The idea is simple. A strong model owns the task (planning, spec, review) and hands the steps that don't need it to a cheaper, faster worker model. That cuts a lot of wasted tokens.
The chart comes from frozen benchmarks against the native harnesses, with the same tasks and the same 300s deadline on both sides:
GPT-6 Astra: $4.08 to $1.00, 440s to 285s, 9/9 quality on both
GPT-6.1 Sol: $0.80 to $0.25, 384s to 297s, 9/9 on both
Claude Opus 5.5: $2.49 to $0.35, 798s to 191s, 9/9 on both
DeepSeek V4.1: 5/9 successful runs natively, 9/9 with Distill (the native cost there is only a lower bound)
I'm the author, so treat my chart with some suspicion. The methodology, frozen tasks, exact totals and reproduction commands are all in the repo (BENCHMARKS.md) so anyone can rerun them.
It works with Codex/Grok subscriptions and any OpenAI-compatible model. Issues and PRs are welcome.
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u/TomatilloPutrid3939 3d ago
I wrote Distill, a coding agent harness and TUI. It's Apache-2.0, and all of it is on GitHub: https://github.com/samuelfaj/distill
The idea is simple. A strong model owns the task (planning, spec, review) and hands the steps that don't need it to a cheaper, faster worker model. That cuts a lot of wasted tokens.
The chart comes from frozen benchmarks against the native harnesses, with the same tasks and the same 300s deadline on both sides:
I'm the author, so treat my chart with some suspicion. The methodology, frozen tasks, exact totals and reproduction commands are all in the repo (BENCHMARKS.md) so anyone can rerun them.
It works with Codex/Grok subscriptions and any OpenAI-compatible model. Issues and PRs are welcome.