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.

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

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.

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.

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 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.

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.

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.

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

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.

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.

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!

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

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

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

4.7 is better, but its also wildly expensive

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

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!

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.

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.

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

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.

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?

what only Oracle can do it?

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.

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

Why use ollama cloud versus like Openrouter?

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.

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.

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.

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.

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.

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.

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?

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

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.

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

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

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?!

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

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.

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.

The Max series has never been open.

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

You're probably just holding it wrong.

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

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

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

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.

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.

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.

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.

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.

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.

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.

> 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.

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

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

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.

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

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.

Recently it had great limits but this month I'm trying open router directly.

And the subjectivity is bidirectional.

People judge models on their outputs, but how you like to prompt has a tremendous impact on those outputs and explains why people have wildly different experiences with the same model.

Where are you getting 3 months for $7?

Haven’t you heard about Codex?

Perhaps not even necessarily subjective, just performance is highly task-dependent and even variable within tasks. People get objectively different experiences, and assume one or another is better, but it's basically random.

> The value in Claude Code is its harness

If this was the case then Anthropic would be in a very bad spot.

It's not, which is why people got so mad about being forced to use it rather than better third party harnesses.

Pi is better than CC as a harness in almost every respect.

> The big kicker for GLM for me is I can use it in Pi, or whatever harness I like.

Yes, but... isn't the same true for Opus and all the other models too?

That's unfortunate. 70-80k tokens is roughly the point where I start wrapping up with giving agent required context even on the small to medium sized requests.

That would leave almost no tokens for actual work

They had a Christmas deal that ended January 31.

Unless you're looking at something like a pass@100 benchmark, the benchmarks are confounded heavily by a likelihood of a "golden path" retrieval within their capabilities. This is on top of uncertainties like how well your task within a domain maps to the relevant test sets, as well as factors like context fullness and context complexity (heavy list of relevant complex instructions can weigh on capabilities in different ways than e.g. having a history where there's prior unrelated tasks still in context).

The best tests are your own custom personal-task-relevant standardized tests (which the best models can't saturate, so aiming for less than 70% pass rate in the best case).

All this is to say that most people are not doing the latter and their vibes are heavily confounded to the point of being mostly meaningless.

>just performance is highly task-dependent and even variable within tasks. People get objectively different experiences, and assume one or another is better, but it's basically random.

You are right that this is not exactly subjectivity, but I think for most people it feels like it. We don't have good benchmarks (imo), we read a lot about other people's experiences, and we have our own. I think certain models are going to be objectively better at certain tasks, it's just our ability to know which currently is impaired.

Aha... Well, I let Codex (Claude Code would work too) manage/troubleshoot .xlsx files too, seems to handle it just fine (it tends to un-archive them and browse the resulting XML files without issues), seen it do similar stuff for .app and .docx files too so maybe give that a try with other harnesses/models too, they might get it :)

outputting docx files does not have much to do with model capability. it is about whether tool calling has be configured .

Try loading the models up in a coding harness like Claude Code. There's a few docx skills listed on Vercel's skill index.

https://skills.sh/tfriedel/claude-office-skills/docx

Recently, more like 20 years ago in LLM-years.

It's a good model though, would be nice with a refresh.

The Nvidia Nemotron models are recent, and of course the Gemma 4 series from Google.

> those days seem so long gone now.

Well, Musk v OpenAI kicks off in one week from now with the objective of forcing them back to their roots. A jury will be deciding whether a nonprofit accepting $50m - $100m of donations and then discarding their mission for an IPO is OK or not. Should be interesting.

gasp Science!

Any idea why they do that?

You took a simple path, embedding smaller into larger. What if you need to reduce number of layers and/or width of hidden layers? How will you embed larger into smaller? As for the "addition of same layers" - would the process of "layers to add" selection be considered training?

What if you still have to obtain the best result possible for given coefficient/tokenization budget?

I think that my comment express general case, while yours provide some exceptions.

