Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

https://qwen.ai/blog?id=qwen3.6-max-preview

by mfiguiere • 3 months ago

705 377 3 months ago

jjice• 3 months ago

With 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• 3 months ago

If 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• 3 months ago

Codex subscription is very generous at pro tiers

dd8601fn2• 3 months ago

Also us weirdos with local model uses. But your point stands.

CamperBob23• 3 months ago

Cost 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.

jpfromlondon2• 3 months ago

anecdotally 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.

elAhmo• 3 months ago

Codex 5.4 is not out?

hirako2000• 3 months ago

You 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• 3 months ago

Quite 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.

oidar• 3 months ago

Opus 4.6 performance has been so wildly inconsistent over the past couple of months, why waste the tokens?

bluegatty• 3 months ago

I think its only been like 10 weeks. I meant that's forever in AI time, but not a long time in normie people time.

vidarh4• 3 months ago

When 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

jasonjmcghee2• 3 months ago

Wow 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• 3 months ago

The 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_boi2• 3 months ago

Comparing it with Opus 4.6 is difficult, since Anthropic may ban accounts and accuse users of state-sponsored hacking.

culi5• 3 months ago

Opus 4.6 was released in February. It can take quite some time to run all these benchmarks properly

trvz• 3 months ago

The 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.

Alifatisk4• 3 months ago

Their 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.

Oras3• 3 months ago

I 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• 3 months ago

I use it and think its intelligence compares favorably with OpenAI and Anthropic workhorses. Its biggest weakness is its speed.

kardianos• 3 months ago

Yes. 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__• 3 months ago

Yeah GLM’s great for coding, code review, and tool use. Not amazing at other domains.

throwaw123• 3 months ago

maybe they decided OpenAI has different market, hence comparing only with companies who are focusing in dev tooling: Claude, GLM

edwinjm• 3 months ago

Haven’t you heard about Codex?

ac29• 3 months ago

GLM 5.1 is pretty good, probably the best non-US agentic coding model currently available. But both GLM 5.0 and 5.1 have had issues with availability and performance that makes them frustrating to use. Recently GLM 5.1 was also outputting garbage thinking traces for me, but that appears to be fixed now.

cmrdporcupine2• 3 months ago

Use them via DeepInfra instead of z.ai. No reliability issues.

https://deepinfra.com/zai-org/GLM-5.1

Looks like fp4 quantization now though? Last week was showing fp8. Hm..

pros• 3 months ago

I'm using GLM 5.1 for the last two weeks as a cheaper alternative to Sonnet, and it's great - probably somewhere between Sonnet and Opus. It's pretty slow though.

bensyverson7• 3 months ago

This is what kills it for me… The long thinking blocks can make a simple task take 30 minutes.

c0n5pir4cy• 3 months ago

I've been using it through OpenCode Go and it does seem decent in my limited experience. I haven't done anything which I could directly compare to Opus yet though.

I did give it one task which was more complex and I was quite impressed by. I had a local setup with Tiltdev, K3S and a pnpm monorepo which was failing to run the web application dev server; GLM correctly figured out that it was a container image build cache issue after inspecting the containers etc and corrected the Tiltfile and build setup.

coder68• 3 months ago

In fact it is appreciated that Qwen is comparing to a peer. I myself and several eng I know are trying GLM. It's legit. Definitely not the same as Codex or Opus, but cheaper and "good enough". I basically ask GLM to solve a program, walk away 10-15 minutes, and the problem is solved.

Oras3• 3 months ago

cheaper is quite subjective, I just went to their pricing page [0] and cost saving compared to performance does not sell it well (again, personal opinion).

CC has a limited capacity for Opus, but fairly good for Sonnet. For Codex, never had issues about hitting my limits and I'm only a pro user.

https://z.ai/subscribe

Alifatisk4• 3 months ago

GLM-5 is good, like really good. Especially if you take pricing into consideration. I paid 7$ for 3 months. And I get more usage than CC.

They have difficulty supplying their users with capacity, but in an email they pointed out that they are aware of it. During peak hours, I experience degraded performance. But I am on their lowest tier subscription, so I understand if my demand is not prioritized during those hours.

ekuck• 3 months ago

Where are you getting 3 months for $7?

cleaning• 3 months ago

Most HN commenters seem to be a step behind the latest developments, and sometimes miss them entirely (Kimi K2.5 is one example). Not surprising as most people don't want to put in the effort to sift through the bullshit on Twitter to figure out the latest opinions. Many people here will still prefer the output of Opus 4.5/4.6/4.7, nowadays this mostly comes down to the aesthetic choices Anthropic has made.

Oras3• 3 months ago

Not just aesthetics though, from time to time I implement the same feature with CC and Codex just to compare results, and I yet to find Codex making better decisions or even the completeness of the feature.

