Summary
Nate Herk runs GPT-6 Astra (via OpenAI Codex) and Claude Fable 5.1 head-to-head across 15 use cases drawn directly from his production workflow, recording time elapsed and dollar cost for every task. The comparison is deliberately practical rather than benchmark-synthetic: McKenzie-style consulting presentations, meeting transcription and analysis, event recap video creation from 150 GB of raw footage, web design, browser automation, vision tasks, tax document work, and building multi-step software automations.
The cost and time data is granular throughout. In the event recap test — creating a 60-second energetic reel from AIS Live conference footage — Astra completed the task in 30 minutes at $16 while Fable took 50 minutes at $26, with Astra edging the creative quality as well. In meeting analysis, Astra processed more meetings in just six active minutes, though the token accounting left Herk puzzled about where the costs went. The consulting deck goes to Fable: more professional slide structure, consistent branding, a footer on every slide, and better visual hierarchy versus Astra’s wordier but less presentation-ready output. The final scoreboard shifts throughout, with neither model dominating cleanly.
Herk’s overall conclusion is that both models have cleared a capability threshold where raw intelligence rarely decides the outcome — the real differentiators are output formatting, how each model handles ambiguity (Astra asks clarifying questions more often; Fable tends to run immediately), and cost efficiency at scale. Timestamps are included for each of the 15 use cases so viewers can jump directly to the tasks most relevant to their own workflows.
📺 Source: Nate Herk | AI Automation · Published September 06, 2026
🏷️ Format: Comparison
