Fable (Mythos) dropped from its originally announced ~$125 per million output tokens to ~$50, making it 30–40% more expensive than Opus but often cheaper due to lower token usage.
Where this was said
At 55:13 · chapter starts 54:50
The pricing story around Fable is revealing. It launched at roughly $125 per million output tokens, then dropped to $50 — still 30–40% more expensive than Opus on a per-token basis, but Fable uses significantly fewer tokens per equivalent task. That means Fable often ends up cheaper than Opus in practice, which Theo argues is the death knell for Opus as a standalone model. [1] — Theo "Fable dropped from $125 to $50 per million output tokens, making it 30–40% pricier than Opus per token — but far cheaper overall because it…" 54:50 He's already made peace with paying API prices once the subscription window closes: he spent $150–$200 closing over 30 PRs in a single 5-hour session, and frames it as a magic button he'd press twice a day if he could. Ben agrees that any serious company would make the same calculation. The conversation turns to whether Opus 5 can survive in a world where Fable is this affordable — both hosts think Opus 5 needs to be priced lower than current Opus just to remain relevant.
Fable dropped from $125 to $50 per million output tokens, making it 30–40% pricier than Opus per token — but far cheaper overall because it uses fewer tokens. The result: Opus is dead. Theo won't maintain a $200/month sub for Opus alone, and thinks Opus 5 needs to be cheaper or it won't survive either.
Ben speculated that Fable (Mythos) is likely a 5 to 10 trillion parameter model, making it the largest publicly accessible frontier model by a wide margin.
Ben accidentally ran Fable subagents on xHigh reasoning, burning through roughly 30% of a weekly usage allocation with negligible useful output.
Running Fable subagents from a Fable orchestrator is a fast path to burning 30% of your weekly limit on nothing useful. The right pattern is Fable commanding cheaper models — Sonnet 5, GLM-5.2, or similar — for implementation work while Fable handles planning, context, and review.
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
Eyal and Yali shut down all marketing and spent 4 months completely rebuilding PropGPT from scratch.
PropGPT has accumulated over 40,000 total downloads since launch.
PropGPT's large language model (AI) operating costs are just $20 per month, and the cost is continually falling.
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
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