Comment by pimeys
8 days ago
I have taken another look on these open models after the fiasco of Fable and GPT 5.6 this weekend and... GLM-5.2 truly is a good workhorse model for daily programming. I consider myself a heavy user of LLMs and a seasoned developer. A typical session for me with GPT is usually over a hundred dollars...
This weekend I programmed a matrix bot with encryption and a Rust agent with some tools. Because I need one and OpenClaw just felt... not what I wanted. Two days later and 20 dollars poorer I have what I need: a multimodal agent written in rust that has access to my homelab.
Nothing felt off with GLM. It did what I wanted, was fast, had a decent not very annoying personality and was much cheaper than Opus or GPT.
I used it unquantized through Fireworks, but there are multiple other providers too.
GLM 5.2 is a great model, but if you only want to use the best model available, it isn't there yet. Every lab releases models that memorize benchmark answers, both intentionally and unintentionally. But we consistently find that models from Chinese labs have a wider gap between public benchmarks and our evaluations, which we designed to be less vulnerable to benchmaxxing.
In multi-agent coding environments, GLM 5.2 is just shy of Opus 4.6 on average. Data at https://gertlabs.com/rankings
But when factoring in performance/cost, GLM 5.2 is the frontier model.
> but if you only want to use the best model available, it isn't there yet
I'm trying to wrap my head around exactly why so may people seem to want the best model available when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price. And the frontier models get nerfed constantly so you with open weight you can get something slightly less performant but way more stable. Almost like buying a Ferrari for your daily commute instead of a Toyota or even a Mercedes.
I think there are several factors. Certainly marketing making us think we need the shiny thing which is rampant online and very smart people think they aren't susceptible to. There's a lot of really odd 'I trust Anthropic/OpenAI more than Deepseek' which tends to ignore, for starters, that you can run choose your provider and still save a ton. I also think there's some amount of addiction and brand loyalty where a Ferrari is one hell of a drive so that you turn your nose up at that sensible Toyota. Oh the other one I see used is like oh only fable can oneshot updating my embedded systems thing from 1975 to rust which is great but let's recognize how niche that is.
And it ends up just coming across as people are getting SO reliant on the tools so fast. Maybe it's ok to think and like read a few lines of code and work with these agents to convert your thing to rust or center your div. Even if coding is over which in some sense it certainly is, don't turn your mind into the wall-e people yet. I found myself guilty of this so often. It takes way more time and effort to do things via prompt and I wouldn't just open the editor and fix it because that dopamine hit of the magic the abstraction provided was so strong.
So I'm pretty much done using the 'best' (on benchmarks, if money isn't an object, etc etc) models available. After a year on Sonnet/Opus/GPT5x I'm having way better results with open weights models that don't get lobotomized weekly. I'm finding ways to do the crafting part of building software by focusing on honing my harness and workflow. I'm enjoying changing the oil on my Toyota after a year of almost flying off cliffs in my Ferrari and if I can check my ego it's a purely positive thing.
> I'm trying to wrap my head around exactly why so may people seem to want the best model available
This is the logical end point of the fear-based way LLMs are marketed. You must want the best, because everyone who has the best can work faster than you, generate more — if you don't have the best, you are behind! Why would you want to use anything other than the best?
The thing is, once everyone has the best, the question is: how much can you spend? If you can't spend more, you are behind! If spending the most will get you ahead, why would you not want to spend the most, if you can afford it?
There is only one way through this, in the long run: work out a way forward that doesn't make you dependent on this cycle. If you can compete at all, without the spend, what happens is: they burn money and you don't.
FWIW so far I don't think the benchmarks prove very much about the actual experience, and you can discover this just as easily without spending any money. And we know this about benchmarks! Once a benchmark seems useful as a measurement, it becomes a target and it stops being as useful.
I think your strategy is right. It requires bravery, and as you say, it requires ego balance. But I believe it is obvious that the world will either come around to a more sensible, stable pattern or it doesn't matter either way because we're fucked. So opting out of this mad early cycle and choosing to be calmer and happier is a choice you can just make.
> most halfway decent models can write damn good code for a fraction of the price.
