Weak spots (categories it fails):
- Trivia — 0/3 - basically not much built-in knowledge
- Combined tool-calling tasks — score 45/100, sometimes makes invalid tool calls
- Puzzle Solving — score 77, flubs carwash-like tests
z.ai doesnt always have the most reliable AI
but I don’t mind the party seeing my trade secrets and thoughts compared to an American corporation + the party seeing my trade secrets and thoughts. So thats not a functional difference to me, and the Chinese one won’t reply to subpoenas so thats a value add tbh
So I’ll consider all, fastest tokens/sec wins
In practice, I tend to just use the default on Claude Code that works well enough. But I wonder to what degree other users really play around with these settings to optimize for their project.
I'm not going to play around with thinking level every request because the goal is to make me save time not spend it in a different setting menu.
Playing around with learning the differences is incredibly helpful to schedule on ones calendar weekly for an hour or two, while saving links throughout the week to try out.
Understandable frankly.
- For Claude.ai subscriptions I think Sonnet is much cheaper than Opus. This is why there was a "Sonnet only" usage bar for Max tier for the longest time.
- For some tasks the sheer amount of raw input tokens is the most important. For example multimodal computer use tasks. You can't make them any more efficient on Opus by turning down the reasoning, so a cheaper model like Sonnet is useful for them
it's still there. I still don't totally grok why I can't use all my tokens on Sonnet if I want to... maybe that signals something?
[0]: https://aibenchy.com/compare/anthropic-claude-sonnet-4-6-med...
It makes some sense, as models are trained more and more with reasoning, than without.
[0]: https://aibenchy.com/compare/anthropic-claude-sonnet-4-6-non...
I don't really believe this however, because so much time is spent fixing up after models, that a slower but more intelligent model is a net time saver in my experience.
The graphs show parts of the cost/performance pareto frontier occupied by Opus 4.8 and others occupied by Sonnet 5.0. If Opus 4.8 was strictly better at cost per task like you say, by definition the entire frontier would be occupied by Opus.
So neither is pareto-dominant over the other. In contrast, Sonnet 5.0 is Pareto-dominent over Sonnet 4.6 on those graphs.
But the entire frontier is occupied by Opus under any reasonable interpolation scheme (piecewise linear which is what they've done, and most reasonable spline or polynomial fits would also lead to the same result) over the overlapping x values for which both are defined.
Under that interpolation scheme, for x > ($ cost of Opus low effort), Opus is Pareto-dominant over Sonnet 5. You can see this by picking any point on Opus's interpolation and realizing that you get strictly worse by switching to Sonnet for the same x value or the same y value. Meaning if you want to pay the same $x then you get a worse y, or if you want the same y you pay more $x.
If you mean extrapolate, at that point you're just making up data. The available effort levels are discrete and covered totally by the benchmarks. You can draw on the monitor with a sharpie to show a "ultra-low" effort level for Opus that scores better than Sonnet "low" at the same price, but it doesn't magic the ultra-low effort into actual existence.
(Anyway, the blog post now has an errata and a graph that shows substantially better relative performance for Sonnet 5.0 than the original graph.)
It was a claim that applies to a range of x-values where both curves are defined.
Of course if you go beyond those x-values where only one of the two are defined, then trivially the one that is defined constitutes the Pareto frontier in that region. Which is what I understand to be your point?
You could make it true by artificially dropping some of the data points, but, like, why?
(Again, this is moot given the updated graph.)
> Of course if you go beyond those x-values where only one of the two are defined, then trivially the one that is defined constitutes the Pareto frontier in that region.
Not so! It's only sound to do that at the low end of the cost axis (x) or the high end of the performance axis (y). You can't do it at the low end of the performance axis or the high end of the cost axis.
However, I am also confused about market positioning. Too expensive to perform daily tasks - open souce models are much cheaper - and not frontier model to address complex real world problems.
Rarely used Sonnet btw.
There is a real advantage, especially for businesses, in using an off the shelf solution from a corporate provider.
Personally, the advantage of not having to set up multiple solutions from multiple sources outweighs the cost of a $20 a month subscription. Think about why a lot of consumers prefer Apple devices over Linux. There are a lot of advantages to Linux, but "never having to think about my tools" is its own advantage.
The graph shows that Opus is cheaper than Sonnet for the same performance. Unless I am suffering a cognitive blindness thing right now.
Alternatively you can draw a horizontal "constant performance" line and see that Opus is cheaper for a given performance level.
It would be great to see these charts with the promotional pricing just because it’s here for about two whole months.
