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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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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!)

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.

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

>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

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.

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)

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

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.

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.

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...

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

Its the new freeware model!

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.

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

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.

Why is it sad? These things are useles all around, along with the people who overuse them.

It would be a great day for humanity if people would stopping glazing text autocomplete as revolutionary.

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

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

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.

Who are you paying $10/month? OpenRouter?

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

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.

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.

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

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

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.

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

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.

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

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.

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

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

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

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.

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

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

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.

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.

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

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

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

How is that different from American?

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…

US companies hate that trick?!

what only Oracle can do it?

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

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.

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.

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

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).

You may be interested in "radiance cascades"

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

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?

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

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.

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.

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.

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.

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.

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.

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...

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.

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.

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

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

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

The Max series has never been open.

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