut sessions short and start new ones. Anthropic's "Best Practices" doc[0] for Claude Code states, "A clean session with a better prompt almost always outperforms a long session with accumulated corrections." [0] https://code.claude.com/docs/en/best-practices https://code.claude.com/docs/en/best-practices
- hedora 6mo agoUnless stuff changed since I last checked, context caching just reduces cost / latency. It does not change what tokens are emitted.
- chatmasta 6mo agoIs this going to be an open weights model or not? The post doesn’t make it clear. It seems the weights are not available today, but maybe that’s because it’s in preview?
- zozbot234 6mo agoThe Max series has never been open.
- XCSme 6mo agoA bit weird to be comparing it to Opus-4.5 when 4.7 was released...
- souravroyetl 6mo ago[flagged]
- mockbolt 6mo ago[dead]
- o10449366 6mo agoI have the M3 Max MBP with 128 GB of memory and the 40 core GPU. What's the best local model I can run today for coding?
- alx-ppv 6mo agoYou can try https://github.com/AlexsJones/llmfit https://github.com/AlexsJones/llmfit
- dakolli 6mo agoToKeN PrIcEs ArE gOiNg tO PluMmEt, InTelLigEnCe WiLl Be AfForDaBlE FoR EvErYOnE
- seanw265 6mo agoKimi K2.6 also released today. I think it's fair to compare the two models. Qwen appears to be much more expensive: - Qwen: $1.3 in / $7.8 out - Kimi: $0.95 in / $4 out -- The announcement posts only share two overlapping benchmark results. Qwen appears to score slightly lower on SWE-Bench Pro and Terminal-Bench 2.0. Qwen: - Teminal-Bench 2.0: 65.4 - SWE-Bench Pro: 57.3 Kimi: - Terminal-Bench 2.0: 66.8 - SWE-Bench Pro: 58.6 -- Different models have different strong suits, and benchmarks don't cover everything. But from a numbers perspective, Kimi looks much more appealing.
- mchusma 6mo agoi think as the pricing has gone up on the Chinese models it has made them less appealing, and with the introduction of Gemma-4 not many are at the pareto frontier (also in my experience, not just the stats): https://arena.ai/leaderboard/text/overall?viewBy=plot https://arena.ai/leaderboard/text/overall?viewBy=plot
- chrisss395 6mo agoFWIW in my recent testing I couldn't find a better model than Gemma 4 31B for the price (openrouter only). My use case was taking discussions and identifying business ideas, so somewhat conceptual problem solving type thing.
- archon810 6mo agoI wonder if this means a better Cursor Composer model update is coming, since it builds on top of Kimi K2.
- 2001zhaozhao 6mo agoCursor would have to run their RL pipeline all over again if they wanted to build a new Composer on K2.6, so almost definitely not.
- archon810 6mo ago
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- Aeroi 6mo agowhy do people continue to benchmark their sota models against older models.
- djyde 6mo agoI've been using glm5.1 for pretty much all my coding work, but Claude is too expensive for me. Haven't tried qwen yet though. China's coding models are now very cost-effective.
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- djyde 6mo agoBut I've recently found that Cursor's composer2 is also really good to use.
- freely0085 6mo agoComposer 2 is just Kimi 2.5, it's not their own model.
- piotraleksander 6mo agoit's such a misinformed statement, as kimi2.5 was used as a base model for composer 2 and then heavily RLed
- syntaxing 6mo agoWhat does heavy RL even mean…similar to how the CEO of cursor said how much better the perplexity got when it’s a terrible metric for model fine tune performance? Let’s be real here, it’s Kimi 2.5 fine tuned for Cursor. There’s nothing wrong with that but they tried to hide it and it’s some work they put in but nothing close to training a model of their own.