I guess I could get Sonnet 5 to do it.
Does anyone else have any review token saving measures?
Assume it to get deprecated sooner rather than later.
I guess it's probably a lot cheaper for them to run, and it cuts costs for them. Seems disingenuous, though.
I have been using Sonnet 4.6 more than Opus, because I'm mostly doing agent-assisted development and not fully agent-driven development. This announcement does not make me positive, I have found that the more models are optimized for fully agentic development, the worse they get at assisted development and often start doing too much despite very strict/specific instructions.
I have been moving more and more to K2.7 Code and GLM-5.2 the last few weeks. They are often good enough for assistance, very fast, and cheap.
Trouble is, everyone inside their buildings seems to believe that no one will be working like that in a year or two.
Offhand, I’m not even certain whether a model like that could justify the constant retraining we’re doing on the agentic models.
It doesn’t make a lot of sense to spend millions or billions on training to reduce hallucinations by 0.3% if your model assumes a human is in the loop to course-correct them.
This source claims that knowledge workers alone (probably because they are paid much more) account for 35 - 50 Trillion of that: https://github.com/danielmiessler/Substrate/blob/main/Data/K...
If LLMs can boost their productivity even by an average of 5% (studies from ~2024 put it in the ~30% range depending on task) that is ~1.5 - 2.5T in value annually. Even if the AI industry can capture a fraction of that, that is a huuuge monetization opportunity.
Note, at 5% productivity boost, humans are not just in the loop, they are the loop. AGI or large-scale replacement of humans is not even needed, but the financial opportunity is already immense, and it scales with how much human productivity can be improved (i.e. how much work can be offloaded to LLMs.)
Now, I don't think AGI will happen soon (or has already happened, depending on how you define it) but I do think humans will be a much smaller part of the loop and large-scale job displacement will happen once companies figure out how to properly use AI.
At this point, the financial upside for the AI industry is extremely high but will be limited by the social turmoil that will inevitably ensue (which we're already seeing brewing in the data center backlash.)
The studies I've seen recently (at least in the software space) put it at something like a 10% increase in coding speed, which for me would probably translate to something like a 3% increase in productivity. I spend a lot more time on things like getting agreement between teams, documenting approaches to things that don't exist on the wiki, etc, that LLMs are significantly less effective at. Or just can't do; no one will be happy if I send an LLM instead of me to meetings.
I suspect a lot of roles are like that. They give a 10-30% boost to the core role function, but that core role is still only 30-50% of what you do.
> that is ~1.5 - 2.5T in value annually
That seems really large, but it's ~2-3x Walmart's yearly revenue, and OpenAI and Anthropic both have estimated valuations that compare to Walmart's market cap. And this is before we consider that they need to do it for cheaper or why would anyone bother. Realistically, potential revenue is probably half that at best.
It's also before cutthroat pricing really kicks in. People are willing to pay for Claude right now; I still suspect that as time goes on people will start looking towards Deepseek/GLM/etc models that provide 95% of the performance at 10% of the price. That'll cut the market even further.
The question is how much demand for knowledge work swells as prices fall, and whether that's a soft landing or a crash.
The frontier labs, on the other hand, are thinking about replacing all human labor, ending death, and the risk of it causing human extinction. Most of the apparatus we're talking about approach it very parochially; it's almost like they're embarrassed to take the grander ideas even a little seriously, for being too nerdy/sci-fi.
They'll show up after the fact and whinge endlessly about how they should have been involved.
Pre-bubble pricing: $1400 gets a 128GiB iGPU optimized for inference. Glm and kimi need 800-1000GiB. Call it 1TiB. The $1400 boxes could be ganged into sets of 4-8, with a switch. Call the switch $1000.
Each box has a TDP of 250W. 8 x 250/120V = 16.666A, or one household circuit in the US, so no new power infrastructure is needed.
$1400 x 8+1000=$12,200. Assuming standard five year depreciation, that’s $2440 a year. There are a billion knowledge workers alive today. So that’s $2.4T annual revenue. Average net profit margins on computer hardware are 4.3%. That works out to $105B net income, globally.
So, I guess the question is whether the (currently #2) open weight models provide $1.4-2.4T less value per year than the #1 and #3 models, and, if so, if customers can measure this, or are willing to spend 2x more and deal with censorship, data theft, intentional enshitification, sabotage, ads, product placement, etc, to get the slightly “better” model.
Also, note that my numbers assume moore’s law stopped for all time in 2024, but we’ve seen HW improvements since then.
