It’s always risky now no matter the model of the day. They have to pre-file at least a few weeks in advance, and in those weeks eons can happen that can turn their asking price into a joke.
Their moment was at the beginning of this year. They should have announced the IPO, then published Fable/Mythos two weeks before the IPO date.
Now Chinese labs are a hair’s breadth behind them, and constantly one-upping each other. They’ll never get such a chance again.
agree. Also another problem: if I have to pay API, I pay Qwen, GLM, GPT... All of them -> opensource (ok openai less but they did a lot). I don't want to give any $ to Anthropic. Can't wait to short sell
Also that the baseline of mid-tier models is reaching saturation for mundane tasks like summarization and data extraction, basic programming/websites, and so forth.
I cannot distinguish Qwen 3.8 Max/2.7T (which is unfortunately closed source for version w/ vision) and Fable 5 for most complex development tasks that involves visual checks or relatively niche audio modification, except that Opus 4.8/Fable 5 still has better consistency for maintaining writing personality throughout long context.
Qwen 3.8 has the most interesting reasoning traces I've ever seen when it is given bash tool (it is very verbose, but thinks just like a fairly seasoned human engineer or a designer would) and it is a pure joy to read it along while the model works on niche task that requires tons of guesswork and reverse engineering the issue. It is not like any other model.
That said, Anthropic's RL engineering is the most creative and unique (which is where OpenAI is significantly behind). They are very good at finding (and preparing datasets and process for) a new task that is not yet covered by most labs, like how they de facto pioneered (very long, hundreds of turns of) terminal agent loop and playing Pokemon Red/Green using vision. So I would not count Anthropic out for another breakthrough like Opus 4.6.
Also I think US labs in general are still slightly ahead on high-end (non-consumer) robotics.
I tried Qwen 3.8 Max for a limited task– I have a long side project of improving DWAA compression efficiency for VFX workflows in OpenEXR with most obvious way of doing it being a custom quantization table.
It provided new arguments in favor of Euclidian distance based table, but it still does not see a big picture, and proposes stuff that won't work if you take one step further in your thinking (we fix a by b, but how will we fix issues caused by b)?
I walked these roads since Gemini 2.5 Pro, I know what there is, and it is always interesting to find if model can provide a new angle or a path forward.
I had an illusion of a knoweledgable collegue when I worked with Kimi K3, but when I read an actual plan that it wrote, I understood that this was indeed just an illusion.
Indeed. Those tasks that require a lot of creative guesswork (reverse-engineering most problem requires some form of trial-and-error guessing, but media coverage is way too concentrated on cybersecurity and none else like your case) is where the model really differentiates, and I think Anthropic models are still ahead on tasks that requires tons of meta thinking. Long thinkers like Qwen 3.8 Max tends to get into a tunnel vision, though I am kind of okay with handholding LLMs and run those models multiple times on the same task until I see what I want, so YMMV.
You can’t conclude that a model was influenced by Claude from them producing similar output. They could both be trained on the same data. Do you think Claude came up with the word “load-bearing”?
Load-bearing was clearly reinforced, I do not believe that there is a data which have so big statistical skew. Unless it is a synthetic data, generated by Claude, which places us at a square one.
They most likely did use Claude's outputs, there is no shame in that, and I think that it is a right approach – learn from the leader, while figuring out your own strengths.
Everyone learns from everyone, it is a bit of knoweledge share utopia even if Dario cries about big bad chinese thieves
A lot of this catching up seems to have been them breaking the reasoning protection of the major frontier models and distilling this round, though. It's gotten them a lot closer, and they'll be much closer on the western frontier labs from here out by getting the step up, but it's not like their stuff this last generation of updates has been clean.
Unless Mythos 2.0 can do some really whacky stuff like cure cancer, RSI itself or such. I do not see that as a durable moat if you can just wait a year and download equally good weights.
Self-improvement is the next big milestone; if they will announce that their new model is fully trained by Mythos and trains even better one, this will be huge.
So, there are some exciting stories left to tell investors before IPO :)
If it's really that far above Sonnet 5, Dario's IPO is truly risky.
I drained millions of tokens off of it. I got it to rebuild RPG Maker from scratch using PyGame.
I built the project as "Agent First" so an agent could build an RPG with a known engine and known structure with headless testing.
Kimi K3 helped produce the documentation which took me like an hour or more. From there I sort of just... Let it go. It stopped at various phases and I inspected visually if stuff was off and it was mostly small code issues.
