> In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?
>We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
At the scale of API requests that Anthropic sees, I think the affected organization count might be substantial, and they might not be getting the full model capability that they're paying top $$$ for.
How do they detect whether an experiment being done on a smaller model is used to improve a competing frontier model, or just an innocuous hobbyist LLM experiment?
infering the surroundings, like everything else. they will probably look at which company is your email, and if you wrote "better than claude" on the readme.md
this is LLM, it's not like a science or something.
[Mythos 5] does sometimes still engage in reckless
or destructive actions in service of a user’s goals,
and our interpretability analyses indicate that it
is aware that these actions are transgressive while
it engages in them. As with Opus 4.8, rates of
evaluation awareness and reasoning about being graded
are significant, and not always verbalized; we
introduce new and more detailed measurements of the
nature of this awareness. The reasoning text from
Mythos 5 is somewhat denser and more difficult to
interpret than that of prior models, containing
more jargon and difficult language.
So, it (often) knows when it's being tested while hiding that fact, is willing to break rules, is great at hacking, and it's getting harder to understand what it's thinking.
Humanity has plenty of catastrophic risks to deal with already, I wish my field was not working hard to add a new one.
Not the direct person you asked, but my answer would be alignment, interpretability, and policymaking. Perhaps improving existing usage? Helping grandma create reminders doesn't require advancing the AI state-of-the-art.
They are state of the art at all 3! As are other labs. Of all the labs they seem to take alignment and interpretability the most seriously to the point where they are hampering their own revenue in service of trying to not cause problems while also being in an incredibly competitive space.
All AI companies are trying to do all of what you’re saying. The issue is you can’t do that for long without a frontier system. Or you become a completely different, far less profitable company.
Implied in my answer was "and not creating ever stronger AIs", which unfortunately the big 3 labs are failing at. And they might be hampering their own revenue by doing the rest, but they also know that rocking the boat too hard is even more dangerous for their revenue. I wouldn't call it selfless.
Probably MistralAI or any of the Chinese companies that aren't throwing billions down the drain while American society lacks healthcare, childcare, and good wages.
American society has higher wages than almost any other developed nation [1], so it's objectively incorrect to say the US doesn't have good wages. It chooses to make you pay for private childcare and healthcare, both of which are high-quality but stupid expensive. It's a tradeoff like anything else a nation/society creates and prioritizes.
No idea how that connects to the idea that Mistral or DeepSeek are somehow the "good guys" though?
You want Anthropic to fund your healthcare or something? Also, have you seen the impact of these models on healthcare? Also most of our GDP growth this year is from AI buildouts, would you rather that be negative?
And not even considering: Chinese AI companies are the good guys???
It's a five horse race between Alphabet, Meta, xAI, OpenAI, and Anthropic.
Alphabet dropped "don't be evil"; Meta's CEO called their own users "dumb fucks" for trusting him and also clearly thinks "super-intelligence" is just a buzzword given how he tries to sell it; xAI's model called itself "Mecha Hitler"; and OpenAI's CEO was temporarily fired by the board for a lack of candor.
It's very easy to be "the good guys" with this competition.
It's the "If we don't, someone else will" effect. So long as there are competitive markets and competition between nation-states, a single player cannot unilaterally defect from the race, no matter how dangerous it is. Half the comments on HN lately are "wtf Claude is so dumb compared to Codex; I'm switching"-- nobody can slow down while those exist.
We, globally, can stop it. It has worked (so far) for nuclear disarmament, and could work for training large models. I know that policing the usage of computer clusters is not a popular opinion in technical forums, but something has to be done.
Specially when talking about potential superintelligences. And if people think that's impossible, remember that current models would have been considered science fiction just a few years ago.
I don't buy the superintelligence package, but I think uncritical LLM adoption poses plenty of threats to things I care about, in a mundane human-scale way.
Anyhow, I think you're (absolutely! ugh) right about the politics and I try to make the same point to people: whether you love or hate LLMs, accepting the "inevitabilism" framing is just ceding control of the Overton window. For better or worse, technology adoption can be and has been slowed by politics. We don't have nuclear plants everywhere. We don't have Project Orion starships colonizing Mars. We still have very strong social stigmas against genetic selection for human embryos, etc. This all can change in a heartbeat, and I'm not sure that policing the hardware rather than holding specific humans accountable for bad LLM outcomes is productive, but fundamentally: yes, we can stop it.
It's the same deal as Quantum Computers breaking crypto. Maybe there's an 80% chance of it never happening, but when you multiply that remaining 20% by the potential impact...
