> We use a bash-tool-only agent harness to evaluate 89 Terminal-Bench 2.1 tasks from the official repository, where resources are capped at 6 CPU cores and 8GB RAM.
This disqualifies the results. Each terminal bench task has a cpu upper limit and RAM upper limit. Overriding either is disqualification.
For reference, in tbench-2.1,
1. 0 out of 89 task allow 6 cpu cores (highest is 4, and i think only 1 task)
2. 8 out of 89 tasks allow 8GB RAM
This kind of shady benchmarking (I was talking about it just yesterday in a different context https://news.ycombinator.com/item?id=48838212) takes all joy out of building a harness to improve benchmark performance of a model because no matter what you do, you won't beat the headline (cheating) number. This is presumably why this model is not in the official benchmark leaderboard https://www.tbench.ai/leaderboard/terminal-bench/2.1
As an ex Meta employee, this is a little sad but not massively surprising. 'Number go up' is the core performance evaluation metric until PSC is done and you move on.
Why are resource limits considered at all aside from models accidentally fork bombing themselves?
I thought the benchmark was supposed to be about terminal use and specifically chaining together lots of bash tool calls. Which test cases does this matter for?
Yeah, I think it is definitely great. Having said that, I am still debating in my mind whether the volume of software engineers needed in the AI era is going to increase or decrease because of all of these advancements.
On the one hand, because it is easy to build products, more and more people will build. And more and more products and features will be built. However, a lot of people who are non-technical will also try to build, but they get stuck, and then they will need engineers. The sheer volume of product built by both experienced technical companies and non-technical novice startups and founders and wannabe founders is going to be massive. That is the bull case for having more software engineers needed in the near future.
On the other hand, in a year or so, people will build all these products, and most of them won't be able to market them, sell them and make money. Eventually, there won't really be a need for that many software engineers.
I think overall the bull case is probably going to win net net.
I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project.
I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?
> On the one hand, because it is easy to build products, more and more people will build.
And those people won't need to be software engineers.
> but they get stuck, and then they will need engineers
You've implicitly assumed here that the AI systems will always be worse than the average engineer. That is IMO myopic. I'm not sure that it's even true now let alone in the nebulous future.
> And those people won't need to be software engineers....You've implicitly assumed here that the AI systems will always be worse than the average engineer.
Most of what we do as engineers is precisely describe or analyze the behavior we want or the behavior we don't want. All other engineering skills that are useful are ultimately downstream from understanding the behavior of software enough to know which parts to keep, improve, or jettison. Chatbots can take care, somewhat, of analysis or expansion of instructions.... but they can't read minds. I don't see that changing any time soon.
I don't know who needs to hear this, but neither can humans.
You've implicitly assumed here that AI systems will always be worse at contextualizing and framing questions than the average engineer. I'm not sure that it's even true now let alone in the nebulous future.
You haven't narrowed the fundamental myopia of the assumption here, just dressed it in slightly different clothing.
By asking the user to explain what they want whenever there's ambiguity.
Plus all the other things that software engineers generally have not learned to a professional level even if they picked up the basics on the job by osmosis, because figuring out the customer's needs (and what they'll pay you for which may be different) is the job of a business analyst, a PM, or a UX Researcher, and those are different skills and may come with a Business Informatics degree rather than a CompSci one.
I don't understand what you mean. I can't build software I can't describe.
If you're implying chatbots can ask their "client" what to build, good luck with that—contractors are at least liable for what they produce and have extreme incentives to ensure that their clients are happy. To the extent of refusing to build anything if they don't know what they want....
> At least in China a lot of software developers are now struggling.
Do you think that Chinese software industry is that relevant to the kind of software market talked about on HN? I.e. lots of enterprise b2b and infra companies.
Chinese companies have always had a very low willingness to pay for software which kinda breaks the flywheel of B2B SaaS companies and companies to service those companies all the way down.
> Chinese companies have always had a very low willingness to pay for software
Are we still left with this mindset? Maybe once upon a time but it has definitely been changing.
There's plenty of B2B and enterprise SaaS companies in China serving the Chinese market. Maybe not as many, but no longer the very low of the past.
I also would not say enterprise were not willing to pay, even many years ago. It's the SME that refused to pay. Large CRM, ERPs etc have always existed.
