I seriously dont' know all this big hullabaloo about one shot prompting.
by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus.
I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventions in the human reviewed spec.
Id rather see performance in agent loops against human defined objectives where it can be verified to stick to defined guardrails and continue without drift till its objectives are complete.
I'd also like to see it identify bugs and potential performance increases by identifying existing code and suggesting refactors based on context it can pickup about the particular use case you are trying to create.
These are way more valuable metrics than "hey build X"
It's true that no one is trying to one shot anything serious right now, but it's still an important metric. Claude Code and Opus really took off when they improved the harnessing enough that it would self-correct many of its mistakes without needing user input. In fact I think long-term autonomy (in the range of several hours) and self-correcting is going to be where we see most improvements in coming years.
The argument is flawed, there is no logical reason to assume a single prompt won’t be sufficient to constitute the complexity of a software project. It may not be practical in many cases but there is too much variability in what is considered a complex software project and in the sufficiency of instruction in a single prompt to make that claim and say it’s “by definition.”
When the model produces reasonable results from one prompt, you could assume that it will also return reasonable results through the follow up prompts.
"We did multi-shot prompting to try and get these two games into comparable states using these two different models."
"Well obviously you provided better follow-up prompts to the one that came out better."
Also nothing about human-provided plan files and guardrails preclude the one-shot benchmark test. Heavens, I almost said "real coding," but in "real agentic program creation" you'd obviously be doing multi-turn interaction with the agent, but how can you provide a fair test when the model's output n determines your n+1 response?
Sure, real-world usage is always more difficult to benchmark, but the additional issue with the one shot prompting benchmark is that by optimizing for it, models are nudged towards making all those assumptions they shouldn't really make. Maybe a better test would be to have a fully spec'd-out plan, but start with a one shot, high-level prompt and expect the agent to discover your preferences by repeatedly asking for clarifications. The system that manages to suss out more of the details in the hidden spec this way, in less steps and with less unnecessary questions would more likely to be a truly well-calibrated agent.
The thing with one-shot prompting is that it tests the ability for the model to make good choices on its own, rather than only instruction following.
Instruction following has been down for years, and while there are of course metrics that continue to improve as the frontier advances (for example, the ability to continue following the original instructions even as context grows), you can't really get that much better at performing a list of instructions as-written if the instructions are sufficiently precise enough that there's no wiggle room for interpretation (which seems to be what you are describing).
For example, one of the things that got me the most excited for Fable 5 was its ability to work for over eight hours straight on a single instruction and seemingly faithfully the entire time. That was something I observed personally after trying out the same workflow that runs for maybe two or three hours with Opus and then still needs followups. Fable needed no followups. That's a game changer for me compared to the prior state of the art.
That kind of stuff is going to end up being the most beneficial to people who are touching the edges of their knowledge or even exploring completely new areas. And that type of work is exactly the kind of work that makes agentic coding so powerful, even as much as it gets harder to judge the quality of the work when you lack the skills yourself. It's a good thing that the quality increases across the board, even for skilled practitioners.
For example, even people who know how to write inference engines or how matmul kernels work or how to optimize model architecture can't always predict just the sheer breadth of things agents can try to improve performance, and sometimes you get over some wall and reach a completely different optimum that you just wouldn't have reached in any reasonable amount of time by applying traditional knowledge even if you're an expert in the field.
That kind of stuff is amazing. And that's exactly the kind of stuff that one-shot prompting is testing for. It's kind of like testing for the model's "innovation", as much of an oxymoron that is.
So GLM emits fewer tokens and does fewer tool calls, but still takes over twice as long to complete.
Can someone explain to me where that time usage is coming from if not from the model operation itself?
Are the individual tool calls more complex and take more time to complete? Or is the rate of tok/s lower because the model does more compute per token?
> So we ran it head-to-head against Claude Opus 4.8: same one-shot prompt, build a 3D platformer in raw WebGL from scratch
Running a single one-shot prompt is not a benchmark, not is it representative of any sort of real-world usage.
