Fable Trailer Reveals Release Date, Hayley Atwell as Its Villain

White House science advisor Michael Kratsios said that Moonshot, the Chinese company behind the Kimi K3, the largest available open-weight LLM, built its model by copying Anthropic’s Fable LLM while using chips that aren’t cleared for export to China. “Large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable,” Kratsios wrote, amid reported discussions about banning Chinese open-weight models that have roiled the AI sector. Moonshot did not respond to questions about its training process, and Kratsios did not share more details about the sources of his allegations. Kratsios’ tweet echoed comments from Treasury Secretary Scott Bessent that “we are finding watermarks of our U.S. large language models on many of the Chinese models, and that that’s unacceptable.” It’s not clear what those watermarks consist of, and the Treasury Department did not respond to a query. However, experts are skeptical that distillation—the process of querying an LLM to determine its inner workings and copy its capabilities—is responsible for the advanced capabilities that Kimi K3 displays. “I don’t think you get a model this strong and this quickly on the heels of Fable doing strictly distillation,” Braden Hancock, a researcher at the Laude Institute and co-founder of Snorkel AI, told TechCrunch. “There’s just not even frankly time, right? Fable’s only been publicly available since July 1st. You can’t distill that much data, train a model, and release it in two weeks.” “I’ve been of the opinion that distillation has becoming less and less impactful over time as the Chinese models get closer to the frontier and the training regime shifts to [reinforcement learning],” Nathan Lambert, an AI researcher at the Allen Institute for AI, said in a podcast released yesterday. “[I]f it were the case, everyone would be easily able to catch up to a GLM or to a K3 by using its data for distillation. But we have not, or we won’t see this, from supervised fine-tuning alone.” Performing distillation requires a lab to systematically query its target model in order to generate data that can be used for post-training. Sometimes this explicitly involves asking the model to articulate its chain-of-thought to understand how it solves problems. Other times, the prompts and responses from a model are used to train a new model in a process called supervised fine-tuning, or SFT. It’s this fine-tuning process that can result in a model ostensibly created by a third party claiming that it is Claude. Fine tuning is where, in Lambert’s view, the “model picks up its manners.” But Lambert says that the benefits of SFT are becoming less important as models become more complex. To distill Fable-like capabilities would likely require reinforcement learning techniques. In many cases, that means having an agent of the larger model grade the smaller model’s responses, and adjusting based on the grade. The more advanced techniques also require more significant infrastructure. Large reinforcement learning runs can require tens of millions of agents. Using a frontier lab’s API to do that “would be insanely expensive and potentially it would probably be a time bottleneck because these models are pretty slow and to be frank might not even give you a performance uplift.” It seems likely that previous frontier models might have contributed to Kimi; Anthropic publicly accused Moonshot, DeepSeek and MiniMax of systematically distilling its models earlier this year. Anthropic said it discovered millions of exchanges between its models and users it identified at those companies through IP addresses and other meta data. Those queries were “distinct from normal usage patterns, reflecting deliberate capability extraction rather than legitimate use.” Anthropic didn’t respond to TechCrunch’s queries about Fable distillation. However, distillation is seen as common among AI companies, not just in China. Elon Musk te

Anthropic is bringing its most powerful AI model to the general public for the first time, but it’s doing it with guardrails. On Tuesday, the AI firm launched Claude Fable 5, the first publicly available version of its Mythos model. Anthropic says Fable 5 excels at software engineering, knowledge work, and vision, but it comes with hard safety limits. In high-risk areas like cybersecurity, biology, chemistry, and distillation, the model blocks responses and falls back to Claude Opus 4.8. Launched as a preview in April, Mythos was initially limited to a handful of partners due to cybersecurity concerns. Last week, Anthropic expanded access to hundreds of organizations across 15 countries, again focusing on organizations that manage critical infrastructure. Now, a version of that technology is available to anyone through Anthropic’s Claude API and consumption-based Enterprise plans. Access on subscriptions will roll out in stages: through June 22, Fable 5 is be included in Pro, Max, Team, and seat-based Enterprise plans at no extra cost. On June 23, Anthropic will pull Fable 5 from those plans, requiring usage credits going forward, with plans to restore it as a standard subscription feature as soon as possible. Anthropic is also deploying a new version of Mythos, called Mythos 5, to organizations that have already been approved to access the advanced model. Fable’s launch comes as Anthropic prepares to enter the public markets, alongside OpenAI and Elon Musk’s SpaceX. It also follows the AI firm’s plea urging major global AI labs to establish a coordinated brake pedal on frontier AI development. Anthropic warned that systems are advancing so rapidly that they may soon achieve recursive self-improvement (RSI), autonomously improving themselves without human intervention. Wary of what a Mythos-class model could do in the wrong hands, Anthropic says it stress-tested its classifiers with jailbreak attempts before releasing Fable 5. “Internally, we ran an external bug bounty that produced no universal jailbreaks in over 1,000 hours of testing. We then worked with external red-teaming orgs which also failed to find universal jailbreaks.” That said, there could still be novel attacks remain possible. As a result, with the launch of Fable 5 and Mythos 5, Anthropic said it will require a 30-day retention on all traffic, even if enterprises previously had zero-retention agreements. Anthropic said it won’t use the data for training, only to “defend against complex and novel attacks, including new jailbreaks,” and “identify and reduce false positives.” The policy could set an industry precedent in which access to increasingly powerful models comes with mandatory data retention policies framed as a safety measure. For those that continue to use the model, not every question will get a Fable 5 answer. Anthropic says the cases in which Fable has to defer to Opus 4.8 are rare, with early data showing at least 95% of Fable sessions running entirely on the model’s own responses. In third-party testing, analytics company Hex said in a statement that Fable was the first to get a 90% on its core analytics benchmark of complex, long-running analytical tasks. “On the hardest questions, it shows strong judgement and attention to nuance,” Hex said. Vibe-coding platform Base44 noted in a statement that Fable is better at “one-shotting full apps” and has excellent tool-calling. AI-powered workspace and agent platform Genspark said Fable beat every other model in its evaluations, and performed significantly better on tasks like UI design and game coding. Pricing for both Fable 5 and Mythos 5 is $10 per million input tokens and $50 per million output tokens, double the price of Opus 4.8. That price alone might serve as a deterrent for widespread use. Many enterprises are growing critical of AI costs after seeing the bills come in or blowing through their yearly AI budgets early. Advanced models like Opus 4.8 can exacerbate those issues, with

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