I'm looking ahead to the next wave of open-weight models that are as efficient as DSv4 (which is really efficient), and have been heavily distilled on GLM 5.2 (which is trivial, given it is open weight)
> No wonder we still can’t get climate change under control
This is was historically a money issue, being green used to be wildly more expensive.
Now being green is cheaper, the limiting factor is how fast PV and batteries can be made or imported.
Recent reports of the sum of all US data centres currently in planning, has a power demand exceeding the (capacity-factor-adjusted!) global annual supply of PV.
This would be less of a problem if Trump wasn't trying to get in the way of anything green, or if the companies building data centres decided to also support factories to make more PV.
* Planned new demand: 300 GW; PV factory capacity ~ 600 GW nameplate, but the capacity factor is 14% so that's really 84 GW on average.
I would call the founders of DeepMind (Demis Hassabis, Mustafa Suleyman, Shane Legg) very smart people. Im pretty sure with the amount of funding everyone of these companies have, they have a long list of very smart researchers in their companies.
Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead and now with xAI and Meta at least delivered something that's competitive with useful models and cheap too. Granted, the narrative that the two leading labs are ahead still holds with Fable (and perhaps an upcoming GPT6), but it's not as over as common knowledge by the opinion leaders would have us believe.
> Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead
Not the way you're implying?
The GLM 5.2 hype was blowing way before this. Neither xAI nor Meta have really made a difference in a different way - similar results / similar pricing (to GLM 5.2).
Debugging and diagnosis is very tool call heavy, whether that's grepping / transforming logs, calling out to profilers/tracers, or even just writing up incident reports.
Bug diagnostics is about being okay at coding but better at tooling.
Given a good diagnostic report, it can be handed to opus for the fix.
Opus is okay at writing reports, but it still regularly gets table widths wrong in typst documents, leaving the last column full of text but only a handful of characters wide.
I wonder if we'll start to see that pattern with every new release. Tool use likely changes rapidly, so the newest, rather than most intelligent, model may always have an edge.
This sounds... kind of useless? Really good JSON or similar constrained decoder performance is interesting, but normal decoder > tool validator loop with good error message > tool retry is almost always able to get a tool to work second try, and input is cached so it's not expensive.
Yes, but each tool call has a different failure %. The tool calls that make up the majority of volume like grep are going to have nowhere near a 5% failure. A custom user-defined skill having a 5% failure rate is probably fine.
Interesting that neither meta nor xai chose to do open source given that they are both clearly behind Google, OpenAI and anthropic - and a serious us open source offering would give them a clear foothold.
I missed the fact that Meta was developing and releasing closed-weights models... bummer. Would be great to see some more progress with American open-weights models.
Good to see Meta finally back to releasing something at least worth evaluating. And it sounds like they did at least a bit skate to where the puck is going by focusing on tool and computer use.
Very strong pricing, cheaper than Grok 4.5, particularly the cached reads. We'll have to wait to see if it's actually worth using (it's not on OpenRouter yet).
Competition for cheaper and efficient models is a good thing, regardless of if you don't like SpaceX, Meta, etc. Especially from US based labs
I for one am really glad to get competitive models that will push the major labs to bring prices down. While Chinese open source labs are also great, unfortunately when it comes to US/Western political pressure it won't often have as much of a bearing on labs bringing prices down, especially for enterprises.
Also if these numbers are true, this is truly breaking ground finally for Meta.
Everyone has been loving to shit on the Alexander Wang acquisition but this seems legitimately impressive to me?
Meta's AI org when from a total mismanaged dumpster fire for multiple years to delivering a competitive model in less than a year on essentially their first try?
It's a high quality benchmark for sure, but it being public means it's at risk of leaking into the models (unintentionally or not), right? For that reason I prefer to look at the private ones, like: HLE, SimpleBench, Kagi, ARC-AGI.
Yes and Zuck effectively disbanded the entire team that did that. Not saying we shouldn't cast a critical eye on it, but it probably does warrant a second chance.
Lot more details in the linked report https://ai.meta.com/static-resource/muse-spark-1-1-evaluatio...
From Terminal-bench-2.1 details,
> We use a bash-tool-only agent harness to evaluate 89 Terminal-Bench 2.1 tasks from the official repository, where resources are capped at 6 CPU cores and 8GB RAM.