The difference is how the model is used.
With Opus you can give it a long-horizon task (eg build an entire feature) and it will plan it out and implement it and almost always stay on task. This is what people mean when they say "agentic tasks"
With the lessor models the code is fine, but they need something else to plan what needs to be done.
GLM-5.2 is the third model (after Opus 4.6+ and GPT-5.5) that can do this agentic style work.
Notably Gemini 3.1 Pro is notoriously bad at this style work - the code is good, but it drifts off task most of the time. 3.5 Flash is supposed to address this, but I haven't had a good reason to try it.
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Yeah, the funniest thing about everyone freaking out about Fable's capabilities recently was that for most of the stuff they were amazed by, you could get roughly the same result from DeepSeek Flash.
I used to be obsessed with what's the best model. Then a while back when the new best model came out, I tested it on a task. I also tested its little brother (much smaller model from same company).
They both completed the task perfectly except the "best" model (the bigger one) cost 5x more and took 3x longer...
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I don't drive the best car available on the market. I don't own the fastest and best PC/Laptop/Smartphones available. I don't live in the best house in my city. I made reasonable choices that balance my needs and my available budget.
I would say one thing I've enjoyed about the latest frontier models from US labs is that you just work at a higher level of abstraction. You can talk about the end goal and it'll just rip. You'll add scaffolding to constrain the patterns etc, but I do way less baby sitting than I expected on 5.6 vs 5.4 vs Deepseek v4 Pro.
For math, even the frontier has shortcomings, and there is a steep drop from GPT 5.5 xhigh to anything else. The time wasted by less-than-SotA just isn't worth it.
Reason people want the best: people want to believe their project is so advanced that they need the most clever LLM possible. To say otherwise is to admit that it's not really frontier or novel in any way. And people don't like that.
I’m writing a lot of React code and find that the cheaper models are pretty terrible. Maybe I’m holding it wrong but the experience that the cheaper model is usually enough just track with my experience. Worse, I find predicting the difficulty of tasks exceedingly difficult. More often than not using the initially cheaper models requires me to reroll with a more expensive one or waste a lot of times and tokens cleaning up the subpar results. With OpenAI and Anthropic still subsiding tokens, not using the best models still seems like a tough ask.
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Because it truly makes a difference. Opus 4.8 was great until we experienced Fable 5.
And post Fable retraction, I am now most certaily noticing Opus being 'dumber' also.
Open Weights are good. Not (yet) as good as leading closed models. Unfortunatly they will be declared 'illegal' any day now, and I unfortunately do not see myself able to run GML 5.2 in my basement homelab any time soon.
I've landed in a similar place by reducing effort and cutting up tasks. I find that more exacting specifications to the models, yield significantly less need for "effort". Combining each with multjple git worktrees and an integration branch for the current worktrees themselves has yielded incresible results.
This also allows me to play with, and mix codex, claude cli, and others. This is my happy spot for the last two months.
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For me, the 20€/months subscriptions were always sufficient, and it's nice if that subscription give the latest and greatest results.
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I agree, but there are use cases for the 'best model' other than converting your 1975 stuff to rust: for use cases where LLMs are just getting started to be useful I really want to use the current 'best' model: e.g. CAD, PCB design etc. In particular anything which requires spatial reasoning. The short time I had access to Fable 5 - it was just way better than any other model.
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Of course people want the best model available, even at 10x costs, if they are not paying for it. If the company is paying, why wouldn't you want a 2% better model?
That changes as soon as the developer is the one paying for a model. Then it's a classical engineering trade-off between money and quality, and that's where open models are clear winners.
I think people are grouping into two flows.
One group is trying to get the LLM to basically one shot everything and not properly reviewing the output.
Others are using the LLM to assist their human intelligence in a tight loop.
If you’re doing the former you really do need the best model available because that’s still right on the edge of what LLMs can do at best, and at worst you’re just shipping pure unmaintainable slop.