Minus the cost of inference, that might not be the boon you're making it out to be. I hear what people around here are spending on their api and I'm skeptical that these tools are making me that much more productive.
Personally, for assisted development, I haven't seen much progress in a while.
I think this sort of thinking is a trap, because it presumes that all software has the same constraints.
There's a spectrum of requirements between "chuck this over the wall at Claude, it only has to work once" and "this is a literal rocket ship, formally verify the whole thing".
I've made some things with Claude I don't understand and don't control. It's fine, they're still useful to me. Things for the house that I wasn't going to build manually, some dashboarding stuff and scripts for work, stuff that can crash and burn and I'll be fine.
They won't justify trillions in investment, but they are useful.
Equally, I do agree with you on some things. Sometimes I hand-hold the LLM or forgo it entirely because I want to be 100% sure I know how something works, and can justify a decision if it causes a production outage.
I think the future is probably multiple different tools with different goals. Better IDE integration for some uses, an entirely separate "LLM herd controller" kind of thing for when you're okay with vibe-coding, and the most interesting is something in the middle where you're more in the loop than pure vibe-coding, but don't see the full context like in an IDE. Something where it surfaces changes to key components, but hides things like test changes.
That's a key point. Keeping knowledge and know how inside the company is strategic. For most people GPS did not result in better sense of direction, spellchecking did not help to write without making mistakes, and delegating translation to deepl does help to be better in a foreign languages. I don't see the gain for an individual, a company, a society if a technology reduces the ability to think, do stuff, understand complex problem, working hard at something. Hiring junior also matters, what is boring for a senior dev is useful for a junior, like the "wax on wax off" in Karatekid. Then when the senior dev retired the junior is not junior anymore and the know how is still here. I want to to transfer my knowledge to a junior, not to anthropic or google or openai.
Ideally, working hand in hand with an AI could be like driving a motorcycle vs riding a bicycle. Both are fine, but you go much faster with a motorcycle and you don't lose any ability. But prompting a motorcycle auto-pilot by voice sound a bit stupid and boring. Insane use of energy rarely comes into the equation, which is a bit weird. Personally it is why I am never tempted to use AI. However I see value in AI for finding weakness in a code (inverse of flattery), writing tests with all the edge cases based on specs since tests are often sloppy, asking a fresh view on a very difficult problem. I'd love to hear about the equivalent of move#32 in game 2 (AlphaGo vs Lee Sedol) in a difficult programming task. But I think that massive delegation of code writing is how you lose the knowledge and the know how: what keeps us sharp.
Final word: I asked once a review to claude, the codes involved a db transaction. Nothing complicated, Claude said everything was fine. However the transaction isolation level was not set (I did it on purpose, like if I did not know about isolation levels). He did not ask me if it was my intention to keep the default level. I would have preferred a challenging feedback: why did you chose the default isolation level ? Is it on purpose ? Do you know that the default depend on the db ? Do you know about isolation ? Tell me about the business use case and I'll explain which one would be the best.
Contrary to what some people suggest, I have not hit any maintenance or reliability dead ends. If something breaks, the agent fixes it.
If it cannot, I have the agent instrument the code and work through the logs to check hypotheses, until the source of the issue is found.
If even that would fail, which did not yet happen, I can still do some old fashioned digging and learning, like I always have.
This is for native mobile app development, and the code base is around 100k LOC.
Now, we can't know if this is true unfortunately, but it's not directly contradicted by anything that's known publicly at least. I thought it was an interesting way to frame it and makes the whole situation look marginally less bad.
Unfortunately (from my perspective) it seems like the US companies are increasingly stuck in their current model. I think it's a competitive disadvantage.
But obviously most of the real insiders seem to disagree with me, so I'm probably wrong :)
Chinese models are quickly commodifying frontier inference, the US Gov is preventing domestic SOTA models access to the public and without those models why would consumers still spend $200/month to use the best models?
It’s such a mess and isn’t inspiring confidence as a non-investor.
It all comes down to whose prediction of the future is closer to correct. I think the most likely future is commodification of inference and "agent-assisted" rather than "agent-driven" workflows dominating the future of work. But insiders - who both know way more than me, and also have more skin in the game, both for better and worse - seem to really think I'm wrong about that.
So I dunno! Could go either way!
What insiders are you talking about? They're going to be hot towards the possibilities so they can exit to a massive windfall. I dont know why they would want to be publicly critical of these technologies that could make millions on IPO.
