Asana cuts model costs 76x in browser tests with GPT-6.1 Sol

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.

“This is what teams of humans and agents look like in practice. An engineer set the direction, GPT-6 Astra ran the experiments, and the results went through Command to production. This demonstrates how Asana brings human and agent teams to life.”

—Arnab Bose, CPO at Asana

Identifying browser-agent inefficiencies with GPT‑6 Astra

To move quickly, Hidalgo started by using GPT‑6 Astra in Codex to map the codebase and explain how the agent built each model request. GPT‑6 Astra discovered that the agent cached its fixed instructions and tool definitions, but not the growing history of page text and screenshots it gathered, so every request resent that history at full price.

From an estimated two months of research to one week with GPT‑6 Astra

Hidalgo reviewed GPT‑6 Astra’s proposed fixes and selected three to test:

—Frank Hidalgo, PhD, StackAI CTO at Asana