If you’re doing the latter then you can get away with a slightly less powerful model without it making a material difference because your human intelligence is filling in gaps
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I'm using DeepSeek v4 Flash through OpenCode and OpenRouter, and works just fine. It's not the bottleneck, I am, for what I'm building. That involves understanding the problem I'm solving, checking correctness
Meanwhile, it's such a cheap model that I've spent not even $25 over 3 weeks.
Because not every problem is a coding problem or not entirely solvable through code. Other tasks include legal, philosophical, financial, investigative, and combinations of these and others.
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> most halfway decent models can write damn good code for a fraction of the price
The problem isn't what they do in a blank state. It is how they get there and the edge cases. Some models also take longer (uses more steps) i.e. end up costing more despite being "cheaper".
I've seen models:
- Back out plans non-stop. Tried the obvious path. Invents X/Y/Z excuse (without verifying) that it can't be done. Notes that down and moves on. It could be as simple as site A being down and to download from site B but that's it.
- Hacks the test to make it work. Code is wrong? Nah, let's update the test.
- Keep saying useless things like YAGNI and infinite excuses like too risky to never do the work.
- Claims they are done but there's 100 edge cases not covered. When you try to use it it fails in ways you as a human assume it should work. You can write a spec to cover it all but then what's the point?
- Be trigger happy and never investigate. Tries to do it. 5 minutes. Oh it failed. Back out. Repeat. Better models definitely spend more time analyzing and actually "think". I've had models spend hours trying to do a change due to this method when an actual investigation (code walkthrough) might have solved it.
- Know and use the right tools. A lot of lesser models have infinite fear e.g. oh docker might not be available (it is) or this and that (even if you nudge it in any way) and spend a lot of extra time "working around" it.
The list goes on. Better models definitely help.
Only thing to agree on is no you don't need Fable but saying Sonnet can do the job instead of Opus is a different story. It's so obvious when Sonnet touches the code that I can't give it more than 5 minutes. It lies. Doesn't check. Forgets things and then messes up.
I am forced to use AI as part of my job to write code. As a matter of fact, I was recently told that I'm not using enough AI according to their metrics, even though I'm producing good quality code on time. Since the cost is one of the things I'm being judged on, you're damn right I want to use the newest and most expensive model available.
>> I'm trying to wrap my head around exactly why so may people seem to want the best model available when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price.
The reason is pretty simple and has to do with statistics: on long-horizon tasks, small errors and deviations from the "good path" compound.
>why so many people seem to want the best model available
In my case, I rarely ever go over the Claude/ChatGPT subscription limits, so might as well use those considered-best models. If I had to generate millions of lines of code, maybe I would've used the open models more.
It's also geeks and engineers using these models and being the most vocal. We always think we're special and need the extra horsepower. Ever been on one of those home lab subreddits ? Same story.
> I'm trying to wrap my head around exactly why so may people seem to want the best model available
To me this is a "more expectations mean more disappointment" situation.
Some people have higher expectations than others, and even the best model available is not good enough for what those people really want it to do once you start digging. In that light, the goal is not using the best model, but rather using the least insidiously deficient model.
Many people chase the edge because it's the least disappointing.
> when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price.
The fatuousness of this statement pretty quickly becomes apparent if you spend more time looking at it, IMO, because the venn diagram of "damn good" and "not nearly good enough" strongly overlaps. Even the best model writing excellent lines of code still has noticeably deficient ability to decide which excellent lines of code to write. The goal is to improve the separation between them, not save a few dollars, because the emotional effort is worth more to us than the money.
> And the frontier models get nerfed constantly so you with open weight you can get something slightly less performant but way more stable.
Your minimization of performance differences and maximization of stability differences is exposing your biases.
Side note: I think you should know that to me at least some of what you said reads like self-rationalized moralizing. I couldn't help but imagine Principal Skinner saying "Am I so out of touch? No, it's the children who are wrong." People don't only want different things than you do because they don't know what they're doing.
> I'm trying to wrap my head around exactly why so may people seem to want the best model available
I've been programming since I was a kid. I enjoy it a lot, I like knowing how things work, I get excited about new compiler features, I stayed up every night for a week when I discovered Lean 4, etc etc etc.