My point is that actually it would be worse for these people if the valuations are only high during this period - which will last awhile longer from now! - where their equity is not liquid, but crashes as the market figures out this commoditization thing.
But if we're wrong about how that's going to go, then this isn't a concern because there won't be any devaluation. And to me that seems to be what they honestly think is going to happen. And they know more than me (and I think they're a lot smarter than me), so this does temper my confidence in my own predictions.
https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest...
I think there is. Pair today doesn’t mean they’re locked into that forever.
Honestly I still don't see how they justify their valuations, period. If anything they're serious liabilities.
Open-weight models are improving and reaching "good enough" levels for more and more tasks. They're also known quantities; you know what you're getting with them and don't have to worry about the model silently (or not so silently) being switched out from under you (whether that's because Anthropic/OpenAI decides you're not worthy of their latest and greatest for one reason or another, or they switch you to a quantized model to save on compute, or they simply sunset the specific model you've been relying on).
And if the open-weight model doesn't run on your local hardware already, there are any number of hosting providers that will handle that for you (so you're back to just paying for colocation/cloud usage instead of nebulous tokens).
Closed models are improving as well, sure, but diminishing returns will eventually kick in (as they already have for various tasks, as I said).
So if not their models, where does their value come from? Just simple network effects/lock-in? "Normal" users will drift to other options if they start showing more and more ads, and enterprise customers will surely be looking for opportunities to avoid lock-in and reduce risk.
I think the last argument I've heard is that these valuations are basically a bet that Anthropic and/or OpenAI will achieve AGI that can fully replace human labor, so they'll essentially be able to sell that replacement labor to everyone. They haven't managed to pull that off, yet, however. Businesses that have tried to replace humans almost immediately realized either that the AI's capabilities were oversold or that they at least needed a human in the loop still, to some degree. And even if they do achieve AGI, that would surely become an issue of national security (they're already flirting with that today), so who's to say governments won't simply nationalize the best AI labs and either remove them from the economy entirely or perhaps even provide models as a public service to level the playing field?
That all sounds like a giant gamble, if anything. And it's incredibly frustrating to watch as someone that's been unemployed for a year because (a) budgets are being burned on tokens and (b) LLM-generated applications are flooding hiring teams and preventing real people from being seen. (Not to mention, as someone that spends a lot of time in gaming circles, the fact that DRAM and flash storage is quickly becoming inaccessible is just an additional frustration that means people can't even find temporary relief in entertainment.) I can only hope this bubble finally implodes before I lose my house.
<banned>
Not the first one to come up with that likely outcome either. I mean, if you're being restricted from SOTA models now, how long do you expect before the FBI kicks in your door for using an 'illegal' open model?
Today's news that Amazon is hiring 11k interns. I think part of the AI story was used as a convenient excuse to get rid of some "fat" and some covid overhiring and gave companies an out to change course.
I don't know if it's a matter of just requiring a tiny amount of optimization or wholesale redesign.
For the non-bleeding edge they have a lot of competition with more competitors showing up every day.
The way this is playing out is not surprising, it's similar to any other technological breakthrough as it becomes commercialized. Eventually those means of production will become commoditized as well.
However the result is exactly the same, concentration of power.
And now in a heavy coding week rather than bumping up against my spend limit by late Wednesday or Thursday I'm comfortably below it all week.
That said if anything I feel like I have to reign in K2.6 much more than Opus, actually. If I want to just ask it a question without it inferring some coding task to immediately start doing, it takes a lot more care to prevent it from just running off half-cocked off of an only 3/4s-cocked idea of my own. I use "plan" mode with both but it's somewhat more defensive with K2.6 than Opus.
I've moved completely to local models that I run with my M1 Mac Studio (64gb ram) some time ago. But for the rare times when I feel the local, quantized Qwen3.6 isn't enough, I just connect to Openrouter and use something like Kimi, GLM or Deepseek for a fraction of the price of Anthropic et al.
https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-OptiQ-4...
Most of my work involves "Agentic engineering" instead of fire-and-forget. I like to stay involved during the planning as well as review and ask a lot more questions from the agent than I've seen others doing. In a way, I'm using the agent in a sort of "hyper auto-complete" mode to fill in the blanks (rather big blanks) once I've set out the requirements, scope and design (sometimes specific module boundaries). This works best for me.
I use Composer (since we use Cursor) or GPT 5.3-codex as my workhorse models and only break out the big guns when I have a genuinely difficult problem to solve.
IMO somewhat weirdly 5.3-codex might be the best overall coding model OpenAI have ever released. It's 90% as good as 5.5 and costs about 20% as much, since it's both cheaper per token and uses fewer tokens for the same task.