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- greyskull 6mo agoI've been using Claude Code regularly at work for several months, and I successfully used it for a small personal project (a website) not long ago. Last weekend, I explored self-hosting for the first time. Does anyone have a similar experience of having thoroughly used CC/Codex/whatever and also have an analogous self-hosted setup that they're somewhat happy with? I'm struggling a bit. I have 32GB of DDR5 (seems inadequate nowadays), an AMD 7800X3D, and an RTX 4090. I'm using Windows but I have WSL enabled. I tried a few combinations of ollama, docker desktop model runner, pi-coding-agent and opencode; and for models, I think I tried a few variants each of Gemma 4, Qwen, GLM-5.1. My "baseline" RAM usage was so high from the handful of regular applications that IIRC it wasn't enough to use the best models; e.g., I couldn't run Gemma4-31B. Things work okay in a Windows-only setup, though the agent struggled to get file paths correct. I did have some success running pi/opencode in WSL and running ollama and the model via docker desktop. In terms of actual performance, it was painfully slow compared to the throughput I'm used to from CC, and the tooling didn't feel as good as the CC harness. Admittedly I didn't spend long enough actually using it after fiddling with setup for so long, it was at least a fun experiment.
- madtowneast 6mo agoYou are experiencing the fact that you might not have enough VRAM to load the entire model at a time. You might want to try https://github.com/AlexsJones/llmfit https://github.com/AlexsJones/llmfit
- greyskull 6mo agoIt's certainly part of the problem. Thanks, I'll give that a shot.
- martinald 6mo agoTry using a MoE model (like Gemma 4 26b-a4b or qwen3.6 35b-a3b) and offload the inference to CPU. If you have enough system RAM (32GB is a bit tight tbh depending on other apps) then this works really well. You may be able to offload some layers to GPU as well though I've had issues with this in MoE models and llama.cpp. You can keep the KV cache on GPU which means it's pretty damn fast and you should be able to hold a reasonable context window size (on your GPU). I've had really impressive results locally with this. I'd strongly recommend cloning llama.cpp locally btw (in wsl2) and asking a frontier model in eg Claude code to set it up for you and tweak it. In my experience the apps that sit on top of llama.cpp don't expose all the options and flags and one wrong flag can mean terrible performance (eg context windows not being cached). If you compile it from source with a coding agent it can look up the actual code when things go wrong. You should be able to get at least 20-40tok/s on that machine on Gemma 4 which is very usable, probabaly faster on qwen3.6 since it's only 3b active params.
- digimantis 6mo agoi dont get why people defend $200/month models against open source model that cost 1/10 of the price, like literally
- EthanFrostHI 6mo ago[dead]
- alanmercer 6mo ago[dead]
- fragmede 6mo agoThis thing on Celebras is going to be ridiculous.
- bauratynov 6mo ago[dead]
- johnnyApplePRNG 6mo agoNowhere near the power of ChatGPT 5.4 Pro imho... thought for maybe 15 seconds on a problem that pro would have spend 15 minutes on... and the results really show :/
- edg5000 6mo agoIs a community LLM possible? We'd have code to dynamically construct the pre-training dataset and use P2P mechanisms to share the acquired dataset. It would involve peer-crawling and other mechanisms to allow many people to contribute chunks to the dataset. Crawling chunks would be dynamically allocated to those contributing to avoid any double-crawling. For post-training, the dataset would be a bunch of code that orchestrates the creation of training data via LLMs (needs to be legally sound), plus some kind of mechanical turk approach (something like wikipedia, where volunteers can work on chunks of data). The main mechanism is this: what is shared is not just code, but also the acquired training data. Critical aspects: - to have a mechanism to peer-validate submissions to the data pool, so that everybody can donate data without the risk of vandalism - a mechanism where the weights go through distributed training stages; somehow devs should be able to get a "lock" on the weights, do a bit of post training on it, and then get it approved. The "lock" means that during this brief period (trainining run), other devs are informed so we don't get two set of branched weights. A mechanism auto-evals the weights and accepts them as the new, updated weights. Retroactive discarding of weighs (e.g. after revising evals) is possible by branching the weights (needs some kind of efficient deduplication to avoid many copies of the weights). I think this is possible. Maybe not with RAM, GPU and power shortages though. Main benefit: Trannsparent training set means you know what the model was trained for. This makes it less opaque and less trial-and-error to see what modality the model is good at. This helps harness builders but also any other users of the models. It also decentralizes power.
- goodmythical 6mo agoIt is possible and already being worked on at [1] though I have no idea how well any of its working. [1] https://bittensor.com/about https://bittensor.com/about
- edg5000 6mo agoVery interesting, thanks for sharing EDIT: It's completely different though. This is more of a commodities market/auction/inference broker mechanism it seems.