GoKawiilWe gave Claude Fable 5 and GPT-5.6 Sol the same song, a budget, web search, and local ffmpeg, then let each autonomously direct a music video.
We built a small agentic harness with one job: hand a model a song, a hard dollar budget, and a set of tools, then get out of the way and let it produce a full music video on its own. The model researches which video models exist, generates clips, watches its own footage, edits with ffmpeg, and assembles a final cut.
A few readers of our last build-off said they wanted to see how tool use actually varies between models, so we gave frontier-level models an open-ended, long-horizon task where each model decides on its own what to research, what to generate, and how to edit. We log every tool call, so you can see exactly how each one worked (full transcripts below).
We ran two models, Claude Fable 5 and GPT-5.6 Sol, each at two budgets ($25 and $100), for four runs total. Every run got the same song (Bruno Mars and Mark Ronson's "Uptown Funk"), a short text description, and a time-stamped lyric transcript.
The setup
Each model ran an autonomous tool-calling loop with six tools:
plan : a tool for thinking (no cost, no action).
: a tool for thinking (no cost, no action). web_search : to research generation models and their APIs and fetch information about music videos (if needed).
: to research generation models and their APIs and fetch information about music videos (if needed). get_budget : to check the remaining budget.
: to check the remaining budget. generate_image and generate_video : the only tools that spend budget. The model can pick any FAL or Replicate model and pass its own parameters.
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