Most agent usage is collaborative so you need to test things like reliability (when I delegate a task, does it complete it without making up test results for e.g.) and steerability (does it obey my instructions or does it just do what it thinks is best).
Hi, I am the author, I completely agree! I set out to run a vibe test on this one, not a benchmark, the real benchmarks are listed. My test shows what the models can do when both tasked with a long-running, technically difficult, one-shot task.
I think your test you describe (collaborative, task delegation, task completion, TTD, steerability) is a great format for a future test that I will definitely try out.
Thanks, I didn't mean to be brusque, but I have seen a lot of these vibe tests lately that come to grand conclusions like "X model is better than Y" from the result of a single prompt.
Appreciate you sharing the results of your tests though!
On the other hand, I did just leave my pi agent running GPT 5.5 overnight on a clearly defined, long running task. It's been running about 10 hours now and it's mostly done. So this kind of use case is also valid.
Thinking about it, I would say that the majority of agentic work I do, by a long shot, is subagents which are launched from the main session, using a prompt of its choosing. Those could be considered short versions of these fully autonomous tasks.
Yes, part of the reason I chose the one-shot test was really to test long-running tasks. A lot of people seem to be experimenting with this format, for example in the now trending loop-writing workflows. And really I am interested in diving into the murky waters of these novel workflows.
sure that's why we look at a mix of formal benchmarks, one longer analysis of a side-by-side, and various other people who we trust to form an opinion, all covered in the article - not intended to be a formal benchmark, there are enough of those.
GLM 5.2 has one big issue that will limit its meaningful success and that's the value of their coding subscription.
Yes, in terms of API pricing, GLM 5.2 outperforms the competition. But the only people that use API billing for their coding work are large corporations, where these highly subsidized subscriptions are being fazed out.
At the same time, none of these companies will use a Chinese coding subscription for their employees.
For individuals and smaller teams, Z.ai's coding subscription is outperformed by Anthropic and OpenAI. You probably get around the same usage with Claude, but Codex definitely offers more usage for the amount you pay.
We can have a debate how much Z.ai closed the gap to GPT5.5 and Opus 4.8, but if I can freely decide between them in a world where they all cost the same, I simply wouldn't choose GLM.
So the important question becomes: How good will the offering from Z.ai get with GLM 5.3 or 6 and how much will OpenAI and Anthropic cripple their current offering in the near future.
I've signed up with Ollama to experiment with these open source models. For the past 3 months, it's just been experimenting, trying it out. GLM is the first model that I am using on a daily basis to do my coding work (as well as using Claude). It's good - I've been maxing out my Ollama usage limits everyday :)
I've been checking out GLM 5.2 on some projects and few thoughts on it:
- it takes it sweet time to get code rolling, not the fastest model by any means
- it strays a lot during discovery/planning but then corrects
- it's not steering friendly, as it hallucinates things that it doesn't follow later on
- its output is quite good
A sample use case: I was optimizing rendering on Swift+Zig codebase. It chocked on 5k data entries.
GLM 5.2 spent 20 minutes building the benchmarks and getting data out, which made me frustrated so I blocked non-editing tool access and went AFK, after approx. 30 minutes I found that it used already-made benchmarks and some "conclusions" to optimize 3 choke points. Output pointed that it couldn't validate suspicions and asked for more data.
Implementation worked well, it was idiomatic and non-intrusive. I would even say that it was more idiomatic than GPT 5.5 effects on same repo.
I would opt in in using it more BUT GPT usually completes same requests 5x faster.
GLM 5.2 was spark for preparing and running inside isolated containers with JJ workspaces (so that multiple can be ran in parallel).
Cost difference matters most as cost optimization is the whole point of AI. Time difference (30 min vs 1 hr) is not a deal-breaker. The small precision gap on the first iteration does not matter for 99% of the work that happens in real world.
Totally agree witg the general assessment.
The biggest problem with Z.ai model for a long time is not quality, but the inference speed and general capacity availability.
Hopefully with this recent hype, there will be more provider on openrouter for 5.2.
> GLM-5.2 cost a fraction as much. Opus finished in half the time and shipped a cleaner game.