At the same time I realized a few years ago that I just don't want to write any code ever. Or read any code. Coding is addictive and fun, but I'd rather talk to the computer and have things magically get done. (FWIW learning how to use LLMs feels more.. fulfilling, too)
Anyway. GLM 5.2 is nice and all, but I might have to spend half an hour guiding it to come up with a plan I'm happy with. And with Opus it could be 15 minutes. I'm still going to spend an hour talking to LLMs one way or the other, but with Opus it will be a less frustrating hour. If Fable gives me a frustration-free hour, I'll switch to Fable.
What is your favorite harness for the open weights?
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In your box plots, 4.6 sonnet wins over all (even opus 4.6, the 4.8’s and fable).
That’s not super surprising to me, but, given the apparent randomness of the stack ranking, is GLM actually worse than any of the Anthropic models? This looks like a 10-way tie to me.
We've spent some time trying to understand this anomaly, even re-running Sonnet 4.6 through our evaluations to see if that would bring down its scores... and it didn't. I don't know what they did differently, but it's basically Opus 4.6 with more temperature variability (some great responses, some less great, with an approximately frontier median response in agentic work specifically). It is smart, methodical and excellent at tool calling in our custom environments.
We now use Sonnet 4.6 for a number of internal use cases we wouldn't have considered otherwise.
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Man, there is exactly zero information on your site about how your benchmarks work. Why should one trust your numbers when there is no way to verify them?
Scroll to the bottom for the methodology (sorry, this should be linkable)
What is the methodology of your benchmark?
On the contrary, I personally think these broader benchmarks are meaningless. I think personalized benchmarks are the way to go. They should answer "How does this model perform for MY use-case?" rather than trying to answer "How does this model perform across all coding environments?"
Case in point: I use Elixir which is not as popular as Python, is always a hit or miss with most SOTA models at the top of these benchmarks. Whereas, the ones in the middle of the benchmarks (like the GLM) almost always outperform even SOTA models from Google / Anthropic. However, this is relevant only for my use case and I wouldn't just advocate a model for everyone based off my use-case alone.
We use a rotating pool of ~100 games for the coding parts of the benchmark, and are scored objectively based on ratings similar to Elo. Models write code submissions to interact with the environment, then are evaluated in large batches against other submissions.
We test 11 popular/interesting languages (you can see the Languages chart to filter), but not Elixir -- although other evaluations have found that many LLMs solve more problems when working with Elixir [0]. Why models write code well in some languages over others seems to go beyond pre-training data (Python scores quite low for most models) and we don't fully understand it.
[0] https://elixirforum.com/t/llm-coding-benchmark-by-language/7...
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Opus 4.6 is still my preferred model for work, so this is great to hear.
I can't wait for open models to take over in all categories.
Sounds like this is the year for coding.
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> In multi-agent coding environments, GLM 5.2 is just shy of Opus 4.6 on average.
Just want to express how amazing that is. Opus 4.6 is an amazing model. That an open weight model like GLM 5.2 competes with it is nothing short of outstanding.
If a good SWE is $150/hour, does the model cost actually matter? Surely you'd be willing to spend $10/hour to make that SWE 20% more productive? The model cost is still much less than the salary.
I don’t think any engineers who cost $150/hr are having their productivity moved by 20% depending on a $10/hr gap between models on or near the frontier.
Most of the gains right now come from tooling and process and any big post 2025 language model. The specific model isn’t that important right now.
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With Claude Code Ultrathink, I used 3 million tokens in 20 minutes. At API prices, that would be around 30$. So 90$/h. Model cost is not that much lower.
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But SOTA models used liberally at API pricing is a lot more than $10/hour. You can probably burn $100+/hour with just a single agent, and probably thousands when running agents programmatically, e.g. workflows.
Opus 4.6 was better than the current 4.8 in my subjective opinion using it. I have no real reference since in Europe mythos and its sister models aren't available...
So having a model of 4.6 quality is still extremely awesome. That currently is more of less the frontier reference outside the US :(
Something I don't see in your charts is acknowledgement of the difference, sometimes paradoxical, in strength between the same model at different reasoning levels. Do you have charts that include low/med/high/xhigh/max for the various models?