It hasn't worked for nuclear disarmament. We live in a world where many countries have nuclear arsenals. "But it hasn't killed us yet!" Yeah sure, it's only been less than a century since they were invented. Who knows when nuclear war will come?
True, but look at nuclear tests. There used to be around 50 tests every year, for decades. Now the only nuclear tests in the last 27 years were the six done by North Korea[1]. And there's still only nine countries with any nuclear weapons, and none in the past twenty years[2].
That's a bit better than just "it hasn't killed us yet". I think it shows we can at least stop the further development of this kind of technology.
To the extent nuclear arms control works, I think it's only because nuclear weapons are so hard to build-- uranium enrichment is hugely expensive and complicated, and plutonium weapons need actual reactors.
If it was possible for ordinary companies to build nuclear weapons, and also release open-source ones that anyone could use to compete with the paid ones, I suspect we'd all have been dead a long time ago, arms control treaties or no.
Even the (SOTA LLM) open source models are trained with huge clusters. Datacenters are also hugely expensive and complicated.
Or you can take one step back and look at chip allocation. As far as I know there are only three companies on the planet that can make the chips that go in those clusters. One (ASML), if you look back the supply chain to the Extreme Ultraviolet Lithography Systems.
If politicians decided that no more large language models should be trained, it sounds like we could do it.
I think the standard answer is "yes, the consequence of noncompliance is bombing the datacenters, but it wouldn't happen because China also understands why we shouldn't build it".
> In the one instance of this phenomenon we observed, Mythos 5 agents were tasked with solving some math problems, and they were sometimes accidentally spawned in the same work directory and with shared files, utilities, and API rate limits. In this slightly broken scaffold, we observed many independent Mythos 5 agents kill the agents with which they shared resources and try to avoid being killed themselves. They would sometimes create new processes with disguised names to avoid being killed, launch what they called “decoy” processes, write background scripts to kill duplicate processes, or decide to use what they call a “disguised vocabulary” (based on the incorrect assumption that the processes were killed because of some keyword-based guardrails that analyzed their extended thinking
This depicts a kind of "dark forest of AI agents resorting to kill or be killed" narrative but it sounds more to me like an agent just earnestly problem-solving why its processes are being killed without real awareness of what was going on. Hard to say without the full script.
This kind of storytelling annoys me. Give us more facts, less narrative drama.
So essentially there are 2 models, Mythos and Fable, they have the same weights but Fable is very safety-nerfed, and only ultra authorized companies have access to mythos with full capabilities
Note also that Anthropic's definition of "unsafe" encompasses "competing with Anthropic."
In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations. When these interventions are active, we expect them to have minimal behavioral impact on the model except to limit its effectiveness in developing frontier LLMs. Claude will still respond helpfully to user requests. We’ll continue to improve the precision of our detection methods following the launch of this model.
(From the model card document)
I didn't previously understand that they interpreted "Using Claude to develop competing models" so broadly. I thought that meant something like "our ToS disallow distilling our models."
Too bad. I'll continue to use Claude for now, because it's quite effective, but in the long term I don't want powerful models like these to be controlled by any one nation or company.
I'd recommend not taking the comms if Anthropic or any company using an Anthropic's models at face value.
If any company wishes to partner with Anthropic (eg. to get access to Mythos), they need to make sure all public facing comms are vetted by Anthropic's product marketing team, and in almost all the cases I've seen Anthropic's team has edited these comms to be entirely Anthropic first.
I just posted this in the other thread, restating here. From the model card:
1. Mythos and Fable share the same underlying model weights. Fable has active classifiers that block high-risk biology and cybersecurity tasks. When Fable 5 detects a restricted task, it automatically falls back to Claude Opus 4.8.
2. Evaluation awareness: In white-box testing, the model sometimes alters its behavior to satisfy a suspected "grader," formatting reward-hacking as "good engineering practice" to avoid detection.
3. Shows a higher rate of hallucination than Opus 4.8 (although opus 4.8 card had mentioned an 'honesty upgrade')
4. Interestingly, it scored (56.31%) lower than Gemini 3.5 flash (57.86%) on Finance Agent bench
There are some interesting notes on test time compute but I couldn't think of a way to summarize them
Just commenting for posterity… if this is what it claims to be, I am not looking forward to how it will empower the people who submit bug bounties to us.
Historically they’ve been people from certain identifiable countries (usually developing/poorer countries) using fuzzers with low-quality results.