For more complicated stuff, like queries or data comparison, Codex seems always behind for me.

vidarh4• 3 months ago

GLM 5.1 is the first model I've found good enough to spring for a subscription for other than Claude and Codex.

It's not crushing Opus 4.5 in real-life use for me, but it's close enough to be near interchangeable with Sonnet for me for a lot of tasks, though some of the "savings" are eaten up by seemingly using more tokens for similar complexity tasks (I don't have enough data yet, but I've pushed ~500m tokens through it so far.

culi5• 3 months ago

If you only look at open models, GLM 5.1 is the best performance you can get on on the Pareto distribution

https://arena.ai/leaderboard/text?viewBy=plot&license=open-s...

0xbadcafebee5• 3 months ago

Everybody'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• 3 months ago

For 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.

zozbot23413• 3 months ago

You 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.

AnonymousPlanet2• 3 months ago

For actually serious work, it's a stark difference if your proprietary and security relevant code is sent abroad to a foreign, possibly future hostile country, or is sent to some data center around the corner. It doesn't even need to be defence related.

slopinthebag6• 3 months ago

$100 / month will get you rate limited to much to rely on with the Claude plans. People still report getting rate limited on the $200 / plan.

Also not everyone wants to use Claude Code, so if they're paying API pricing it's more likely thousands of dollars a month. If you can get the same results by spending a fraction of that, why wouldn't you?

0xbadcafebee5• 3 months ago

You don't magically get better results by spending 10x more on a model. If your prompt is crap and harness is crap, you get crap results, regardless of model. And if you run into limits, you aren't working at all.

Buying the most expensive circular saw doesn't get you the best woodworking, but it is the most expensive woodworking.

columk• 3 months ago

But $10/mo this year gets you the same quality and throughput as $200/m did last year. Most of us prefer to spend that $190 on our friends and family each month. It's like buying a new iPhone every year - not necessary unless you absolutely need the latest features. If your employer wants you to use a SOTA model they can pay for it.

chatmasta3• 3 months ago

Who are you paying $10/month? OpenRouter?

tgrowazay• 3 months ago

https://platform.minimax.io/docs/guides/pricing-token-plan

0xbadcafebee5• 3 months ago

OpenCode Go, BlackBox, Chutes. https://codeberg.org/mutablecc/calculate-ai-cost/src/branch/...

xutopia• 3 months ago

How do you use this? Do you use opencode or another frontend?

0xbadcafebee5• 3 months ago

yep, OpenCode with a few plugins (context management, memory, a few MCPs)

ninjahawk12• 3 months ago

The 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.

visarga• 3 months ago

I think it is in the interest of chip makers to make sure we all get local models

zozbot23413• 3 months ago

Definitely. Many big hardware firms are directly supporting HuggingFace for this very reason.

ninjahawk12• 3 months ago

True, chip companies have the opposite mindset, Nvidia is making their own open weights I believe

qalmakka3• 3 months ago

I 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

testbjjl• 3 months ago

Any reason for them to do this other than altruism? I don’t think this can be regulated.

Rohansi• 3 months ago

Bake ads into them.

baq• 3 months ago

Always has been, it’s literally saas; the slight difference is that the lowest tier subscriptions at the frontier labs are basically free trials nowadays, too

CamperBob23• 3 months ago

I'm a little more optimistic than that. I suspect that the open-weight models we already have are going to be enough to support incremental development of new ones, using reasonably-accessible levels of compute.

The idea that every new foundation model needs to be pretrained from scratch, using warehouses of GPUs to crunch the same 50 terabytes of data from the same original dumps of Common Crawl and various Russian pirate sites, is hard to justify on an intuitive basis. I think the hard work has already been done. We just don't know how to leverage it properly yet.

thesz3• 3 months ago

Change layer size and you have to retrain. Change number of layers and you have to retrain. Change tokenization and you have to retrain.

pduggishetti• 3 months ago

I do not think it's common crawl anymore, its common crawl++ using paid human experts to generate and verify new content, weather its code or research.

I believe US is building this off the cost difference from other countries using companies like scale, outlier etc, while china has the internal population to do this

WarmWash3• 3 months ago

The Chinese state wants the world using their models.

People think that Chinese AI labs are just super cool bros that love sharing for free.

The don't understand it's just a state sponsored venture meant to further entrench China in global supply and logistics. China's VCs are Chinese banks and a sprinkle of "private" money. Private in quotes because technically it still belongs to the state anyway.

China doesn't have companies and government like the US. It just has government, and a thin veil of "company" that readily fool westerners.

grttsww2• 3 months ago

So what?

I still prefer that over US total dominance.