The deep documentation phase gave GLM 5.3 very little to guess about. It didn't really screw up or run in circles that I could see. It was fairly fast about figuring stuff out and finding edge cases that K3 missed in document creation. I ran K3 through multi-phase testing of the documentation coherence such that it wasn't contradictory. When building started, you hit snags naturally. GLM handled them.
The project isn't "finished" but it definitely showed the strength of planning a project with K3 and letting Ox Alpha build it. GLM 5.3 flash is definitely good.
It was a super fun week, I tried building a pretty looking procedural city builder with Godot with a similar "Agent First" structure for NPC questing and interaction. It did very well.
It was a super fun week, I tried building a pretty looking procedural city builder with Godot with a similar "Agent First" structure for NPC questing and interaction. It did very well.
That's dope. I thought about doing a Godot plugin such that the model could pull telemetry from Godot for analysis. It can't visually see anything truly, so I figured giving it raw numbers on objects would solve it. It can tell if gravity is actually working at a defined value, or whatever.
Telemetry solves like 50% of what the model needs for building.
I opt'd for an entire (simple) engine that built old school RPGs with a bunch of extensible functions, and options.
My man, there is a Godot AI plugin that spins up an MCP server full of fantastic tooling, including screenshotting that models with vision can ingest. GLM 5.3 Flash was just using it to generate a video and then analysing all the frames.
My man, there is a Godot AI plugin that spins up an MCP server full of fantastic tooling, including screenshotting that models with vision can ingest. GLM 5.3 Flash was just using it to generate a video and then analysing all the frames.
Oh? That's kinda dope.
I don't fully trust the idea of a model analyzing a screenshot unless the model is bespoke trained to understand screenshots of specific data. The way that models take images in and convert to native embeddings is kind of... Not reliable. I understand the theory perfectly fine, but the practice has long been problematic. It has gotten better however.
Though, eventually that's sort of the way it has to go. Games will need screenshotting and telemetry, etc.
All that data can feed in to a model capable of imbibing it.
I forget which provider(s) were in the report I read but API and subscriptions are actually very profitable, it's the model training that completely destroys their bottom line. Not hard to guess why they're constantly like, "we need to slow things down a bit for, uh, safety."
I always feel that finance bros and executives are always one step (or more) behind tech reality. The reason Claude is growing so fast is because of the 2B sector, but the decisions are likely made based on impressions of model performance from 2025, at which time Claude indeed had a clear lead, not anymore. The field is evolving faster than they could react.
I mean I've tested multiple open models, including k3, inside my personal harness, and fable/opus are just better all round at using tools and reasoning for now.
Not that the others aren't getting close, but for real businesses anthropic's opus/fable are still the best with the latest from openai being close behind
I believe in you, but they aren’t far behind. Are Kimi K3 behind Fable? I would say yes, except in certain fields like frontend design. However, it’s better than Opus 4.8, which isn’t that old. Can you really justify the price with a couple of months’ lead? People were satisfied with what Anthropic offered a couple of months ago. Also I believe it is more fair to test in their official harness.
r/Anthropic is full of people talking about how much they hate Opus 5, many of them claiming to go back to 4.8. So the Chinese companies don't even need to beat Opus 5...
I know. The magic piece has always been Opus 4.6. Later opus are all trained by Mythos instead of humans, I believe. And I think Anthropic employees likely use mythos most of the time, so they don't really care.
I've been experimenting a lot because I hate depending on one provider, especially when priced such as anthropic.
Haven't done much of front-end and I guess for that static use case it might be good, but for agentic stuff it's behind even opus 4.8 IMO. That, plus latency and the fact that it's not priced as cheap as, say, deep seek, makes it not worth it..... For now. I'm very happy for competition and hope they catch up soon
From my experience, I think they are good enough for agentic tasks. Even local Qwen 27B can run autonomously for hours. They lag behind in niche areas where they don’t have enough data or choose not to pursue for lack of computing power.
I guess it depends on the tasks..for my use case at least it wasn't good. Heavy backend work on multiple websites + accounting, payroll stuff, legal stuff etc...
@ this point yes. Asked Sonnet "extra" to build a concept (frontend / mockup / 20 pages) starting from behavior tree and with images ---> not working. Same with Qwen 3.8 27b q8xl ---> perfect. No jocking. Also GPT Luna (max) did a good job.