I'll miss it when they inevitably deprecate it, but hopefully I can use Kimi K2.7 by then
OpenAI claims to have made their new Terra model as good as GPT 5.5, but with half the cost per intelligence. Hopefully, this will bring it closer to the price you're expecting (or even better considering GPT models have good acceptance/success rates according to benchmarks).
There are so many models, and I personally ignore benchmarks so it takes some time to try different models on my use cases. Fortunately, it is ‘good enough’ to do the work to find a few models that work for me, and just use them for a month or two before re-investing time for my own evals to possibly change models.
People should evaluate what works for them and ignore other people and benchmarks. (Apologies if that sounds snarky.)
the incentives aren't there sadly
If this was the last model I could ever use I think I would be happy.
I can't help but feel this is intentional towards the 'Agentic' workflow.
For the 'safety' argument (Re: Fable), they need these models to have basically a 2-tier instruction system, but given LLMs aren't great with actual Logic unless they program it out to test, this runs afoul and we get one or the other.
Feels like optimizing for either precision or recall, but can't have both
By observing how in 4 workdays it achieved more than Opus in ~11 days. I am my team's backend lead and the Fable 5 model finally turned the tide on my overwhelming backlog. Back to Opus and I have to treat it like special-education kid multiple times a day.
If you set off a classifier, that's how it looks to Claude.
IMO, they were quite good with checklists even a year ago, and tried to tick off each one.
I ask “where did you get that?” … too often if I’m not constantly guiding it, and even then it still goes off the rails.
The quite useful tool is to use /opusplan along with /codex:rescue (https://github.com/openai/codex-plugin-cc) means you get quite a strongly reviewed plan using native claude + codex without having to implement the mostly useless trust-me-bro plugins and other bs.
Fable was amazing as a vibecoder but as an assistant it can't resist jumping into implementation and filling chats of pointless jargon.
It's really grim if you're looking for assistance instead of an implementor.
GPT 5.5 Pro and Fable are gorgeous bullshitters that pretend to be right (often convincingly because they are very smart) even when they are wrong and I need tons of energy to process their information.
I don't like it but don't know what to do, Anthropic models especially increasingly ignore instructions whether in memory or agents files.
The problem is obviously who will be left. There’s a lot of scifi to catch up on.
I recently migrated a very large web app to Tailwind and Opus kept screwing up over and over, refactoring and changing the design, the more complex the component became.
I ended up asking Haiku to do it and it managed to do everything correctly, pretty much without intervention.
I've taken to instructing the agent to manage the subagent, and the principal agent's sole job is to ensuring the subagent follows instructions to the letter.
"I just cloned this repo, investigate how to set it up, don't install anything, just collect information"
_spews information_
I proceed with the setup, but get a Linux specific dependency in a bash script, so I want to evaluate whether it can be rewritten...
"There's this error on MacOS, I think it's because we need linux-utils from brew, verify whether the script can be written in bare posix"
_proceeds installing linux-utils and all the rest_
"Didn't I tell you to not install anything?"
_you're absolutely right_
F*k me..
Sonnet as an autonomous agentic model is silly. We already have other models for that if you want something weaker and cheaper than Opus.
Only thing I can think of is for when someone is out of opus credits. Of course there are API billing use cases but I'd probably still just use opus on low.
I think the models are being optimized for wealth extraction from users and companies, instead of solving problems.
I don't know why Opus would try to create an entire library when I told it specifically to do something simple that would take 2-3 lines of Python.
Yeah, that’s my thoughts as well. I feel it’s great for benchmarks and some tasks while in other it tries to spend as much tokens as possible, tries to overcomplicate task and needs seconds or third round of steering that costs. With the scale Anthropic operates I bet it’s huge amount of extra money just to make sure their model works.
Because it reasons in one direction. First it encounters some kind of issue with 2-3 lines of Python that might make it not work, and then it goes onto plan B, which is making a library, but it doesn't circle back and compare the effort of making the library to working around whatever might make the 2-3 lines not work. Except sometimes it does, because it's inscrutable.
From the system card: "On CyberGym vulnerability discovery, Claude Sonnet 5 is less capable than Sonnet 4.6, and far less capable than Opus 4.8 and Mythos 5
As with the other evaluations in this section, these results were achieved with all safeguards turned off. When run with our default mitigations, Sonnet 5 scored a 0 on CyberGym"
Similar situation was with planning and coding. GLM-5.2 seems to be good “on paper” but the real usage results was different.