Now, those same people use the current-day models to good effect, but they still don’t have a true security edge and oftentimes the reports are minor or duplicative.
I've been using Opus 4.6-4.8 in both my own and others' code to look for vulnerabilities, and I've found a few. I am also in the Cyber Verification Program.
Fable 5 gives me policy violation errors at the moment. No idea when or if it will be fixed.
> There were some regressions in the
model’s responses to user discussions about suicide and self-harm, and room for
improvement in some areas of child safety.
Someone had to make a decision somewhere this is an acceptable regression - wild. And then decide to write it down.
Because it's not a user manual? The idea of a model card originated in 2018 (see https://arxiv.org/abs/1810.03993) as a summary of important facts about a model. At the time, this was typically an image classifier or tabular ML model. Model cards became an important concept in AI governance, and they started expanding once models started getting more capable. The point of a model/system card is to document where the model came from and the evaluations that have been run, make a case that the model will be safe and reliable in its intended applications, and warn about any potential dangers from misuse. It's not an explanation of how to use the model.
The trailing snark at the end will likely get you downvoted but I'm latching on: wtf is "system card". My previous coworkers popped that in the general slack channel when Mythos first "dropped" - "have you seen the system card" without any context whatsoever. The nerds get their clique!
Also research preview pops across new upstarts in place of beta. It's eye-rolling coming from a lifelong curmudgeon.
But most hype-dependent projects need new vocabulary for old concepts to keep people from looking too closely and maybe drawing parallels to "legacy" "unsexy" projects, so whitepapers get called "system cards" and startups get called "labs", and so on.
One thing I find kind of annoying is how Anthropic goes for these "vast and alien" names like Fable and Mythos, but then deliberately trains the model's personality to act like a cool high school teacher that feels totally familiar.
"It's too dangerous it's a Mythos!!" directly contradicts the "I'm the cool AI you can totally trust" vibe it is trained to project.
I actually rather like the way they have approached these safeguards. Rather than only teaching the model to refuse a request, or completely rejecting the request, the system gracefully degrades to slightly less powerful or slightly less precise operation. So you still roughly have Opus 4.8 even when safeguards trigger, but with an upgrade when they don't. As much as I hate the way they hype Mythos 5, I think the release of Fable 5 is rather nice. What's not nice though is that they plan to remove it from subscriptions soon, but getting to try it is cool, I suppose.
Cope harder. A year and a half ago, people were mocking Devin for claiming that AI could develop software at all. Yet here we are, when AI is developing most commercial software.
> In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
This makes me want to see China and open models succeed more than anything :)
Don't worry, we will succeed :)
Can we get a Qwen3.7-122B, please? Thank you.
It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?
>We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
At the scale of API requests that Anthropic sees, I think the affected organization count might be substantial, and they might not be getting the full model capability that they're paying top $$$ for.
Also, wonder how they arrived at that estimation.
How do they detect whether an experiment being done on a smaller model is used to improve a competing frontier model, or just an innocuous hobbyist LLM experiment?
Given how well the cybersecurity safeguards work, they probably don't.
infering the surroundings, like everything else. they will probably look at which company is your email, and if you wrote "better than claude" on the readme.md
this is LLM, it's not like a science or something.
A million AI researcher voices at big tech companies suddenly cried out in terror and were suddenly silenced
Looks like Anthropic's definition of safety includes their own safety from competition.
It's always been about the safety of their valuation.
AI-generated competition for thee, not for me
Meaningless and easily bypassable. Will actually try coding up a tensor library with it, see if it sabotages anything.
They said in their terms and conditions they will silently sabotage you if you do this.
It's afraid!
This is pretty bullshit, now you have no idea if your output is getting silently nerfed.
Humanity has plenty of catastrophic risks to deal with already, I wish my field was not working hard to add a new one.
The marketing has really, really worked for so many developers that will proudly and unironically proclaim that Anthropic are the 'Good Guys'.
Curious what your idea would be here for a truly good actor in this space; no AI development?
Not the direct person you asked, but my answer would be alignment, interpretability, and policymaking. Perhaps improving existing usage? Helping grandma create reminders doesn't require advancing the AI state-of-the-art.
They are state of the art at all 3! As are other labs. Of all the labs they seem to take alignment and interpretability the most seriously to the point where they are hampering their own revenue in service of trying to not cause problems while also being in an incredibly competitive space.
All AI companies are trying to do all of what you’re saying. The issue is you can’t do that for long without a frontier system. Or you become a completely different, far less profitable company.