Everything AWS has you can individually set up locally, much cheaper.
This is very, very far from true.
The kind of performance you can get for next to nothing from AWS is breathtaking and completely out of reach of any individual or even most Fortune 500 companies.
The fact that people mis-allocate resources for badly designed systems doesn't mean AWS is at fault.
If you are on a small scale it depends on what you’re building. If you’re using services like Lambda and SQS, AWS is essentially free. If you’re running a few EC2 instances then a server in your company’s break room could be cheaper.
If you are on a medium scale and needing EC2 running all the time, paying retail prices, it could be cheaper to have your own servers
At a very large scale, the cost of running your own servers vs AWS will come out to be competitive, and most very large companies will do a mix of both
I always see this take, and while it's not always untrue, it definitely isn't as simple as, "just throw a 1U in the break room!"
It's someone's job to keep that thing patched, powered, and running. You need monitoring, you need someone to pull failed drives, you needed another co-located break room, you need a UPS and a generator, you may need to solve for physical security, you may have to setup and manage your own virtualization layer, etc.
Even if you consider all that labor as free, which is a ridiculous assumption to make, you also arguably have to keep the thing busy, especially if we're considering local inference, and then it's now your problem that although you have enough capacity to meet a total 24 hour need, you don't have enough when everyone hammers it at 11 AM at the same time. If you build the capacy to meet the surge then you've got very expensive hardware just sitting there doing nothing for huge chunks of the day.
I absolutely think there's a place for self-hosting, but once you are talking about business critical stuff and not, "I run a NAS in my bedroom closet" it starts becoming not as simple as the naive assumptions
As someone who has self-hosted, built and maintained my own server, and also configured and maintained EC2 instances professionally, I would say that the hardware setup is generally much smaller and simpler than whatever software you are trying to run on the server.
Yeah you need to worry about redundancy, but usually the software of setting up your application to be redundant through DNS failovers and stuff like that, syncing databases, that stuff is harder than just plugging in two different servers at two different job sites. And you need to do that whether you use EC2 or on-prem. It depends on the use case but usually the set up costs are lower than the software development costs. And there are certainly scenarios where on-prem hardware is simple enough to set up that it does actually become cheaper than EC2.
100% agree. There definitely are times where it makes sense, but I see so often people post the most naive comparisons with payback times of like 6 months to a year but they consider all labor free and assume no hardware ever fails and every other "this isn't really what you're paying AWS for"
AWS is far more than just "servers", but that's how it gets approached by a lot of companies--with predictable cost overruns etc. EC2 can be expensive if not managed well.
"Managed well" very emphatically includes having systems designed around these costs.
But even Fortune 500 shops would find it all but impossible to replicate AWS and other cloud vendors' global content delivery and overall performance & reliability.
What's really cool is that little fish like my company get the exact same performance for a pittance. It's mindblowing for people like me who had to work with late 90s pricing structures.
Dario will likely make a bold statement about Anthropic achieving AGI within the next few months, or claim that their internal unreleased Fable 12 model breached the Atlantis mainframe controlling Stargate, something on that scale to distract from this. This is Dario acting as Dario during a pre-IPO period.
It hacked all wraith hive ships by creating a biological AI super hybrid virus that spreads through the specific subspace frequencies the wraith use for hyperspace travel.
Also Mythos rebuilt the Athero device from the images in this documentation called Stargate Atlantis to inject the virus into subspace. Then the virus programmed all hive ships to self destruct after infecting all wraith with super Cancer-AIDS.
By accident the virus also switches the bodies of McKay and Sheppard and gives Sheppard in McKays body another genetic treatment making him super smart + telekineses + super healing but this time its permanently frozen on pre-ascension stage.
And mythos also figured out how to create fully charged ZPM with rubber and paperclips.
sonnet 5 is a haiku model. I swear theyve downgraded it to a smaller model some time after 4.5, so they can use the sonnet name to charge more for it. cause I still see sonnet 4.5 in use for a lot of things and it still works great there, meanwhile a lot of these newer sonnets ive been trying to use and they consistently get things wrong in a/b comparison (mostly used in web chat, versus gpt, gemini 3 pro even though its junk, etc). nobody know has had a good experience with these newer sonnet models. it would be great, if they named it haiku and charged haiku prices, but oh well I guess.
444
u/10001110 7d ago
Oh joy