Let them fight it out.

darkwater• 3 months ago

Well, isn't this what the US and really any other power in the world has always done, since forever?

zozbot23413• 3 months ago

I'm not sure how local AI models are meant to "entrench China in global supply and logistics". The two areas have nothing to do with one another. You can easily run a Chinese open model on all-American hardware.

subw00f• 3 months ago

As opposed to the US, which just has companies and a thin veil of “government”.

jillesvangurp• 3 months ago

Like with nuclear technology, it's not healthy for only one country to dominate AI. The cat is already out of the bag and many countries now have the ability to train and run models. Silicon Valley has bootstrapped this space. But it should be noted that they are using AI talent from all over the world and it was sort of inevitable that this technology would get around. Lots of Chinese, Indian, Russian, and Europeans are involved.

As for what comes next, it's probably going to be a bit of a race for who can do the most useful and valuable things the cheapest. If OpenAI and Anthropic don't make it, the technology will survive them. If they do, they'll be competing on quality and cost.

As for state sponsorship, a lot of things are state sponsored. Including in the US. Silicon Valley has a rich history that is rooted in massive government funding programs. There's a great documentary out there the secret history of Silicon Valley on this. Not to mention all the "cheap" gas that is currently powering data centers of course comes on the back of a long history of public funding being channeled into the oil and gas industry.

OtomotO4• 3 months ago

So an OPEN model that I can run on my own fucking hardware will entrench China in global supply and logistics how?

Contrary: How will the closed, proprietary models from Anthropic, "Open"AI and Co. lead us all to freedom? Freedom of what exactly? Freedom of my money?

At some point this "anti-communism" bullshit propaganda has to stop. And that moment was decades ago!

devilsdata2• 3 months ago

I'm Aussie. Please explain to me; why should I care whether Chinese SOEs or the US tech companies are winning? Neither have my best interests at heart.

Zavora• 3 months ago

Its the new freeware model!

elorant2• 3 months ago

This is obviously a strategic move at a national level. Keep publishing competing free models to erode the moat western companies could have with their proprietary models. As long as the narrative serves China there will be no turn to proprietary models.

Barrin92• 3 months ago

This is obviously a strategic move at a national level.

no it isn't. That's the kind of thing people say who've never worked in the Chinese software ecosystem. It's how the Chinese internet has worked for 20+ years. The Chinese market is so large and competition is so rabid that every company basically throws as much free stuff at consumers as they can to gain users. Entrepreneurs don't think about "grand strategic moves at the national level" while they flip through their copies of the Art of War and Confucius lol

culi5• 3 months ago

I think that's an overgeneralization. We've seen all the American models be closed and proprietary from the start. Meanwhile the non-American (especially the Chinese ones) have been open since the start. In fact they often go the opposite direction. Many Chinese models started off proprietary and then were later opened up (like many of the larger Qwen models)

embedding-shape4• 3 months ago

We've seen all the American models be closed and proprietary from the start.

Most*.

OpenAI, contrary to popular belief, actually used to believe in open research and (more or less) open models. GPT1 and GPT2 both were model+code releases (although GPT2 was a "staged" release), GPT3 ended up API-only.

robot_jesus• 3 months ago

We've seen all the American models be closed and proprietary from the start

What about Gemma and Llama and gpt-oss, not to mention lots of smaller/specialized models from Nvidia and others?

I would never argue that China isn't ahead in the open weights game, of course, but it's not like it's "all" American models by any stretch.

3836293648• 3 months ago

GPT started off open? They just closed before anyone else even joined the space

try-working2• 3 months ago

Exactly. Open source is a commercial strategy for Chinese labs. They have no other effective way of marketing their models and inference services: https://try.works/writing-1#why-chinese-ai-labs-went-open-an...

stingraycharles• 3 months ago

That has been a viable commercial strategy for most modern, funded businesses. Capture market share at a loss, then once name is established turn on the profit.

atilimcetin2• 3 months ago

Nowadays, 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.

zozbot23413• 3 months ago

What 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).

atilimcetin2• 3 months ago

I'm directly using https://chat.qwen.ai (Qwen3.6-Plus) and planning to switch to Qwen Code with subscription.

jansan• 3 months ago

How "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?

zozbot23413• 3 months ago

Qwen feels like wise Chinese philosopher. Talks in very short elegant sentences, but does very solid work.

jasonjmcghee2• 3 months ago

You may be interested in "radiance cascades"

wg03• 3 months ago

I have said similar things about someone experiencing similar things while writing some OpenGL code (some raytracing etc) that these models have very little understanding and aren't good at anything beyond basic CRUD web apps.

In my own experience, even with web app of medium scale (think Odoo kind of ERP), they are next to useless in understanding and modling domain correctly with very detailed written specs fed in (whole directory with index.md and sub sections and more detailed sections/chapters in separate markdown files with pointers in index.md) and I am not talking open weight models here - I am talking SOTA Claude Opus 4.6 and Gemini 3.1 Pro etc.