This is something we omit for a few reasons but it's probably the biggest blind spot in our evaluations; we opt-in to auto-reasoning/adaptive reasoning or max thinking token budgets where supported (supported by most models now), but when an explicit reasoning level is required, we fall back to High reasoning. In practice, we've found most models scale High-><whatever marketing term is max reasoning> pretty consistently, but if one vendor started throwing 10x the resources into max reasoning and they didn't support auto-reasoning, they would be unfairly penalized in our evaluations.
I find it hard to trust a ranking system that gives Sonnet a higher capability score than Fable.
It would have made things easier for us if Sonnet 4.6 scored lower, but it's a great model and the data is real.
It doesn't have a higher capability score than Fable, though. We break our coding evaluations into 2 parts, and "one-shot coding" makes up part of the index, where Fable significantly outperforms every other model, which is why it's ranked at the top despite Sonnet 4.6 having a slightly higher median (and lower average) in long-horizon agentic workloads. One-shot coding tends to be the most correlated with other companies' model cards, whereas agentic coding is partly about how well a model can adapt to a custom harness. Fable also refused some tasks.
Data at https://gertlabs.com/rankings?ow=1&mode=oneshot_coding
Why Deepseek v4 flash is better than pro in your benchmarks?
It's 100% due to tool use -- Flash adapts much better to our custom harness with tool names that are not identical to what models were likely trained on. DeepSeek V4 Pro performs much worse in that aspect than almost all other recent releases, for whatever reason.
I have also found deepseek flash beat pro in some of my own internal evals for tasklet.ai it’s really surprising and I don’t understand it
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This was a preview release. They haven't finish training. The Pro contains more knowledge but it probably takes longer training than flash for the smarts to kick in.
After having used GLM 5.2 and Opus 4.8 for enough time, I'm very unconvinced of the benchmark maxxing claims - if anything, GLM 5.2's rather lackluster performance on benchmarks compared to Opus 4.8 paints the opposite picture when compared to the subjective experience.
When I first used Opus 4.8, I threw several different workloads I had at it - I have Claude doing a lot of misc projects whose primary purpose is pretty much just studying what AI agents can do for my own curiosity and no other reason. Opus 4.8 was one of the first models I ever snuck in there that basically ran out of control. No previous Opus or Sonnet model I had used ever did this. Within hours every agent I had running was writing non-sense tool calls that echoed pretend commands that didn't exist, like 10 in a row, and talking about the "tool channel" being dirty. I switched back to Opus 4.7 and assumed Opus 4.8 was legitimately just broken.
I did come back to Opus 4.8 and found that it was indeed, pretty powerful. But that initial experience has stuck with me on just how narrow of a perspective any given test or benchmark is guaranteed to have. LLMs are too broad, it really doesn't matter what you try to do in your benchmark, you will necessarily get a limited view of what the model is capable of and its shortcomings. This will remain true for at least as long as models are susceptible to massive swings in performance based on randomness and minor differences in prompts and other environmental factors.
I'm not saying benchmarks are useless or that your benchmarks are not possibly closer to the truth either. All evidence at least points to the idea that Chinese models perform very well in coding but often have more mixed results on other tasks. I'm just saying that at this point, benchmarks feel like they have limited connection to my actual real experiences. GLM 5.2 actually scored kinda meh on a lot of benchmarks (compared to closed frontier models) but my actual experience using it does not match this.
And I'm definitely not saying GLM 5.2 is better than the frontier LLMs here, just that the race is close. I still prefer GPT 5.5 right now for code review, I think, and Opus clearly has some advantages depending on the task. It's just no longer a given that Opus 4.8 will perform better than GLM 5.2 on any given task, so to me the calculus behind "using the best model available" is getting complex and you might need to get a feel for what models have what strengths to really figure it out.