And I am not an attorney for Claude or GLM-5.2… :)
But as I’ve been using LLM models daily since Nov 2022 I have realized that all common tests have to be confirmed in your project - there is no “one model rules them all” - you need to dig out a specific model from that LLM haystack with thousands of models.
Benchmarks help but they start to be similar to fuel consumption specs in car ads - real consumption is different for everybody :)
"Wow, X models is Y% better or worse than Claude Z model on T benchmark"
"That's irrelevant, they're just benchmaxing."
"Not useable for daily coding or agentic workloads, the vibes are totally wrong."
"It's almost as good, and costs a lot less, so I will absolutely use it."
"I cannot imagine justifying using these, as the step change means open models lower costs do not make up for the productivity loss"
I'm an unhappy Anthropic customer and really rooting for open models and non-gatekept intelligence, but how do we move on from this now meme-like model release discourse rigamarole. I do not know what that would be. I don't design LLMs nor benchmarks, and I genuinely appreciate that people do their best to provide information, even if non-perfect here. I'm sure most of you who actively read these comment pages on announcements must feel similarly, though, right?
20 minutes after the announcement there's no real useful statement that can be made about it.
I generally agree with this in spirit https://www.seangoedecke.com/are-new-models-good/ , but I think you can read Anthropic's results showing Sonnet 5 as almost strictly worse than Opus 4.8 as very credible/meaningful, and then draw comparisons from that
This may be the goal.
> Illustration of a white goose riding a bicycle, with one wing extended forward to grip the handlebar, set against a plain white background with a brown ground line.
I keep specific branches a state where they are ready to develop new features.
As with any new model, you won't know the real impact until you start using it for your workload.
There was a fairly major regression in Claude Code performance for some time when they changed the system prompt to try and make it less verbose (saving tokens). And if I'm not misremembering, there were a lot of complaints when they changed the default effort from high to medium.
I've been using Sonnet instead of Opus for almost all coding tasks for a while now. A little elbow grease to break down tasks and you can spend a lot less money for just about the same output quality.
Why would they brag about something like this? It's like they know people want to use models to perform cybersecurity tasks yet knowingly deny them the ability.
And Opus 4.8 is still cheaper for a higher pass rate (much less open weight models like GLM 5.2) so not sure why I'd use Sonnet except on the low effort level for I suppose trivial tasks where I want it to work only 50% of the time judging by the graph. The pricing doesn't really make any sense.
It’s like telling a chef to cook without a knife because knives can kill people.
Dario and his lackeys at Anthropic aren’t visionaries.
I'm sure they're well-aware that this also will make it worse at building secure systems, but the gov't isn't restricting releases based on that.
Are there some Less Wrong posts or similar I should read that probably explain it?
thats true because their point of view makes no sense for us. dario is all in on lesswrong machine god theory and really believes they need to create a super intelligence before anyone else. that means doing as much as possible to slow down others progress and accelerate your own. but the fact that they believe its the only option doesnt make it true for the rest of us.
Fable is effectively not available to the general public in the US either
Also, I wouldn’t expect Mythos-class models to be allowed to be openly released by the CCP. Thinking otherwise is pure naivety.
Quite a lot of these models have "safety" (lol) filters in front of them, vs it being heavily encoded into the weights not.
Also Heretic as it is does not work for GLM5.2 (at least as of 3 days ago when I tested it). You'll need some hybrid approaches.
I am planning to release the steering patch for the GLM 5.2 eliminating pro-CCP alignment in the next few days.
I supposed I shouldn't be surprised at how the trump admin is approaching AI regulation, counter-productive is really all they do
>Our safety assessments found that Sonnet 5 shows an overall lower rate of undesirable behaviors than Sonnet 4.6, and is generally safer to use in agentic contexts.
which is obviously painting that as a good thing. So reading the next sentence as "in other good news" is reasonable.
This recent government interference is about trying to preserve US offensive cyberwarfare and cyberespionage capabilities. It’s not about “bad actors”. It’s about defensive capabilities becoming pervasive and cheap, which would kneecap us cyberoffensive capability.
It’s like making seatbelts illegal so that police chases can be more effective.
Gemini wouldn't do a security audit. But it came up with a great set of mitigations and identified an extant XSS flaw in the process of improving robustness.
There's an awful lot of good that can come from proactive, defensive use of LLMs. I realize there's also a lot of pain when the difficulty of exploit finding drops suddenly, but in the long term we may all benefit from the defensive side of this.
