Asana cuts browser-agent costs 76x with GPT-6.1 Sol, optimized by GPT-6 Astra in Codex
Frank Hidalgo, StackAI CTO at Asana, used GPT-6 Astra in Codex to profile and refactor the company's browser agent, producing an optimized workflow on GPT-6.1 Sol that averaged about $0.47 in estimated model cost and roughly four minutes per run across a 144-run study — 76x cheaper and 5x faster than the original production setup on an anonymized 'Model B'. He estimates the work would have taken one to two months by hand but took about a week. The gains came from extending caching to browsing history, raising the history budget to 480,000 characters and batching screenshot removal; Asana says the browser-navigation changes have shipped in StackAI.
The case study suggests careful history and caching management can matter more than switching models for agent operating costs, though the figures come from a single vendor-published test.
With GPT‑6 Astra in Codex running experiments, Asana optimized its browser agent’s workflow on GPT‑6.1 Sol to run 76x cheaper and 5x faster.
Asana helps customers automate work across business applications through StackAI(opens in a new window), a platform it acquired(opens in a new window). Using StackAI, customers can build workflows that navigate websites, fill out forms, and gather information without writing code. At Asana’s scale, small inefficiencies in these workflows add up.
Asana’s StackAI CTO, Frank Hidalgo, PhD, set out to make the browser agent faster and cheaper to run. He directed GPT‑6 Astra in Codex to investigate the agent, test improvements, and compare the results. Work he estimates would have taken one to two months by hand took about a week.
Asana’s 144-run study(opens in a new window) tested GPT‑6.1 Sol and three other frontier models, called here Models A, B and C. The optimized workflow that emerged on GPT‑6.1 Sol averaged $0.47 in estimated model costs and about four minutes per run, 76x cheaper and 5x faster than the original production setup on Model B.