Claire from Lenny’s newsletter puts Claude Fable 5, Anthropic’s first generally available Mythos-class model, through a series of real-world tests : product specs, agent workflows, design tasks, vision tasks, and multi-agent orchestration. She breaks down what Anthropic is claiming, where the model genuinely feels like a leap forward, and where it surprisingly falls short.
According to Claire,
Fable 5 is Anthropic’s first “Mythos-class” model to reach general availability, and it’s crushing benchmarks across the board. It hit 80% on SWBench Pro, significantly outperforming Opus 4.8, GPT-4.5, and Gemini 3.1 Pro. Claire found the model excels in specific areas while falling short in others that matter for everyday product work.
The model is expensive by design : $10 per million input tokens and $50 per million output tokens. That’s a new tier above Opus, and it consumes tokens at roughly twice the rate of other models. You need to be strategic about when to deploy this level of intelligence versus using cheaper models like Sonnet or Opus for simpler tasks.
Fable 5 works like a “seasoned engineer”—which is both its superpower and its Achilles’ heel. It’s thorough, autonomous, and will investigate every corner of a problem to be 120% sure it’s shipping the right thing. Sometimes you need a model that’s a little less thorough, a little “dumber,” to actually ship something useful quickly.
The model is exceptionally good at vision tasks, particularly document formatting and PDF parsing. Claire tested it on creating handwriting worksheets for her 7-year-old and found it dramatically outperformed Opus 4.8—better spacing, clearer layout, appropriate white space. This extends to other vision tasks where you want something to look good or need to parse complex documents.
The writing is nearly unreadable for specs and PRDs. Claire found that Fable 5 produces extremely detailed, technically complete documents that are almost impossible to parse. It gets wrapped around the axle on details, creates big blocks of dense paragraphs with internal references, and makes it hard to see the forest for the trees.
Design output is shockingly bad, at least for one-shot design tasks. When Claire asked Fable to design a skills registry, it produced fundamentally terrible design : gray, black, red, simple outlines. This was a real surprise given the model’s benchmark performance.
The model is conservative on execution and takes “minimal” very literally. When Claire asked it to ship an MVP that would deliver customer value, Fable produced something extremely narrow and not actually that useful. This conservatism may stem from the safety guardrails built into the model.
Fable 5 includes specific safeguards for cybersecurity, biology, chemistry, and distillation tasks. Instead of blocking you entirely, it uses a new “fallback” concept—if you get classified into one of these categories, it gracefully falls back to Opus 4.8. Anthropic reports that 95% of sessions don’t hit a fallback, and they maintain a 30-day retention policy solely to catch misuse.
Multi-agent orchestration is technically possible but not yet reliable. Claire tested the dynamic workflows and subagent capabilities extensively and had some successful multi-agent runs, but also encountered frequent stalls and errors. She walked away from her laptop and came back to find subagents had stalled after about three hours.
The key insight : match model intelligence to task complexity. Claire recommends using it for hard technical problems where extreme detail matters, long-horizon work, and vision tasks. But for front-end work, strategy, specs, and design, other models in the ecosystem will serve you better and cost less.
To conclude, This is “baby Mythos,” not the full Mythos model. Fable 5 has guardrails that the unrestricted Mythos model (available only to Project Glasswing partners) doesn’t have. The underlying model is the same, but Fable is tuned for safety and general availability.
