Claude Fable 5 and new safety fables - by Nathan Lambert

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Claude Fable 5 and new AI safety fables<br>One step further into the power politics of frontier AI systems.<br>Nathan Lambert<br>Jun 09, 2026

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Audio playback is not supported on your browser. Please upgrade.Today, Anthropic released their Claude Fable 5 model to consumer and enterprise audiences. This is the general-access variant of their Mythos-class models. With it, Anthropic rolled out a series of safety measures — some explicitly called out to users and some modifying the model without telling the user. It should be less surprising than it is that the next major step in AI capabilities came with heavier-handed safety measures indicating Anthropic’s intention to protect, or entrench, their current lead.<br>The unevenly applied safety policies that Anthropic have rolled out are on track to become a classic cautionary fable in how narrow and self-fulfilling notions of safety and control rarely work out.<br>The smartest model in the world

Before digging into the nuance of the safety facts, it is important to establish the quality of this model. The quality of the model paints the stakes of today — as these safety features are meaningfully changing the shape of access to frontier AI, something which has never happened with the modern LLMs we know. Second, the capabilities point to this story only accelerating. Recursive self-improvement isn’t quite the right mental model of progress from here, but Claude Fable 5 should make it very clear that there are no immediate walls in training LLMs.<br>To start — Claude Fable 5 is definitely the smartest model available to the general public — a remarkable leap on pretty much every relevant benchmark of the day — at only 2X the price of current Opus models1 (which is still less than GPT 5.5 Pro’s variant). This alone is a seminal moment for the field. To have a model iteration take such a substantial step in capabilities, a few years into the post-ChatGPT LLM race, is astounding. There’s no clear breakthrough associated with this model, such as inference-time scaling or RL, and public wisdom is that this is achieved by advances across the whole stack (of course, we can’t know for sure — it’s not documented). This is a major technical achievement and the employees who built the model should be very proud of their work.<br>This model was delayed 2+ months after it was done training before it was publicly available2. Given the competitive dynamics of the AI economy, the smarter version of this model is already well underway.<br>To continue, the benchmarks for the model are below.

An asterisk on these scores is that these aren’t necessarily the scores that the public will get, as some of the prompts will be downgraded to Opus 4.8 with the current safety filters on the model.<br>This is the type of jump in benchmark scores where I don’t even need to substantially test the model to know it’s an incredible tool. Remember that Anthropic is also the AI lab with the track record of caring the least about benchmarks (in particular, when compared to OpenAI and Gemini). Recall a comment I made in June of 2025:<br>This is a different path for the industry and will take a different form of messaging than we’re used to. More releases are going to look like Anthropic’s Claude 4, where the benchmark gains are minor and the real world gains are a big step. There are plenty of more implications for policy, evaluation, and transparency that come with this. It is going to take much more nuance to understand if the pace of progress is continuing, especially as critics of AI are going to seize the opportunity of evaluations flatlining to say that AI is no longer working.

Clearly, a few pieces of the progress dynamics have changed, but that’s a post for another day. I’ve written multiple posts about new models this year specifically in how it’s hard to trust benchmarks (and partially because the benchmarks don’t move that much). Altogether, this is a major validation for AI-savvy workers who realized they’re likely never going to write meaningful code again and need to develop new workflows around agents.<br>Interconnects AI is a reader-supported publication. Consider becoming a subscriber.

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Smarter models spawn new safety games

There are multiple pieces of safety tooling associated with this release, including but not limited to required data-retention policies and added prompt filters. Through this analysis it is particularly important to be precise and clear as to which pieces of these are causing harm, and why single elements being out of place in an otherwise comprehensive policy are so damning for the overall safety process.<br>For their focus areas of cybersecurity, targeted model distillation, and research biology, Anthropic details new safety classifiers in their blog post:<br>Fable 5 comes with a new set of classifiers: separate AI systems that detect potential misuse, including...