Off topic, but does anyone else instantly pick up on LLMisms like this? It seems like all the models have converged on this style of writing, and improvements aren't really changing it.
Yep, as I reread my own sentances I notice these LLMisms and have to rewrite them quite often. Reading so much llm-output definitely impacts your writing style.
> Through an API it costs a fraction of Opus, and you can run it yourself for free if you have the hardware.
I haven't been keeping up on hardware costs for state of the art LLM inference, but this remark made me ask myself how many readers of the article would actually be able to run this model on hardware they own. How much would it cost to acquire such a setup?
This framing local LLMs as free is stupid. Basically pay 100+ months worth of API costs up front isn't free in the slightest. And it will be slower than non-local, your hardware will be outdated in 12 months and probably won't be able to run SOTA at anywhere near non-local speed in max 20 months
Yeah, it glosses over a gigantic capital expenditure. It's sort of like saying that an open source modern CPU architecture allows you to build your own CPU "for free" (provided that you own and operate a fab).
I used GLM 5.0/5.1/5.2 for some projects, and for me, the area in which they lag behind frontier models the most are user interfaces. They get really close to Opus when it comes to pure algorithms, but when I need something like web application or a mobile app that looks and works well, they are very noticeably worse than even Sonnet.
I wonder how much tokens and time where used for the verifying part.
Maybe GLM 5.2 instantly found the "solution" to read the screen pixel by pixel, but it could also have been a major token and time consumer.
Having issues with coding a render for good looking realistic smoke coming off burning incense, opus 4.8 & gpt-5.5 both have code issues, glm-5.2 did it. Amazing.
The real time 3d fluid dynamics appear to be the tricky part, I wish I still had opus access, would love to see if it can do it.
I've seen glm 5.2 struggle writing simple compilable c code. It might be good at web, but it's world knowledge is limited due to the small model size, making it's use quite limited in my opinion.
>On output tokens, GLM-5.2 is less than a fifth the price of Opus.
Opus is most expensive model in pay as you go model, but IMO fair comparison should include subscription price as well. For example when one has $100 Claude Max and use it up through the month, it might not be more expensive than GLM, or at least not 5x.
Yes this is true. This test was run on a $20 pro Claude subscription. I would definitely love to try use both models on the highest plans for a whole month and compare the two, great format for a future head-to-head comparison.
GLM-5.2 is quietly becoming the most interesting open model release this year. The coding benchmarks are surprisingly close to frontier models at a fraction of the inference cost.
We've had the great small Qwen 3.6 early April that many could actually run on their laptop. Then similar from Google a few weeks later (Gemma4, better in prose, worse in code). Then the super cheap large Deepseek V4 a few weeks later. Then antirez DS4 build that made that actually runnable on MacBooks and Mac Studios. And now the "near-frontier / near-Opus" GLM 5.2.
For people who follow open LLMs, none of these were quiet and all were the most interesting open model release for a few days/weeks. In one or two months, it will be some other model again. Now I do appreciate the real rapid improvements in open models. But there's also a ton of hype and fast-fashion around all of this.
To me DS 4 is still the most interesting due to much lower costs. Also DS 4 training isn't done yet.
From my Opus vs DS 4 Pro personal benchmarks, 16 different real-life work tasks, DS 4 has performed as well as Opus 4.8 high overall but with few drawbacks:
- on the 16 tasks, one needed several prompts to be steered back into the topic
- its review capabilities seem much worse
- DS4 had the cleanly better solution in 3 cases out of 16, with Opus "only" doing cleanly better 2 times out of 16. But still, I want to emphasize, is the worst case scenarios that imho matter the most, not the best ones, and on that front Opus outperformed.
That being said I spent less than 2$ of API working 4 days, which is more or less what I would've spent with Anthropic APIs for less than one task.
Seeing the results I don't see how the results are even comparable Opus is clearly far superior in most aspects. Smoothness, design, functionality etc.
At the end of the day, the time earned is more important then the cost for big players.
The ability to spawn 10 claude agents and rush a project to outcompete someone is more important for big businesses in my imo. Also the small details that GLM missed would take significant more time to iron out, considering it already took double the time.