That "underappreciated underlying structure or geometry" can be just an artifact of the same tokenization used with different models.

Tokenization breaks up collocations and creates new ones that are not always present in the original text as it was. Most probably, the first byte pair found by simple byte pair encoding algorithm in enwik9 will be two spaces next to each other. Is this a true collocation? BPE thinks so. Humans may disagree.

What does concern me here is that it is very hard to ablate tokenization artifacts.

there is evidence it is useful in some cases, but obviously no evidence it is enough if you chase to beat SOTA.

If an American company, let's say a company that writes software for power stations, would use the services of a French or Chinese AI company under such enterprise licenses, how long would you think it would take until someone, in Congress e.g., would interfere?

What if they learned that half of the American small and medium sized companies would have started pouring all their business information into such a service?

I’m not sure what you are implying by “enterprise license”, but if you think it provides any meaningful protection against malicious US government actors, you really need to read and internalize the US CLOUD Act.

On a related note, I really need to try some local models (probably starting with qwen), since, at least in 2026, the Chinese models are way better at protecting democracy and free speech than the US models.

Thats crazy to pin your entire workflow on. Sorry boss, I can't work today I'm being rate limited by Anthropic :/

That doesn't address the concern. Google isn't interested in violating 1st and 4th amendment rights of people who criticize the government... but they do anyway (or more correctly assist the government in doing so).

The Max series was never available for local use, though. So this is expected.

Indeed. And that’s with Anthropic hiding reading traces unlike these other comparisons.

Oh for sure, they're getting a whole lot of Chinese people and other non-Westerners through the door already - mostly, the people who are being ignored or even blocked outright by the big Western labs. That's territory we purposely abandoned, and they're going to control it by default.

What a delusional dumb ass you are

Damn. Yeah, that sucks. I did play with it earlier again and it did seem to slow down.

It's frankly a bummer that there's not seemingly a better serving option for GLM 5.1 than z.AI, who seems to have reliability and cost issues.

its an SKU from OpenAI's perspective, broader goal and vision is (was) different. Look at the Claude and GLM, both were 95% committed to dev tooling: best coding models, coding harness, even their cowork is built on top of claude code

Anthropic limiting Claude subs to Claude code is what pushed me away in the end because I wanted to keep using Pi.

Opus is about 7 times more expensive than GLM with API pricing. And since you can only use the Opus subscription plan in CC, you're essentially locked into API pricing for Pi and any other harness.

So you're either paying $1000's for Opus in Pi, or $30/month for GLM in Pi. If the results are mostly equivalent that's an easy choice for most of us.

You're not giving an AI command line access to your work computer? How do you expect to keep up? /s

The problem with B2C is that you need to have leverage of some kind (more demanding applications, planned obsolescence, ...) in order to get people to keep on buying your product. The average consumer may simply consider themselves satisfied with their old product they already own and only replace it when it breaks down. On the contrary, with the cloud you can keep people hooked on getting the latest product whether they need it or not, and get artificial demand from datacentres and such.

Currently, yes. But I don't find it hard to imagine that in a while we could get reasonably light open models with a level of reasoning similar to current opus, for instance. In such a scenario how many people would opt to pay for a way more expensive cloud subscription? Especially since lots of people are already not that interested in paying for frontier models nowadays where it makes sense. Unless keep on getting a constant, never ending stream of improvements we're basically bound to get to a point where unless you really need it you are ok with the basic, cheaper local alternative you don't have to pay for monthly.

rich people dont concern themselves with the cost of tokens.

Ask a few Native Americans about dominance.

Or maybe families of African descent.

Or maybe families of Japanese Americans who lived in the US during WWII.

Or maybe people of Latin descent living in the US today.

Future upgrade cycles on phones and laptops, PCs, will be driven by SOCs that embed some type of ASIC that run a specific model. Every 6 months there will be a new, better version to upgrade to, which will require a new device. This is how Apple will be able to reduce cycles from 3 years to 6-12 months.