This disqualifies the results. Each terminal bench task has a cpu upper limit and RAM upper limit. Overriding either is disqualification.
For reference, in tbench-2.1,
1. 0 out of 89 task allow 6 cpu cores (highest is 4, and i think only 1 task)
2. 8 out of 89 tasks allow 8GB RAM
This kind of shady benchmarking (I was talking about it just yesterday in a different context https://news.ycombinator.com/item?id=48838212) takes all joy out of building a harness to improve benchmark performance of a model because no matter what you do, you won't beat the headline (cheating) number. This is presumably why this model is not in the official benchmark leaderboard https://www.tbench.ai/leaderboard/terminal-bench/2.1
As an ex Meta employee, this is a little sad but not massively surprising. 'Number go up' is the core performance evaluation metric until PSC is done and you move on.
Why are resource limits considered at all aside from models accidentally fork bombing themselves?
I thought the benchmark was supposed to be about terminal use and specifically chaining together lots of bash tool calls. Which test cases does this matter for?
Out of curiosity, how often are the resource limits the bottlenecks? What do harnesses do to help here - limit parallelism? More efficient tools?
The task could be verifiable in the environment so limiting its CPU and RAM could be to discourage brute forcing the answer.
I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too)
- Chinese models
- Grok
- Meta
- Google
- OpenAI
- Anthropic
I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.
Meta's local llama models used to be the face of open source AI. The scene has really changed.
they likely got the Peter Theil newsletter proclaiming open source models are the antichrist
Yeah, I think it is definitely great. Having said that, I am still debating in my mind whether the volume of software engineers needed in the AI era is going to increase or decrease because of all of these advancements.
On the one hand, because it is easy to build products, more and more people will build. And more and more products and features will be built. However, a lot of people who are non-technical will also try to build, but they get stuck, and then they will need engineers. The sheer volume of product built by both experienced technical companies and non-technical novice startups and founders and wannabe founders is going to be massive. That is the bull case for having more software engineers needed in the near future.
On the other hand, in a year or so, people will build all these products, and most of them won't be able to market them, sell them and make money. Eventually, there won't really be a need for that many software engineers.
I think overall the bull case is probably going to win net net.
I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project.
I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?
> On the one hand, because it is easy to build products, more and more people will build.
And those people won't need to be software engineers.
> but they get stuck, and then they will need engineers
You've implicitly assumed here that the AI systems will always be worse than the average engineer. That is IMO myopic. I'm not sure that it's even true now let alone in the nebulous future.
> And those people won't need to be software engineers....You've implicitly assumed here that the AI systems will always be worse than the average engineer.
Most of what we do as engineers is precisely describe or analyze the behavior we want or the behavior we don't want. All other engineering skills that are useful are ultimately downstream from understanding the behavior of software enough to know which parts to keep, improve, or jettison. Chatbots can take care, somewhat, of analysis or expansion of instructions.... but they can't read minds. I don't see that changing any time soon.
> but they can't read minds
I don't know who needs to hear this, but neither can humans.
You've implicitly assumed here that AI systems will always be worse at contextualizing and framing questions than the average engineer. I'm not sure that it's even true now let alone in the nebulous future.
You haven't narrowed the fundamental myopia of the assumption here, just dressed it in slightly different clothing.
> You've implicitly assumed here that AI systems will always be worse at contextualizing and framing questions than the average engineer.
How would they know what to ask or contextualize if they don't know what the user wants?
By asking the user to explain what they want whenever there's ambiguity.
Plus all the other things that software engineers generally have not learned to a professional level even if they picked up the basics on the job by osmosis, because figuring out the customer's needs (and what they'll pay you for which may be different) is the job of a business analyst, a PM, or a UX Researcher, and those are different skills and may come with a Business Informatics degree rather than a CompSci one.
Are you suggesting that psychic mindreading powers are real?
> How would they know
How would you? The answer is the same.
I don't understand what you mean. I can't build software I can't describe.
If you're implying chatbots can ask their "client" what to build, good luck with that—contractors are at least liable for what they produce and have extreme incentives to ensure that their clients are happy. To the extent of refusing to build anything if they don't know what they want....
At least in China a lot of software developers are now struggling.
I think for a lot of type of software we have now reached peak employment.
Someone payed a few k just for a normal website.