But that narrative isn't popular. I see the parallels here with the Crypto and NFT era. That was surely the future and at least my firm pays me in cypto whereas NFTs are used for rewarding bonusess.

wg03• 3 months ago

Someone exactly said it better here[0] already.

[0]. https://news.ycombinator.com/item?id=47817982

amarcheschi• 3 months ago

a semester ago i was taking a machine learning exam in uni and the exam tasked us with creating a neural network using only numerical libraries (no pytorch ecc). I'm sure that there are a huge lot of examples looking all the same, but given that we were just students without a lot of prior experience we probably deviated from what it had in its training data, with more naive or weird solutions. Asking gemini 3 to refactor things or in very narrow things to help was ok, but it was quite bad at getting the general context, and spotting bugs, so much that a few times it was easier to grab the book and get the original formula right

otoh, we spotted a wrong formula regarding learning rate on wikipedia and it is now correct :) without gemini and just our intuition of "mhh this formula doesn't seem right", that definitely inflated our ego

esperent3• 3 months ago

To be fair, I've had the extreme misfortune of working on Odoo code and I can understand why an LLM would struggle.

Yearly breaking changes but impossible to know what version any example code you find is related to (except that if you're on the latest version, it's definitely not for your version), closed and locked down forum (after several months of being a paying customer, I couldn't even post a reply, let alone ask a question), weird split between open and closed, weird OWL frontend framework that seems to be a bad clone of an old React version, etc. etc. Painful all around. I would call this kind of codebase pre-LLM slop, accreted over many years of bad engineering decisions.

muyuu• 3 months ago

for Anthropic and OpenAI there is a very real danger that people invest serious time finding the strengths of alternative models, esp Chinese/open models that can to some degree be run locally as well

it puts a massive backstop at the margins they can possibly extract from users

hedora3• 3 months ago

What do you use instead of the Claude code client app?

DeathArrow3• 3 months ago

I 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.

alex7o2• 3 months ago

Ok 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.

OtomotO4• 3 months ago

Many 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.

taurath• 3 months ago

I agree - the problem is it’s hard to see how people who say they’re using it effectively actually are using it, what they’re outputting, and making any sort of comparison on quality or maintainability or coherence.

In the same way, it’s hard to see how people who say they’re struggling are actually using it.

There’s truth somewhere in between “it’s the answer to everything” and “skill issue”. We know it’s overhyped. We know that it’s still useful to some extent, in many domains.

psychoslave• 3 months ago

What is it that is dogma free? If one goes hardcore pyrrhonism, doubting that there is anything currently doubting as this statement is processed somehow, that is perfectly sound.

At some point the is a need to have faith in some stable enough ground to be able to walk onto.

ecshafer2• 3 months ago

All people think dogmatically. The only difference is what the ontological commitments and methaphysical foundations are. Take out God and people will fit politics, sports teams, tools, whatever in there. Its inescapable.

balls1872• 3 months ago

I've used Claude for many months now. Since February I see a stark decline in the work I do with it.

I find myself repeating the following pattern: I use an AI model to assist me with work, and after some time, I notice the quality doesn't justify the time investment. I decide to try a similar task with another provider. I try a few more tests, then decide to switch over for full time work, and it feels like it's awesome and doing a good job. A few months later, it feels like the model got worse.

taneq2• 3 months ago

I wonder to what degree it depends on how easy you find coding in general. I find for the early steps genAI is great to get the ball rolling, but rapidly it becomes more work to explain what it did wrong and how to fix it (and repeat until it does so) than to just fix the code myself.

cornedor2• 3 months ago

I tried GLM and Qwen last week for a day. And some issues it could solve, while some, on surface relatively easy, task it just could not solve after a few tries, that Opus oneshotted this morning with the same prompt. It’s a single example ofcourse, but I really wanted to give it a fair try. All it had to do was create a sortable list in Magento admin. But on the other hand, GLM did oneshot a phpstorm plugin

dev_l1x_be2• 3 months ago

Do you use Opus through the API or with subscription? Did you use OpenCode or Code?

ternaryoperator• 3 months ago

The models test roughly equal on benchmarks, with generally small differences in their scores. So, it’s reasonable to choose the model based on other criteria. In my case, I’d switch to any vendor that had a decent plugin for JetBrains.

FlyingSnake2• 3 months ago

I tried GLM5.1 last week after reading about it here. It was slow as molasses for routine tasks and I had to switch back to Claude. It also ran out of 5H credit limit faster than Claude.

bensyverson7• 3 months ago

If you view the "thinking" traces you can see why; it will go back and forth on potential solutions, writing full implementations in the thinking block then debating them, constantly circling back to points it raised earlier, and starting every other paragraph with "Actually…" or "But wait!"

nothinkjustai2• 3 months ago

Z.ai’s cloud offering is poor, try it with a different provider.

Moosdijk• 3 months ago

I wonder why glm is viewed so positively.