I do hope other (open weight) models catch up, but to act like they are anywhere close for me is a bit disingenuous.
Not my experience at all, I have written about comparing DS4 vs Opus 4.8 on 16 real life work tasks on multiple posts.
Also, every single lab does RL on benchmarks, which is why Opus 4.6 was the last truly great assistant, after it, all models tend to drift into implementation asap.
by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus.
I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventions in the human reviewed spec.
Id rather see performance in agent loops against human defined objectives where it can be verified to stick to defined guardrails and continue without drift till its objectives are complete.
I'd also like to see it identify bugs and potential performance increases by identifying existing code and suggesting refactors based on context it can pickup about the particular use case you are trying to create.
These are way more valuable metrics than "hey build X"
"Well obviously you provided better follow-up prompts to the one that came out better."
Also nothing about human-provided plan files and guardrails preclude the one-shot benchmark test. Heavens, I almost said "real coding," but in "real agentic program creation" you'd obviously be doing multi-turn interaction with the agent, but how can you provide a fair test when the model's output n determines your n+1 response?
Instruction following has been down for years, and while there are of course metrics that continue to improve as the frontier advances (for example, the ability to continue following the original instructions even as context grows), you can't really get that much better at performing a list of instructions as-written if the instructions are sufficiently precise enough that there's no wiggle room for interpretation (which seems to be what you are describing).
For example, one of the things that got me the most excited for Fable 5 was its ability to work for over eight hours straight on a single instruction and seemingly faithfully the entire time. That was something I observed personally after trying out the same workflow that runs for maybe two or three hours with Opus and then still needs followups. Fable needed no followups. That's a game changer for me compared to the prior state of the art.
That kind of stuff is going to end up being the most beneficial to people who are touching the edges of their knowledge or even exploring completely new areas. And that type of work is exactly the kind of work that makes agentic coding so powerful, even as much as it gets harder to judge the quality of the work when you lack the skills yourself. It's a good thing that the quality increases across the board, even for skilled practitioners.
For example, even people who know how to write inference engines or how matmul kernels work or how to optimize model architecture can't always predict just the sheer breadth of things agents can try to improve performance, and sometimes you get over some wall and reach a completely different optimum that you just wouldn't have reached in any reasonable amount of time by applying traditional knowledge even if you're an expert in the field.
That kind of stuff is amazing. And that's exactly the kind of stuff that one-shot prompting is testing for. It's kind of like testing for the model's "innovation", as much of an oxymoron that is.
Since Opus 4.6 I've seen later Anthropic models being more and more capable on one hand, but also less useful on multi turn open tasks.
It feels like with each model they are more and more prone to go "their own way" and jump into the implementation as soon as they can.
I can't but blame it on benchmarks and fine tuning around prompt-to-solution work.
Can someone explain to me where that time usage is coming from if not from the model operation itself?
Are the individual tool calls more complex and take more time to complete? Or is the rate of tok/s lower because the model does more compute per token?
Running a single one-shot prompt is not a benchmark, not is it representative of any sort of real-world usage.
Most agent usage is collaborative so you need to test things like reliability (when I delegate a task, does it complete it without making up test results for e.g.) and steerability (does it obey my instructions or does it just do what it thinks is best).
I think your test you describe (collaborative, task delegation, task completion, TTD, steerability) is a great format for a future test that I will definitely try out.
Appreciate you sharing the results of your tests though!
Thinking about it, I would say that the majority of agentic work I do, by a long shot, is subagents which are launched from the main session, using a prompt of its choosing. Those could be considered short versions of these fully autonomous tasks.
You make a very strong claim at the end that the hype is mostly real, and making it clear to what extent your claim holds should help the reader.
Yes, in terms of API pricing, GLM 5.2 outperforms the competition. But the only people that use API billing for their coding work are large corporations, where these highly subsidized subscriptions are being fazed out.
At the same time, none of these companies will use a Chinese coding subscription for their employees.
For individuals and smaller teams, Z.ai's coding subscription is outperformed by Anthropic and OpenAI. You probably get around the same usage with Claude, but Codex definitely offers more usage for the amount you pay.