> At least in China a lot of software developers are now struggling.
Do you think that Chinese software industry is that relevant to the kind of software market talked about on HN? I.e. lots of enterprise b2b and infra companies.
Chinese companies have always had a very low willingness to pay for software which kinda breaks the flywheel of B2B SaaS companies and companies to service those companies all the way down.
> Chinese companies have always had a very low willingness to pay for software
Are we still left with this mindset? Maybe once upon a time but it has definitely been changing.
There's plenty of B2B and enterprise SaaS companies in China serving the Chinese market. Maybe not as many, but no longer the very low of the past.
I also would not say enterprise were not willing to pay, even many years ago. It's the SME that refused to pay. Large CRM, ERPs etc have always existed.
Its a signal. They were earning well and AI crashed the market in China.
To expand on Chinese models:
- DeepSeek
- GLM (Z.ai)
- Minimax
- Kimi (Moonshot)
- Hy3 (Tencent)
- Qwen (Alibaba)
(Each one of these with weights available to download and run locally)
GLM 5.2 is great, but is so rate limited now I no longer recommend it
Aren't there multiple providers for it? is it rate limited in all providers?
I'm looking ahead to the next wave of open-weight models that are as efficient as DSv4 (which is really efficient), and have been heavily distilled on GLM 5.2 (which is trivial, given it is open weight)
While data centers are still using lots of energy created from fossil fuels and many still evaporate water for cooling?
No wonder we still can’t get climate change under control
> No wonder we still can’t get climate change under control
This is was historically a money issue, being green used to be wildly more expensive.
Now being green is cheaper, the limiting factor is how fast PV and batteries can be made or imported.
Recent reports of the sum of all US data centres currently in planning, has a power demand exceeding the (capacity-factor-adjusted!) global annual supply of PV.
This would be less of a problem if Trump wasn't trying to get in the way of anything green, or if the companies building data centres decided to also support factories to make more PV.
* Planned new demand: 300 GW; PV factory capacity ~ 600 GW nameplate, but the capacity factor is 14% so that's really 84 GW on average.
Its the biggest technology race we have ever seen. Richest companies, smartest people, richest countries.
I do not know if competition is good, we will see in a few years.
Looking forward having a physical job for a change :D
A bit much describing our tech leadership as smartest people we've ever seen.
I would call the founders of DeepMind (Demis Hassabis, Mustafa Suleyman, Shane Legg) very smart people. Im pretty sure with the amount of funding everyone of these companies have, they have a long list of very smart researchers in their companies.
I do not mean Suckerberg or Eric Schmidt.
Greediest, perhaps?
The pricing is insane: $1.25/$4.5 for 1M tokens, and $0.15 for cached input!
https://dev.meta.ai/docs/getting-started/pricing-rate-limits
Meta isn’t right now on the radar for most folks picking models.
If they have a really good model, it makes sense to subsidise it, to gain users, before they align prices with competitors.
this is not subsidizing. this is way too expensive for a no-name model.
Cheaper than Qwen 3.7 Max. Second indication, after Grok 4.5 ($2 in / $6 out), that the BigLabs are feeling the GLM 5.2 heat.
Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead and now with xAI and Meta at least delivered something that's competitive with useful models and cheap too. Granted, the narrative that the two leading labs are ahead still holds with Fable (and perhaps an upcoming GPT6), but it's not as over as common knowledge by the opinion leaders would have us believe.
> Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead
Not the way you're implying?
The GLM 5.2 hype was blowing way before this. Neither xAI nor Meta have really made a difference in a different way - similar results / similar pricing (to GLM 5.2).
Why are the plans and pricing for all these products so complicated.
I don't know where I need to sign up to try it out. What is pricing? Is it API or subscription, what?
I had the exact same experience with Grok 4.5 as well.
My trust factor is gone with Meta right now. Has there been any independent analysis to confirm they didn't cheat on benchmarks again?
It seems to trade blows with GPT 5.5 and Opus 4.8 in performance while being cheaper than GLM 5.2.
> Model API is not available in your region.
:(
Well, Vietnam is not in the list of restricted territories.
Anyway, what is "your region" ?
Is this where I am now, or is it where I activated my Oculus 2 five years ago ?
How is every company able to show itself at the top of every benchmark?
Not much moat, incremental improvements, cherry picking models to compare.