Every time I try to build something with it, the output is worse than other models I use (Gemini, Claude), it takes longer to reach an answer and plenty of times it gets stuck in a loop.

Akira1364• 3 months ago

IDK about GLM but GPT 5.4 Extra High has been great when I've used it in the VS Code Copilot extension, I see no actual reason Opus should consume 3x more quota than it the way it does

Mashimo3• 3 months ago

I have used GLM 4.7, 5 and 5.1 now for about 3 month via OpenCode harness and I don't remember it every being stuck in a loop.

You have to keep it below ~100 000 token, else it gets funny in the head.

I only use it for hobby projects though. Paid 3 EUR per month, that is not longer available though :( Not sure what I will choose end of month. Maybe OpenCode Go.

pkulak• 3 months ago

I've been running Opus and GLM side-by side for a couple weeks now, and I've been impressed with GLM. I will absolutely agree that it's slow, but if you let it cook, it can be really impressive and absolutely on the level of Opus. Keep in mind, I don't really use AI to build entire services, I'm mostly using it to make small changes or help me find bugs, so the slowness doesn't bother me. Maybe if I set it to make a whole web app and it took 2 days, that would be different.

The big kicker for GLM for me is I can use it in Pi, or whatever harness I like. Even if it was _slightly_ below Opus, and even though it's slower, I prefer it. Maybe Mythos will change everything, but who knows.

slopinthebag6• 3 months ago

You're probably just holding it wrong.

spaceman_2020• 3 months ago

I think it offers a very good tradeoff of cost vs competency

4.7 is better, but its also wildly expensive

chillfox4• 3 months ago

GLM is the first open source model that actually worked for me, where I found the output ok.

And yes, sonnet/opus is better and what I use daily. But I wouldn’t be that upset if I had to drop down to GLM.

dev_l1x_be2• 3 months ago

Benchmarking is grossly misleading. Claude’s subscription with Code would not score this high on the benchmarks because how they lobotomized agentic coding.

jxmesth4• 3 months ago

The only reason I'm stuck with Claude and Chatgpt is because of their tool calling. They do have some pretty useful features like skills etc. I've tried using qwen and deepseek but they can't even output documents. How are you guys handling documents and excels with these tools? I'd love to switch tbh.

ecocentrik2• 3 months ago

When was the last time you used Qwen models? Their 3.5 and 3.6 models are excellent with tool calling.

jwitthuhn• 3 months ago

I've been using qwen-code (the software, not to be confused with Qwen Code the service or Qwen Coder the model) which is a fork of gemini-cli and the tool use with Qwen models at least has been great.

embedding-shape4• 3 months ago

I've tried using qwen and deepseek but they can't even output documents

What agent harness did you use? Usually, "write_file", "shell_exec" or similar is two of the first tools you add to an agent harness, after read_file/list_files. If it doesn't have those tools, unsure if you could even call it a agent harness in the first place.

sscaryterry• 3 months ago

You can use GLM-5.1 with claude code directly, I use ccs, GLM-5.1 setup as plan, but goes via API key.

estimator7292• 3 months ago

You can use both codex and Claude CLI with local models. I used codex with Gemma4 and it did pretty well. I did get one weird session where the model got confused and couldn't decide which tools actually existed in its inventory, but usually it could use tools just fine.

NobleLie• 3 months ago

Yep Claude Code CLI does A LOT (which is now confirmed even more)

ycui19864• 3 months ago

qwen3.5 and qwen3.6 are both good at tool calling.

zrn900• 3 months ago

You can just use Cline in VSCode to get most of the tooling you need - it works with all models. Including Xiaomi's new Mimo with 1m context window and blazing fast speed. It's much cheaper than Claude's biggest plan and with much, much more quota.

solomatov• 3 months 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

alex7o2• 3 months ago

Maybe 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

ezekiel682• 3 months ago

Qwen3-Coder produced much better rust code (that utilized rust's x86-64 vectorized extensions) a few months ago than Claude Opus or Google Gemini could. I was calling it from harnesses such as the Zed editor and trae CLI.

I was very impressed.

justincormack• 3 months ago

Codex is pretty good at Rust with x86 and arm intrinsics too, it replaced a bunch of hand written C/assembly code I was using. I will try Qwen and Kimi on this kind of task too.

gck13• 3 months ago

I think claude in general, writes very lazy, poor quality code, but it writes code that works in fewer iterations. This could be one of the reasons behind it's popularity - it pushes towards the end faster at all costs.