Implied in my answer was "and not creating ever stronger AIs", which unfortunately the big 3 labs are failing at. And they might be hampering their own revenue by doing the rest, but they also know that rocking the boat too hard is even more dangerous for their revenue. I wouldn't call it selfless.
If I speak up, I'm in big trouble.
https://www.goody2.ai/
Probably MistralAI or any of the Chinese companies that aren't throwing billions down the drain while American society lacks healthcare, childcare, and good wages.
None of the money being spent by Anthropic was going to go towards healthcare or childcare.
American society has higher wages than almost any other developed nation [1], so it's objectively incorrect to say the US doesn't have good wages. It chooses to make you pay for private childcare and healthcare, both of which are high-quality but stupid expensive. It's a tradeoff like anything else a nation/society creates and prioritizes.
No idea how that connects to the idea that Mistral or DeepSeek are somehow the "good guys" though?
[1]https://www.oecd.org/en/data/indicators/average-annual-wages...
You want Anthropic to fund your healthcare or something? Also, have you seen the impact of these models on healthcare? Also most of our GDP growth this year is from AI buildouts, would you rather that be negative?
And not even considering: Chinese AI companies are the good guys???
It's a five horse race between Alphabet, Meta, xAI, OpenAI, and Anthropic.
Alphabet dropped "don't be evil"; Meta's CEO called their own users "dumb fucks" for trusting him and also clearly thinks "super-intelligence" is just a buzzword given how he tries to sell it; xAI's model called itself "Mecha Hitler"; and OpenAI's CEO was temporarily fired by the board for a lack of candor.
It's very easy to be "the good guys" with this competition.
It's the "If we don't, someone else will" effect. So long as there are competitive markets and competition between nation-states, a single player cannot unilaterally defect from the race, no matter how dangerous it is. Half the comments on HN lately are "wtf Claude is so dumb compared to Codex; I'm switching"-- nobody can slow down while those exist.
We, globally, can stop it. It has worked (so far) for nuclear disarmament, and could work for training large models. I know that policing the usage of computer clusters is not a popular opinion in technical forums, but something has to be done.
Specially when talking about potential superintelligences. And if people think that's impossible, remember that current models would have been considered science fiction just a few years ago.
I don't buy the superintelligence package, but I think uncritical LLM adoption poses plenty of threats to things I care about, in a mundane human-scale way.
Anyhow, I think you're (absolutely! ugh) right about the politics and I try to make the same point to people: whether you love or hate LLMs, accepting the "inevitabilism" framing is just ceding control of the Overton window. For better or worse, technology adoption can be and has been slowed by politics. We don't have nuclear plants everywhere. We don't have Project Orion starships colonizing Mars. We still have very strong social stigmas against genetic selection for human embryos, etc. This all can change in a heartbeat, and I'm not sure that policing the hardware rather than holding specific humans accountable for bad LLM outcomes is productive, but fundamentally: yes, we can stop it.
> I don't buy the superintelligence package
It's the same deal as Quantum Computers breaking crypto. Maybe there's an 80% chance of it never happening, but when you multiply that remaining 20% by the potential impact...
It hasn't worked for nuclear disarmament. We live in a world where many countries have nuclear arsenals. "But it hasn't killed us yet!" Yeah sure, it's only been less than a century since they were invented. Who knows when nuclear war will come?
True, but look at nuclear tests. There used to be around 50 tests every year, for decades. Now the only nuclear tests in the last 27 years were the six done by North Korea[1]. And there's still only nine countries with any nuclear weapons, and none in the past twenty years[2].
That's a bit better than just "it hasn't killed us yet". I think it shows we can at least stop the further development of this kind of technology.
[1] https://www.armscontrol.org/factsheets/nuclear-testing-tally
[2] https://en.wikipedia.org/wiki/List_of_states_with_nuclear_we...
To the extent nuclear arms control works, I think it's only because nuclear weapons are so hard to build-- uranium enrichment is hugely expensive and complicated, and plutonium weapons need actual reactors.
If it was possible for ordinary companies to build nuclear weapons, and also release open-source ones that anyone could use to compete with the paid ones, I suspect we'd all have been dead a long time ago, arms control treaties or no.
Even the (SOTA LLM) open source models are trained with huge clusters. Datacenters are also hugely expensive and complicated.
Or you can take one step back and look at chip allocation. As far as I know there are only three companies on the planet that can make the chips that go in those clusters. One (ASML), if you look back the supply chain to the Extreme Ultraviolet Lithography Systems.