I do feel like the "use the best model available" mentality is not going to die any time soon, but if it does die, it will be gradual and start soon for programming. Modern LLMs are still not a full superset of what human programmers can do, but still larger models are definitely starting to hit diminishing returns for tasks at the lower end of complexity, and that is a big deal. It's a weird world where some tasks you can feel kinda confident just throwing Gemma 4 at it and not sweating whether you should use a better model; I've certainly done it for some quick Python scripts or getting an overview of some code I'm unfamiliar with.
I really dislike opus 4.8 it rarely compete things and prefer to waste tokens making lists of things that are missing. When stuck or need input it words the challenge at length without conveying anything useful for decision making, and quite often its solution to problems is to excise features or just try catch errors and proceed with faulty data silently
Why is Sonnet 4.6 ranked higher than Opus 4.6?
Sonnet 4.6 is ahead of Opus 4.7? Hm.
Notice the website url is the same name as the commentor.
Notice he's using "trust me bro" benchmarks.
Can we just remove all the motivated speech on HN? This is just not trustworthy information at all and obviously is incentivized.
Everyone is grinding and marketing nobody is actually discussing anything for real.
What does this even mean?
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Im really curious about this. Why pay API pricing? I burn 1000s of dollars a month of api according to claude usage but only pay the $100 subscription
My increasing frustration with these plans is the harness lock in.
Anthropic won't even let you run "claude -p [prompt]" any more... They bill it at api rates.
So if you're trying to automate the ai (and seriously, that's the point) the subsidized plans are crippled.
They postponed that change, here is the email they sent out:
> In May, we sent you an email announcing that starting today, the Claude Agent SDK, claude -p, and third-party apps built on the Agent SDK would stop drawing from subscription rate limits and move to a dedicated monthly credit. We're writing to let you know that we’re not making this change today. We’re working to update the plan to better support how users build with Claude subscriptions.
> What this means for you
> Nothing changes for now. Agent SDK, claude -p, and third-party app usage continues to work with your subscription exactly as it did before today, and there's no credit to claim. Your subscription limits are unchanged. When we have an update, we'll share it with advance notice before it takes effect
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Z.ai does not lock you in to any harness.
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They reverted this decision, "claude -p [prompt]" works with your subscription ok.
I'm using synthetic.new and Neuralwatt with pi and its good and also cheap
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I think they rolled that back
They canned the moved to make -p commands API billable.
There is a whole iceberg topic on subsidizing.
So your question is really “if they’re giving free usage, why not take advantage of it?”
I do, so I don’t know the reasons not to, other than to experiment.
And codex is even more subsidized. It's an absurdly good deal.
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If you're using Matrix, consider Hermes as a harness if you haven't already. Native gateway support. I've been primarily using mine through Element and it has largely been great.
Oh interesting. I basically chose Matrix because setting anything up with Whatsapp or signal was kind of painful and telegram doesn't make it easy to use encryption with bots.
I kind of wanted to see if I can make a Matrix agent from scratch with Rust with GLM and it was surprisingly easy. Just make something for myself how I want it. Maybe I'll take a look on Hermes later...
Very interesting—Element X solved a lot of the pains of Element (iOS), could be a good solution!
I am seeing extremely positive results with Elixir too. Previously I was on Deepseek (deepseek-v4-pro) and GLM5.2 outperforms Deepseek easily. It's been a month since I used any native Claude models (simply because of pricing) but then, GLM5.2 is running for me at $20/day in usage on OpenRouter for GLM5.2. I am not sure if I've misconfigured Claude code or if this is indeed normal usage pricing. But, the output more than makes up for it. However, using Deepseek v4 pro directly from deepseek.com using their discounted pricing is insanely cost efficient. I topped up $10 a month and a half ago and I'm still yet to use up all the money in my account. Here's hoping that SOTA models become even cheaper!
Could you share more about the homelab project? Is it so you could message your local agent via Matrix and it can poke around the lab, check if services are up, restart VMs, that kind of thing? Would love to hear what you use it for, I'm thinking of building something similar for my lab.
Nice. I'm working on an agent too. How are you handling tool calls?
I followed this example
https://minimal-agent.com/
but I'm running into issues with nested backticks so I'm thinking of making dedicated close tags per tool call.
Are you sure fireworks is unquant? It's not listing precision on openrouter like everyone else.