I think average users are already okay with the reasoning level they'd get with current open models. But the big AI firms have pivoted their frontier models towards the enterprise: coding and research, as opposed to general chat. And scale is quite important for these uses, ordinary pro hardware is not enough.

This is really just a question of product design meeting the technology.

Today, lots of integer compute happens on local devices for some purposes, and in the cloud for others.

Same is already true for matmul, lots of FLOPS being spent locally on photo and video processing, speech to text, …

No obvious reason you wouldn’t want to specialize LLM tasks similarly, especially as long-running agents increasingly take over from chatbots as the dominant interaction architecture.

It is not even rich. If you earn more than $30k it is worth your employer spending $3k on AI tools.

I think businesses running datacenters are much less likely to frivolously buy the latest GPUs with no functional incentive than general consumers are...

Sure, not plus or max. I just use their lesser moe ones locally (that would never come close to massive sota models) all the time.

I'm not sure how this makes sense when Claude models aren't even coding specific: Haiku, Sonnet, Opus are the exact same models you'd use for chat or (with the recent Mythos) bleeding edge research.

Perhaps I'm being extremely daft: If the API is 7 times more expensive, then why is it $1000 vs $30? Or is there a GLM subscription one can use with Pi? Certainly not available in my (arguably outdated) Pi.

Just sign up for an AWS account and use the Anthropic models through Bedrock which Pi can use.

You give it command line access in a VM...

That is essentially what the reasoning reinforcement training does. It is getting the model to say things that are more likely to result in the correct final answer. Everything it does in between doesn't necessarily need to be valid argument to produce the answer. You can think of it as filling the context with whatever is needed to make the right answer come out next. Valid arguments obviously help. but so might expressions of incorrect things that are not obviously untrue to the model until it sees them written out. The What's The Magic Word paper shows how far that could go. If the policy model managed to learn enough magic words it would be theoretically possible to end up with an LLM that spouts utter gibberish until delivering the correct answer seemingly out of the blue.

I am using GLM-5.1 in pi.dev through Ollama Cloud. I am able to get by on the $20 plan. I use it a lot and the reset is hourly for sessions and weekly overall. This is the first week I got close to the limit before reset at about 85% used. I am probably using it about 4 hours a day on average 6 or 7 days per week.

I'm not the OP, but it's the latter. I'm currently using the "Lite" GLM subscription with OpenCode, for example. I'm not using it very heavily, but I haven't come close to hitting the limits, whereas I burned through my weekly limits with Claude very regularly.

Why use tricks to support a company that is hostile to your use case?

Anthropic models and training data is optimized for coding use cases, this is the difference.

OpenAI on the other hand has different models optimized for coding, GPT-x-codex, Anthropic doesnt have this distinction

You can use GLM’s coding plan in Pi, just use the anthropic API instead of the OpenAI compatible one they give.

i give it in real ubuntu, no vm, no docker. so long I don't ask it to organize files, it will behave. it has not screw me so far.

You mean a VM like the one that contains a 0day that can escape the sandbox that gets found every year at pwn2own?

Or tell pi to add support for the coding plan directly. That gave me GLM-5.1 support in no time along with support for showing the remaining quota, etc, too.

It also compresses the context at around 100k tokens.

In case anyone is interested: https://github.com/sebastian/pi-extensions/tree/main/.pi/ext...

But they detect it under the hood and apply a similar "variant", as API results are not the same than on Claude Code (that was documented before by someone).

Godspeed

Maybe, but the sort of 0days you're talking about aren't exploited in any meaningful way for almost all developers.

Presumably you’re also using a browser to view this web page. There have also been vulnerabilities in that. You have to draw a line somewhere.

"Seatbelts don't save the life of everyone who gets into an accident, so why bother wearing one?"

I run mine as a separate unprivileged user. (No VM.) Am I pwned?