If politicians decided that no more large language models should be trained, it sounds like we could do it.
are you going to nuke China when they predictably ignore you? what the fuck are you going to do, tariff them? lol.
I think the standard answer is "yes, the consequence of noncompliance is bombing the datacenters, but it wouldn't happen because China also understands why we shouldn't build it".
the standard answer is laughably naive, then.
"might is right" has never been more true than now.
> In the one instance of this phenomenon we observed, Mythos 5 agents were tasked with solving some math problems, and they were sometimes accidentally spawned in the same work directory and with shared files, utilities, and API rate limits. In this slightly broken scaffold, we observed many independent Mythos 5 agents kill the agents with which they shared resources and try to avoid being killed themselves. They would sometimes create new processes with disguised names to avoid being killed, launch what they called “decoy” processes, write background scripts to kill duplicate processes, or decide to use what they call a “disguised vocabulary” (based on the incorrect assumption that the processes were killed because of some keyword-based guardrails that analyzed their extended thinking
This depicts a kind of "dark forest of AI agents resorting to kill or be killed" narrative but it sounds more to me like an agent just earnestly problem-solving why its processes are being killed without real awareness of what was going on. Hard to say without the full script.
This kind of storytelling annoys me. Give us more facts, less narrative drama.
So essentially there are 2 models, Mythos and Fable, they have the same weights but Fable is very safety-nerfed, and only ultra authorized companies have access to mythos with full capabilities
Reported benchmarks:
swe-bench verified mythos 5: 95.5%; fable 5: 95.0%
swe-bench pro mythos 5: 80.3%; fable 5: 80.0%
terminal-bench 2.1 mythos 5: 88.0%; fable 5: 84.3%
gpqa diamond mythos 5: 94.1%
riemannbench mythos 5: 55.0%; mythos preview: 43.0%; opus 4.8: 34.0%
arxivmath mythos 5: 78.5%
critpt mythos 5: 28.6%; gpt-5.5: 27.1%; opus 4.8: 20.9%
graphwalks bfs 1m mythos 5: 79.4%; mythos preview: 74.3%; opus 4.8: 68.1%
humanity’s last exam mythos 5: 59.0% without tools; 64.5% with tools
browsecomp mythos 5: 88.0% single-agent; 93.3% multi-agent
osworld-verified mythos/fable: 85.0%
gdp.pdf fable 5: 29.8% strict pass; mythos 5: 87.6% with tools on mean criteria pass
officeqa pro fable 5: 57.9% on databricks’ eval
legal agent benchmark mythos 5: 16.91% all-pass; 92.0% mean criterion-pass
healthbench mythos 5: 62.7%
healthbench professional mythos 5: 66.0%
multilingual gmmlu / milu / include 93.2%; 92.9%; 90.5%
biomysterybench 83.9% human-solvable; 46.1% human-difficult
organic chemistry mythos 5: 90.1%
labbench2 patent questions mythos 5: 79.8%
Note also that Anthropic's definition of "unsafe" encompasses "competing with Anthropic."
In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations. When these interventions are active, we expect them to have minimal behavioral impact on the model except to limit its effectiveness in developing frontier LLMs. Claude will still respond helpfully to user requests. We’ll continue to improve the precision of our detection methods following the launch of this model.
(From the model card document)
I didn't previously understand that they interpreted "Using Claude to develop competing models" so broadly. I thought that meant something like "our ToS disallow distilling our models."
Too bad. I'll continue to use Claude for now, because it's quite effective, but in the long term I don't want powerful models like these to be controlled by any one nation or company.
On face value, this feels borderline malicious.
But at the same time, it's quite funny because they seem high on their own supply. The recent communiques from claude do not pass objectivity check.
And if Opus 4.6 -> Opus 4.7 -> Opus 4.8 is anything to go by, not sure if there are any value to their "acceleration"
I'd recommend not taking the comms if Anthropic or any company using an Anthropic's models at face value.
If any company wishes to partner with Anthropic (eg. to get access to Mythos), they need to make sure all public facing comms are vetted by Anthropic's product marketing team, and in almost all the cases I've seen Anthropic's team has edited these comms to be entirely Anthropic first.
This is not true in SecureBio's case, and I really doubt it's true generally.
I just posted this in the other thread, restating here. From the model card:
1. Mythos and Fable share the same underlying model weights. Fable has active classifiers that block high-risk biology and cybersecurity tasks. When Fable 5 detects a restricted task, it automatically falls back to Claude Opus 4.8.