What exactly do you want Anthropic to say here? "This model, the one we are about to give to the entire world for cheap, is really good at hacking"? Saying Sonnet is terrible at cybersecurity is the most reasonable thing they can say, out of a lot of bad options.
Unless it spams as much as Opus, I doubt it. Opus 4.8 literally spams text like puke. On a longer run especially if you get cache misses here and there the bulk of the cost is all the extra context it adds.
In effect, high reasoning only makes sense when you're using the frontier model and need extra performance (higher levels of reasoning are never pareto optimal unless you're at the largest model size).
At least for Claude family models.
e.g. {
"reason": "<Describe why you picked this result>",
"selection": "<The number of the value you selected>"
I'm sure native reasoning produces more accurate results, but for my use case the quality was about the same, and the model would reason for thousands of tokens in native reasoning vs just 1-200 with response level reasoning.
Again, to be clear, this is for deterministic/pipeline style workflows, not agentic/coding use.
I don't know whether that comes out ahead compared to just staying with the better model in the first place.
I'm sure folks' mileage will vary though.
This line as a selling point is also pretty funny:
> Evaluations also show that it has a much lower ability to perform cybersecurity tasks than our current Opus models.
I also like that the difference between low, medium, high, xhigh seems more spread, which is actually a good thing for people trying to tune applications. Running Sonnet 5 on low with the launch pricing makes this potentially a better fit than Haiku or open source models for some tasks. I don't think it will make sense at full price.
I'm talking back-end, with database models, classes, queries, accompanying front-end layouts, with real dynamic data, running. Stuff that takes days to weeks to spin up, with minimal errors or issues, having cut down on days or weeks of effort, you can focus on testing and making it all into better code.
I struggle to understand where this model fits in. If I need a cheap model for simple stuff (like, summarizing an email); I'd go Haiku (actually, I'd go Deepseek v4 Flash, but you catch my drift). I just can't think of many tasks where I'm like "yeah let me reach for Sonnet Low Reasoning so I can save a dollar but also seriously run the risk of it failing"; I'd just reach for Opus Low.
Low and maybe medium will save money on simpler tasks, but after that it just isn’t worth it compared to Opus.
I wish they would have explained in the blog post why they think anybody would ever want to use this above medium.
Maybe it works well on things that aren’t clear in the benchmarks.
In that, it seems sonnet 5 on high costs more than opus 4.8 at a lower pass rate. Am I reading this correctly?
Edit: It looks like the key value proposition of the updated model is that it is much better than Sonnet 4.6.
Wheras, Sonnet 5 delivers great value (by browsercomp benchmarks and compared to opus) when running in low and medium.
So: Sonnet 4.6 should ~never have been run for low, medium or high when Opus 4.8 has been available. Whoops, I think I have some skills that delegate easy stuff to Sonnet.
---
I remember Anthropic pivoting everyone's default model to Opus but had not seen it put so starkly before.
I am a bit confused on the subscription `/usage` screen. It splits out sonnet usage, and I'd presumed that would have contributed to a lower use of subscription Quota.
But if this is correct, Sonnet usage was basically like smoking unfiltered cigarettes.
Sort of like, getting an automatic upgrade at a car rental or hotel if there is availability.
But isn’t Fable supposed to be another step change? I never used it, myself.
Tbh, at this point I think top tier models are smart “enough” (I’m sure this will look antiquated in a year), and the way to give me MORE noticeable improvement is to make them much faster rather than much smarter. Or even a way to automatically and accurately pick faster models when it makes sense. I know that IDE’s have Auto modes, but it’s not something that I trust right now to pick smart+fast instead of picking “maybe smart enough”+”cheaper for harness owner”
In other words, for certain tasks, Opus 4.8 is cheaper than Sonnet 5, and does better than Sonnet 5.
I've noticed this pattern on a lot of benchmarks. You can try to emulate a bigger model by ramping up the test time compute (max reasoning, more turns, model fusion etc.), but you can't reach the same quality level, and you often exceed the cost you would have paid by just using a bigger model.
tldr: if you're doing something hard, just use a bigger model.
or
The Dodge Charger is built to be the most Charger like car yet.
"Sonnet 5 is an upgrade to Sonnet 4.6, but it uses an updated tokenizer that changes how the model processes text to improve performance (this is similar to the tokenizer change we introduced with Claude Opus 4.7). The tradeoff is that the same input can map to more tokens: roughly 1.0–1.35× depending on the content type. The introductory pricing is set so that the transition to Sonnet 5 is roughly cost-neutral."