Every time codex reviews claude written rust, I can't explain it, but it almost feels like codex wants to scream at whoever wrote it.

lambda• 3 months ago

Their latest, Qwen3.6 35B-A3B is quite capable, and fast and small enough I don't really feel constrained running it locally. Some of the others that I've run that seem reasonably good, like Gemma 4 31B and Qwen3.5 122B-A10B just feel a bit too slow, or OOM my system too often, or run up on cache limits so spend a lot of time re-processing history. But the latest Qwen3.6 is both quite strong, and lightweight enough that it feels usable on consumer hardware.

sirnicolaz• 3 months ago

Consider that SWE benchmarking is mainly done with python code. It tells something

odie5533• 3 months ago

If you showed me code from GLM 5.1, Opus 4.6, and Kimi K2.6, my ranking for best model would be highly random.

mikenew2• 3 months ago

GLM 5.1 was the model that made me feel like the Chinese models had truly caught up. I cancelled my Claude Max subscription and genuinely have not missed it at all.

Some people seem to agree and some don't, but I think that indicates we're just down to your specific domain and usage patterns rather than the SOTA models being objectively better like they clearly used to be.

operatingthetan2• 3 months ago

It seems like people can't even agree which SOTA model is best at any given moment anymore, so yeah I think it's just subjective at this point.

LoganDark• 3 months ago

The value in Claude Code is its harness. I've tried the desktop app and found it was absolutely terrible in comparison. Like, the very nature of it being a separate codebase is already enough to completely throw off its performance compared to the CLI. Nuts.

abustamam• 3 months ago

What is your workflow? Do you use Cursor or another tool for code Gen?

mettamage• 3 months ago

Hmm

Will try it out. Thanks for sharing!

vidarh4• 3 months ago

I feel like it's Sonnet level for implementation, but not matching up to Opus for planning.

But I agree it's close enough that it's worth using heavily. I've not cancelled my Claude Max subscription, but I've added a z.ai subscription...

scotty79• 3 months ago

I had one occasion where GLM 5.1 did about 95% of the implementation that I needed but couldn't progress form there. And Codex (free quota) solved the remaining 5% on the spot. I'm super happy with both. I don't touch anything Anthropic with a 10 foot pole.

_blk• 3 months ago

What hardware do you run it on? Trying to consider the cost of subscription + API vs new HW..

DeathArrow3• 3 months ago

GLM 5.1 was the model that made me feel like the Chinese models had truly caught up. I cancelled my Claude Max subscription and genuinely have not missed it at all.

GLM 5.1 is pretty good but there are some "buts".

They hiked the prices 2 times this year. I subscribed to the pro coding plan just before the last hike. At the start of the year, they had only 5 hours quota and no weekly quota. And I hit the weekly quota hard. I can't upgrade the subscription to get a higher weekly quota because they jacked up the prices a lot recently.

My $30 subscription costs now $72. Previously was $15. Max was $49,then $80 and now $160.

_s_a_m_• 3 months ago

I used GLM 5.1 and it was bad, I have no clue why people claim it is good

bink-lynch3• 3 months ago

I have been using GLM-5.1 with pi.dev through Ollama Cloud for my personal projects and I am very happy with this setup. I use pi.dev with Claude Sonnet/Opus 4.6 at work. Claude Code is great but the latest update has me compacting so much more frequently I could not stand it. I don't miss MCP tool calling when I am using pi.dev; it uses APIs just fine. I actually think GML-5.1 builds better websites than Claude Opus. For my personal projects I am building a full stack development platform and GLM-5.1 is doing a fantastic job.

jadbox• 3 months ago

Why use ollama cloud versus like Openrouter?

zackify2• 3 months ago

I'm using pi the same as you. However, I have an MCP I need to use and the popular extension for that support works fine for me.

Really liking pi and glm 5.1!

blurbleblurble• 3 months ago

Opus 4.6 was incredible but Opus 4.7 is genuinely frustrating to me so far. It's really sharp but can be so lazy. It's constantly telling me that we should save this for tomorrow, that it's time for bed (in the middle of the day), and very often quite sloppy and bold in its action. These adjustments are getting old. The next crop of open models seems ready to practically replace the big ones as sharp orchestrator agents.

chillfox4• 3 months ago

I have never seen a model be “lazy” before (I have seen them go for minimal change). I have been using the models through the api with various agents and no custom system prompt.

So I am curious, how do people get these lazy outputs?

Is it by having one of those custom system prompts that basically tells the model to be disrespectful?

Or is it free tier?

Cheap plans?

make33• 3 months ago

I had to write multiple times in my prompt that it's not the model's role to change the subject or end the conversation at all.

I think that they do that to dodge conversations about controversial subjects without full-on refusing to answer. They'll give you an ok answer then tell you to go to get the walk you were talking about.

I also feel like maybe they think people are still ready to pay a lot if they feel like they're getting a lot of "high value stuff" even if the low value stuff the model refuses to do, so they basically try to stop you from doing low value stuff on Opus. I suspect that Sonnet or Haiku never tells you to go take a hike.

mkhalil• 3 months ago

Not to mention, that Opus cost orders of magnitude more money. These are VERY impressive and usage.