To be fair, seems more correct to compare against similar strength models if your main edge is pricing.
Their published benchmarks seem to indicate that it's pretty good at coding and multimodal, but VERY good at successful tool calls.
What kind of use case would be best for that shape?
Debugging and diagnosis is very tool call heavy, whether that's grepping / transforming logs, calling out to profilers/tracers, or even just writing up incident reports.
Bug diagnostics is about being okay at coding but better at tooling.
Given a good diagnostic report, it can be handed to opus for the fix.
Opus is okay at writing reports, but it still regularly gets table widths wrong in typst documents, leaving the last column full of text but only a handful of characters wide.
I wonder if we'll start to see that pattern with every new release. Tool use likely changes rapidly, so the newest, rather than most intelligent, model may always have an edge.
This sounds... kind of useless? Really good JSON or similar constrained decoder performance is interesting, but normal decoder > tool validator loop with good error message > tool retry is almost always able to get a tool to work second try, and input is cached so it's not expensive.
Things are not always that simple, eg https://lucumr.pocoo.org/2026/7/4/better-models-worse-tools/
The avg coding session has hundreds or thousands of tool calls. Even a 5% failure rate noticeably notches up token use and cost. See Gemini.
Yes, but each tool call has a different failure %. The tool calls that make up the majority of volume like grep are going to have nowhere near a 5% failure. A custom user-defined skill having a 5% failure rate is probably fine.
Interesting that neither meta nor xai chose to do open source given that they are both clearly behind Google, OpenAI and anthropic - and a serious us open source offering would give them a clear foothold.
I missed the fact that Meta was developing and releasing closed-weights models... bummer. Would be great to see some more progress with American open-weights models.
Good to see Meta finally back to releasing something at least worth evaluating. And it sounds like they did at least a bit skate to where the puck is going by focusing on tool and computer use.
Very strong pricing, cheaper than Grok 4.5, particularly the cached reads. We'll have to wait to see if it's actually worth using (it's not on OpenRouter yet).
That's what one does when its product and public perception is way behind competitors.
How are people trying this? I don't see it on openrouter. Any ways of testing this without subscribing to meta stuff?
Probably need to wait some hours/1-2 days and openrouter will add it.
Thanks. I was asking because I couldn't find even their previous 1.0 model there.
Yeah, no thanks. I cannot think of a worse company to trust with additional personal data.
Me neither, though LLMs also provide services that don’t involve personal or sensitive data
Competition for cheaper and efficient models is a good thing, regardless of if you don't like SpaceX, Meta, etc. Especially from US based labs
I for one am really glad to get competitive models that will push the major labs to bring prices down. While Chinese open source labs are also great, unfortunately when it comes to US/Western political pressure it won't often have as much of a bearing on labs bringing prices down, especially for enterprises.
Also if these numbers are true, this is truly breaking ground finally for Meta.
Everyone has been loving to shit on the Alexander Wang acquisition but this seems legitimately impressive to me?
Meta's AI org when from a total mismanaged dumpster fire for multiple years to delivering a competitive model in less than a year on essentially their first try?
Not their first try. There’s been reporting about how they’ve kept pushing their model releases back because of underwhelming performance.
How is it their first try? They were leading the race with Llama 3.x a few years ago.
They were leading the race in a niche category a few years ago. Now they are, according to some benchmarks, even on the right playing field.
Considering the DeepSWE result (imho if you're gonna give value to benchmarks this is one of the best) it's not good enough.
It's a high quality benchmark for sure, but it being public means it's at risk of leaking into the models (unintentionally or not), right? For that reason I prefer to look at the private ones, like: HLE, SimpleBench, Kagi, ARC-AGI.
Is this the model trained on Meta "draftees"? Are we seeing this in the jump on JobBench?
A lot of these benchmarks are unfamiliar. Are labs just choosing the ones that make them look best?
This is not open-weights, right?
Correct
Meta is back in the game, albeit not at the top. Impressive stuff, nonetheless.
Weren't they caught multiple times gaming the benchmark even more so then the rest?
Yes and Zuck effectively disbanded the entire team that did that. Not saying we shouldn't cast a critical eye on it, but it probably does warrant a second chance.
Zuck was part of that team.
Let me assure you, literally everybody does this
They are not open source anymore, right?