We can have a debate how much Z.ai closed the gap to GPT5.5 and Opus 4.8, but if I can freely decide between them in a world where they all cost the same, I simply wouldn't choose GLM.
So the important question becomes: How good will the offering from Z.ai get with GLM 5.3 or 6 and how much will OpenAI and Anthropic cripple their current offering in the near future.
- it takes it sweet time to get code rolling, not the fastest model by any means
- it strays a lot during discovery/planning but then corrects
- it's not steering friendly, as it hallucinates things that it doesn't follow later on
- its output is quite good
A sample use case: I was optimizing rendering on Swift+Zig codebase. It chocked on 5k data entries.
GLM 5.2 spent 20 minutes building the benchmarks and getting data out, which made me frustrated so I blocked non-editing tool access and went AFK, after approx. 30 minutes I found that it used already-made benchmarks and some "conclusions" to optimize 3 choke points. Output pointed that it couldn't validate suspicions and asked for more data.
Implementation worked well, it was idiomatic and non-intrusive. I would even say that it was more idiomatic than GPT 5.5 effects on same repo.
I would opt in in using it more BUT GPT usually completes same requests 5x faster.
GLM 5.2 was spark for preparing and running inside isolated containers with JJ workspaces (so that multiple can be ran in parallel).
Off topic, but does anyone else instantly pick up on LLMisms like this? It seems like all the models have converged on this style of writing, and improvements aren't really changing it.
My only, I guess feedback, is that it's not really clear about the price.
Would the 21.92 be the API pricing I guess?
Cost $5.39 (real billed) ~$21.92 (estimate, list pricing)
I haven't been keeping up on hardware costs for state of the art LLM inference, but this remark made me ask myself how many readers of the article would actually be able to run this model on hardware they own. How much would it cost to acquire such a setup?
I am not sure where this is going to lead us but it is fun to watch.
The real time 3d fluid dynamics appear to be the tricky part, I wish I still had opus access, would love to see if it can do it.
Opus is most expensive model in pay as you go model, but IMO fair comparison should include subscription price as well. For example when one has $100 Claude Max and use it up through the month, it might not be more expensive than GLM, or at least not 5x.
https://z.ai/subscribe
I’m currently trying to figure out whether a downgrade from Max 5x to Pro in combination with one of those would save me money and if so, how much.
And z.ai themselves also have subscriptions.
I think it's most fair to compare the plain token pricing that is used by everyone.
As a consumer, yes, it's totally fair. All that matters to me is the price I pay at the pump, not whether that price is "real" or not.
And Anthropic have claimed they expect their first profitable quarter this year. They may have bigger margins on their raw API than you realise.
For people who follow open LLMs, none of these were quiet and all were the most interesting open model release for a few days/weeks. In one or two months, it will be some other model again. Now I do appreciate the real rapid improvements in open models. But there's also a ton of hype and fast-fashion around all of this.
From my Opus vs DS 4 Pro personal benchmarks, 16 different real-life work tasks, DS 4 has performed as well as Opus 4.8 high overall but with few drawbacks:
- on the 16 tasks, one needed several prompts to be steered back into the topic
- its review capabilities seem much worse
- DS4 had the cleanly better solution in 3 cases out of 16, with Opus "only" doing cleanly better 2 times out of 16. But still, I want to emphasize, is the worst case scenarios that imho matter the most, not the best ones, and on that front Opus outperformed.
That being said I spent less than 2$ of API working 4 days, which is more or less what I would've spent with Anthropic APIs for less than one task.
At the end of the day, the time earned is more important then the cost for big players.
The ability to spawn 10 claude agents and rush a project to outcompete someone is more important for big businesses in my imo. Also the small details that GLM missed would take significant more time to iron out, considering it already took double the time.
I do hope other (open weight) models catch up, but to act like they are anywhere close for me is a bit disingenuous.
Also, every single lab does RL on benchmarks, which is why Opus 4.6 was the last truly great assistant, after it, all models tend to drift into implementation asap.