FAANGS love to give away money to get people addicted to their platforms, and even they, the richest companies in the world, are throttling or reducing Opus usage for paying members, because even the money we pay them doesn't cover it.

Meanwhile, these are usable on local deployments! (and that's with the limited allowance our AI overlords afford us when it comes to choices for graphics cards too!)

vidarh4• 3 months ago

I don't find GLM 5.1 beating Opus personally, but I do think it is good enough to consider it part of the SOTA pack at this point. It feels like it needs more time and tokens to achieve things, but that's okay - it's so much cheaper per token.

If Qwen3.6-Max is up there as well, it will be very interesting.

flyingsquirrel_• 3 months ago

GLM 5.1 is sometimes better than Opus 4.7. So it made me buy GLM coding plan.

wg03• 3 months ago

Notice the pattern that Chinese providers are now:

1. Keeping models closed source.

2. Jacking up pricing. A lot. Sometimes up to 100% increase.

embedding-shape4• 3 months ago

Huh yeah, that's truly a unique trait these Chinese companies don't share with companies in other countries.

aerhardt• 3 months ago

No it is not, but they had a unique positioning around open-source and the parent commenter means that they are losing it.

cute_boi2• 3 months ago

Well, they can't subsidize forever. And, it is kinda expected?

gpm• 3 months ago

Considering the propaganda value in controlling the inputs to the machine that answers peoples questions, I rather expect them to be subsidized forever.

OtomotO4• 3 months ago

US companies hate that trick?!

rc_kas• 3 months ago

you mean: invented

ai_fry_ur_brain• 3 months ago

Yeah, its almost like the casinos started rigging the game after they got all the addicts hooked. Who saw that coming???

If you overuse LLMs or get excited about them at all, you're ngmi and a complete idiot.

Tepix• 3 months ago

Are you talking about GLM 5.1, DeepSeek V3.2 or Kimi K2.6 (released one hour ago!)?

Oh wait, it doesn't apply to those…

dingocat• 3 months ago

Yet.

Kerrick• 3 months ago

Z.ai's Coding Plan with GLM 5.1 (Max) did more than double in price. It was $80 two weeks ago, and now it's $160.

cnlwsu• 3 months ago

what only Oracle can do it?

nicce• 3 months ago

Jacking up pricing. A lot. Sometimes up to 100% increase.

How is that different from American?

halJordan• 3 months ago

Qwen max has always been cloud only. And its a 1T+ model so it would be expensive

marsulta• 3 months ago

I think the benchmarks and numbers need to be easier to read. Those benchmarks are useless to the regular consumer.

jdw64• 3 months ago

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.

ezekiel682• 3 months ago

TBF, 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

hedora3• 3 months ago

Unless stuff changed since I last checked, context caching just reduces cost / latency. It does not change what tokens are emitted.

chatmasta3• 3 months ago

Is 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?

zozbot23413• 3 months ago

The Max series has never been open.

XCSme• 3 months ago

A bit weird to be comparing it to Opus-4.5 when 4.7 was released...

o10449366• 3 months ago

I 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• 3 months ago

You can try https://github.com/AlexsJones/llmfit

dakolli• 3 months ago

ToKeN PrIcEs ArE gOiNg tO PluMmEt, InTelLigEnCe WiLl Be AfForDaBlE FoR EvErYOnE

xmly• 3 months ago

Very impressive!

seanw265• 3 months ago

Kimi 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• 3 months ago

i 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

chrisss395• 3 months ago

FWIW 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.

archon8102• 3 months ago

I wonder if this means a better Cursor Composer model update is coming, since it builds on top of Kimi K2.

2001zhaozhao• 3 months ago

Cursor 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.

Aeroi• 3 months ago

why do people continue to benchmark their sota models against older models.

djyde2• 3 months ago

I'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.

djyde2• 3 months ago

But I've recently found that Cursor's composer2 is also really good to use.

freely0085• 3 months ago

Composer 2 is just Kimi 2.5, it's not their own model.

greyskull5• 3 months ago

I'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• 3 months ago

You 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

greyskull5• 3 months ago

It's certainly part of the problem. Thanks, I'll give that a shot.

martinald2• 3 months ago

Try 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.

greyskull5• 3 months ago

Thanks! These things you're mentioning like "You may be able to offload some layers to GPU...", "You can keep the KV cache on GPU..." configured as part of the llama.cpp? I wouldn't know what to prompt with or how to evaluate "correctness" (outside of literally feeding your comment into claude and seeing what happens).

Aside: what is your tooling setup? Which harness you're using (if any), what's running the inference and where, what runs in WSL vs Windows, etc.

I struggle to even ask the right questions about the workflow and environment.

Ey7NFZ3P0nzAe• 3 months ago

In my case, I was also running an ASR model and a TTS model so it was a bit much for my RTX 3090. I opted to offset like 5 layers to the cpu while adding a GPU-only speculative decoding with their 0.8B model.