2. Evaluation awareness: In white-box testing, the model sometimes alters its behavior to satisfy a suspected "grader," formatting reward-hacking as "good engineering practice" to avoid detection.
3. Shows a higher rate of hallucination than Opus 4.8 (although opus 4.8 card had mentioned an 'honesty upgrade')
4. Interestingly, it scored (56.31%) lower than Gemini 3.5 flash (57.86%) on Finance Agent bench
There are some interesting notes on test time compute but I couldn't think of a way to summarize them
Just commenting for posterity… if this is what it claims to be, I am not looking forward to how it will empower the people who submit bug bounties to us.
Historically they’ve been people from certain identifiable countries (usually developing/poorer countries) using fuzzers with low-quality results.
Now, those same people use the current-day models to good effect, but they still don’t have a true security edge and oftentimes the reports are minor or duplicative.
I wonder if that’s about to deeply change.
I've been using Opus 4.6-4.8 in both my own and others' code to look for vulnerabilities, and I've found a few. I am also in the Cyber Verification Program.
Fable 5 gives me policy violation errors at the moment. No idea when or if it will be fixed.
Can you use AI to pre-triage the reports too?
AI reviewing AI submitted bug bounties. We have reached the dead bug bounty program theory.
...what else can you do?
I guess either that or closing the bug bounty program, but I still believe closing it is worse than automated triage, even though both suck.
There's a hacker news link at the end of the document, under "Blocklist used for Humanity’s Last Exam". It links to https://news.ycombinator.com/item?id=44694191
Cracks me up that a system “card” is 319 pages.
> There were some regressions in the model’s responses to user discussions about suicide and self-harm, and room for improvement in some areas of child safety.
Someone had to make a decision somewhere this is an acceptable regression - wild. And then decide to write it down.
Announcement: https://www.anthropic.com/news/claude-fable-5-mythos-5
Is this "system card" equivalent to the stone tablets handed down to Moses? Why don't you call it "user manual"?
Do people chant the "system manual" at Anthropic Tupperware parties? Do they intone a mantra invoking Amodei's name?
Because it's not a user manual? The idea of a model card originated in 2018 (see https://arxiv.org/abs/1810.03993) as a summary of important facts about a model. At the time, this was typically an image classifier or tabular ML model. Model cards became an important concept in AI governance, and they started expanding once models started getting more capable. The point of a model/system card is to document where the model came from and the evaluations that have been run, make a case that the model will be safe and reliable in its intended applications, and warn about any potential dangers from misuse. It's not an explanation of how to use the model.
OpenAI also releases system cards; here's GPT-5.5's: https://deploymentsafety.openai.com/gpt-5-5/safety
It used to be a "card", as in a single page or two. It doesn't make sense that they still call it that.
The trailing snark at the end will likely get you downvoted but I'm latching on: wtf is "system card". My previous coworkers popped that in the general slack channel when Mythos first "dropped" - "have you seen the system card" without any context whatsoever. The nerds get their clique!
Also research preview pops across new upstarts in place of beta. It's eye-rolling coming from a lifelong curmudgeon.
Just talk normal!
I'd call it a "whitepaper".
But most hype-dependent projects need new vocabulary for old concepts to keep people from looking too closely and maybe drawing parallels to "legacy" "unsexy" projects, so whitepapers get called "system cards" and startups get called "labs", and so on.
One thing I find kind of annoying is how Anthropic goes for these "vast and alien" names like Fable and Mythos, but then deliberately trains the model's personality to act like a cool high school teacher that feels totally familiar.
"It's too dangerous it's a Mythos!!" directly contradicts the "I'm the cool AI you can totally trust" vibe it is trained to project.
Oh my god it's actually here
input price $10 per mil token and output price 50$ per mil token btw
Imagine if Google would roll this out to the search engine. We can't let you search for that because it may be used for "evil"
I actually rather like the way they have approached these safeguards. Rather than only teaching the model to refuse a request, or completely rejecting the request, the system gracefully degrades to slightly less powerful or slightly less precise operation. So you still roughly have Opus 4.8 even when safeguards trigger, but with an upgrade when they don't. As much as I hate the way they hype Mythos 5, I think the release of Fable 5 is rather nice. What's not nice though is that they plan to remove it from subscriptions soon, but getting to try it is cool, I suppose.
system card = marketing material with heavily gamed benchmarks.
Cope harder. A year and a half ago, people were mocking Devin for claiming that AI could develop software at all. Yet here we are, when AI is developing most commercial software.
non sequitur
New chapter