A user experimenting with the Fable 5 model for autonomous coding encountered unexpectedly high input token consumption, with usage escalating from 50k to over 168k tokens per reasoning step. This led to significant API costs, prompting the user to question their model choice and configuration. While ruling out project size and basic setup issues, a suggestion from GPT proposed a planner-executor architecture, using Fable 5 for planning and a cheaper coding model for execution, to potentially reduce costs. AI
IMPACT High token consumption in models like Fable 5 could impact the cost-effectiveness and adoption of autonomous coding agents.
RANK_REASON User-generated commentary on a model's performance and cost, not an official release or benchmark.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