> A typical session for me with GPT is usually over a hundred dollars.
I don't think a $100 session is "typical". I use GPT for months. $20/m plus plan is enough for my daily work.
I use an observability tool with claude code [1] that shows me usage including prompt and session cost. Even though I use a max subscription, it's interesting to see what it would cost me if I was using API directly.
My typical session ranges from $100-$400 - higher end when using workflows with lots of subagents. $100/session is expected when using the API without the subsidized subscription pricing. Most larger orgs have to use API pricing AFAIK.
[1] https://github.com/simple10/agents-observe
>Most larger orgs have to use API pricing AFAIK.
There are Business and Enterprise plans, both have discounting.
It's really interesting what "normal" is for folks. I use the $200/month Anthropic subscription and use it within a few percentages of my limit every week.
I'd blow through $20/month plan in hours.
Shorter sessions more often doing a /clear etc. save a shit ton of tokens. I pay 100 bucks a month but barely use 30% of it most weeks.
I have Claude max plan and the vscode claude dashboard plugin has logged about $4k worth of tokens in the past 2 months. I upgraded because I was using my weekly basic plan tokens in like 5 hours.
Likewise, I don't understand how anyone survives on the basic plans. It's funny seeing these two camps not understanding what the other is doing :)
$20 on API pricing or on subscription?
API, pay per token.
Why are you not using the subscription plan?
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Have you tried using DeepSeek V4 Pro instead? It will be cheaper and faster than GLM.
Why use an API when you can use a subscription though? Surely a $200 subscription is cheaper than using GLM 5.2 API?
Why are you spending on API for GPT coding instead of stacking 20x subs and using codex-lb?
Company pays API prices so we can use daily the best model for our job without being locked in. Also the team subscriptions started to be more like X per seat + usage...
Oh it sounded like personal use.
I understand the reasons to use team/enterprise accounts, but apart from the policy/management/billing side of it, I still don't understand the value in spending thousands for API instead of hundreds - even when there's argument that one provider is better than another depending on the use case, I don't think that credibly extends much beyond OpenAI + Anthropic frontiers, which both have $200 subs you can stack.
How are you using it? A subscription from z.ai?
Which harness did u use?
Opencode and Zed about 40/60.
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What makes you use API billing instead of a plan?
> This weekend I programmed a matrix bot with encryption and a Rust agent with some tools.
Did you program or did you gave the order to an agent to program?
Twenty dollars?
How are you comfortable spending that much to write something as simple as a matrix bot?
Are people doing this kind of thing just super rich or am I missing something?
It's pretty simple. There are things that I do because it's fun, like gamedev. I hand code that, and don't use LLM tools because I like learning and building. I do lots of utility stuff coding for my wife's business, most of that is stuff I could do in a few hours. It's worth $20 to not spend a few hours doing it. It's a cost benefit tradeoff. I won't learn much fixing WordPress themes or adding a feature to her web page, or setting up an automation for her, so I don't see the point of doing that.
Same thing for stuff at work. Oh, the tables/schema changed and my queries broke? I could dork around with spark and cypher for an hour, or I can tell claude to update the queries for the new schema. At the rate I am paid, spending on Claude tokens is generally a better use of my resources.
Building a net new solution? Coding tools take a back seat until I get the core logic right, then I let automation handle web page and UI scaffolding.
A lot of people spend $20 on a hobby for an hour of enjoyment a couple times a week. Not odd at all to do that for a few hours of coding if you find it fun. It could be a day pass at a bouldering gym or a yoga class or amortized running shoes/garmin/electrolytes.
Many factor to consider, really, but if it can build be a project while I'm in gym or walking around the city with my Fujifilm - 20$ is a good trade.
$20 is really cheap for the amount of work saved, considering you're in the US.
Is spending $20 considered "super rich"?
Recall that the marginal utility of money diminishes when you have more of it - when you have a lot of money it's easier to turn it into even more money, and vice-verca. It's not linear. So 20$ difference has exponential not linear influence on "being rich".
Yeah we're all doing this from our Super Yachts that performs Marine Biology research in its spare time.
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