Working well so far.

daemonologist2• 3 months ago

First of all nothing you can run locally, on that machine anyways, is going to compare with Opus. (Or even recent Sonnet tbh - some small models benchmark better but fall off a bit in the real world.) This will get you close to like ~Sonnet 4 though:

Grab a recent win-vulkan-x64 build of llama.cpp here: https://github.com/ggml-org/llama.cpp/releases - llama.cpp is the engine used by Ollama and common wisdom is to just use it directly. You can try CUDA as well for a speedup but in my experience Vulkan is most likely to "just work" and is not too far behind in speed.

For best quality, download the biggest version of Qwen 3.5 27B you can fit on your 4090 while still leaving room for context and overhead: https://huggingface.co/unsloth/Qwen3.5-27B-GGUF - I would try the UD-Q5_K_XL but you might have to drop down to Q5_K_S. For best speed, you could use Qwen 3.6 35B-A3B (bigger model but fewer parameters are active per token): https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF - probably the UD-Q4_K_S for this one.

Now you need to make sure the whole model is fitting in VRAM on the 4090 - if anything gets offloaded to system memory it's going to slow way down. You'll want to read the docs here: https://github.com/ggml-org/llama.cpp/tree/master/tools/serv... (and probably random github issues and posts on r/localllama as well), but to get started:

  llama-server -m /path/to/above/model/here.gguf --no-mmap --fit on --fit-ctx 20000 --parallel 1

This will spit out a whole bunch of info; for now we want to look just above the dotted line for "load_tensors: offloading n/n layers to GPU" - if fewer than 100% of the layers are on GPU, inference is going to be slower and you probably want to drop down to a smaller version of the model. The "dense" 27B will be slowed more by this than the "mixture-of-experts" 35B-A3B, which has to move fewer weights per token from memory to the GPU.

Go to the printed link (localhost:8080 by default) and check that the model seems to be working normally in the default chat interface. Then, you're going to want more context space than 20k tokens, so look at your available VRAM (I think the regular Windows task manager resource monitor will show this) and incrementally increase the fit-ctx target until it's almost full. 100k context is enough for basic coding, but more like 200k would be better. Qwen's max native context length is 262,144. If you want to push this to the limit you can use `--fit-target <amount of memory in MB>` to reduce the free VRAM target to less than the default 1024 - this may slow down the rest of your system though.

Finally, start hooking up coding harnesses (llama-server is providing an OpenAI-compatible API at localhost:8080/v1/ with no password/token). Opencode seems to work pretty reliably, although there's been some controversy about telemetry and such. Zed has a nice GUI but Qwen sometimes has trouble with its tools. Frankly I haven't found an open harness I'm really happy with.

greyskull5• 3 months ago

Thank you for all this, I'll give it a shot. Out of curiosity, are there any resources that sort of spell this out already? i.e., not requiring a comment like this to navigate.

nothing you can run locally, on that machine anyways, is going to compare with Opus

Definitely not expecting that. Just wanted to find a setup that individuals were content with using a coding harness and a model that is usable locally.

What does your setup look like? Model, harness, etc.

unethical_ban• 3 months ago

This is exactly what I have been looking for: Something straight to the point. Thanks a lot!

ihowlatthemoon2• 3 months ago

I run a setup similar to yours and I've had the best results with Qwen3.5 27B. Specifically the Q4_K_M variant. https://unsloth.ai/docs/models/qwen3.5

I use llama-server that comes with llama.cpp instead of using ollama. Here are the exact settings I use.

llama-server -ngl 99 -c 192072 -fa on --cache-type-k q4_0 --cache-type-v q4_0 --host 0.0.0.0 --sleep-idle-seconds 300 -m Qwen3.5-27B-Q4_K_M.gguf

greyskull5• 3 months ago

Thanks, I'll have to continue experimenting. I just ran this model Qwen3.6-35B-A3B-GGUF:UD-Q4_K_XL and it works, but if gemini is to be believed this is saturating too much VRAM to use for chat context.

How did you land on that model? Hard to tell if I should be a) going to 3.5, b) going to fewer parameters, c) going to a different quantization/variant.

I didn't consider those other flags either, cool.

Are you having good luck with any particular harnesses or other tooling?

digimantis• 3 months ago

i dont get why people defend $200/month models against open source model that cost 1/10 of the price, like literally

fragmede• 3 months ago

This thing on Celebras is going to be ridiculous.

johnnyApplePRNG• 3 months ago

Nowhere 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 :/

edg50004• 3 months ago

Is 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.

goodmythical2• 3 months ago

It 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

edg50004• 3 months ago

Very interesting, thanks for sharing

EDIT: It's completely different though. This is more of a commodities market/auction/inference broker mechanism it seems.

Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

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