If we trust them, then it is roughly the same as sonnet 4.6
Today sonnet 5's med level effort is equivalent to sonnet 4.6 low level effort :/
Unfortunately that means I won't be using it at work for now.
"They took my shit away!" -- 3-day Fable 5 addicts (me)
"How dare they tell Trump no?" -- US nationalist / "my country right or wrong" types
"Great to see a closed source company fail!" -- open source boosters
"Great to see an American company fail!" -- anti-US, and/or pro-China folks
"Great to see a successful company fail!" -- anti-capitalists and/or sour-grapes crab bucket types
"Serves you right for ripping off creators!" -- copyright warriors
"They keep silently nerfing the models!" -- secret downgrade conspiracy theorists
"Quit killing the planet!" -- anti-datacenter advocates
Which is a bit of a bummer considering they do genuinely make the best model that's most pleasant to work with in my opinion.
I don't agree with your framing that all negativity is from crazies
It feels like your analysis is mostly spot on, it's the confluence of several motivated parties pouring effort into social media.
Many of the posters are pro-foreign models/pro-open source, and most can't distinguish the difference between "open source" and open weight models like Qwen, Minimax, or GLM.
Reminds me of the old "free as in beer" vs "free as in speech" debate. Free beer means you don't pay, but you don't get to see the recipe or change it. Free speech means you get the actual source and the right to study it, modify it, and redistribute it.
Open weight models are basically the beer version. You can download the weights, run them locally, fine-tune them, quantize them, host them on your own boxes — but what you have is a finished product, not the blueprint for how it was built.
Qwen is also censored - although since it's open weight, there are completely uncensored versions available.
The owners of Qwen can't jack up the prices to something I'm unable to pay. They can't take it away.
The owners of Qwen can't log and train on my data.
Open weight models share far more in common with free speech than free beer.
If big daddy Dario and his company are getting pushback it's not being of some motivated group trying to take them down. They brought it on themselves.
It seems being incompetent is a feature now...
cool to see, still waiting for models to get better at computer use.
Claude Code generates more revenue than OpenAI...It appears to be a nice meme.
Bro that is financial engineering, not real revenue growth. They engineered the switch to usage based pricing and a price hike timed the quarter before they wanted to go public, long enough to juice their numbers but not long enough for them not to be able to manage backlash and have to walk things back. Then they tried to extrapolate that manufactured bump to make it look like they have record shattering revenue growth.
Based upon the "Agentic Computer usage", Sonnet 5 Max was going to be off "Agentic Search results" chart. lol ...
In short, Sonnet 5 Low/Medium is more cost efficient, if its a task below Opus 4.8 Medium. For the rest its expensive and your better off using Opus 4.8.
Why even release this model?
You are reading too much into the graph and ignoring the threshold of usefulness for real world tasks. By that logic Sonnet 4.5 would have never been worth using.
For the rest the gap in pricing vs efficiency is so small, that there is no point in using Sonnet. I am looking at their own cost comparisons vs efficiency...
I use Haiku a lot for agent workflows, if I can get better output at similar prices, Sonnet 5 will replace it completely.
- Do the ever increasing scores on the mean we will soon have models that approach 100%? And what would that even mean? That there is no more room for improvement?
- Would Anthropic (or any other model vendor for that matter) ever release a newer model that scores lower? If not, does that mean they keep tweaking a new model they want to release until it shows an improvement of the prior model?
- Would it be more useful to move toward a comparative rather than absolute ranking?
And yet, the $2-$5 section is the widest, even though it only contains a single point.
I can't even say if this is making the product look better or not, but it sure is weird. Maybe Claude just hallucinated those splits xD
None of the other labs are doing this kind of long lived two model series.
Okay.
Oh Claude you master of software engineering does it ever end? DO you have no bounds?
How may we further assist you oh Claude?
I'd generously assume this is something about the specific category of agentic task presented in the chart... but it does raise the question "then why is that category the one they chose to highlight here".
Agentic search is a different story, but even there it still dominates 4.6 (as in, for everything Sonnet 4.6 can do, Sonnet 5 can do it as well or better at the same or lower cost).
Yes, Opus 4.8 dominates Sonnet 5 over its entire range in both categories, but Opus's lower range is limited and there is a valid regime on the lower end where Sonnet 5 use makes economic sense. This is not the case for Sonnet 4.6 where Opus 4.8 dominates it completely on both charts.
Edit -- reading your response closer I think we're saying the same things, maybe just disagreeing on whether that lower end is valuable or not.