Models & Labs · United States

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

Credited to OpenAI · openai.com · 16h ago

Using GPT‑6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.

Lower estimated model costs with the optimized GPT-6.1 Sol workflow

Faster browser runs with the optimized GPT-6.1 Sol workflow

Average estimated model cost with the optimized GPT-6.1 Sol workflow

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’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.

“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.”

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.

The agent also dropped older screenshots and trimmed text at nearly every step. Each edit altered the history, so caching the history alone would not have helped, and losing those facts could require the agent to revisit pages it had already read.

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:

Extending caching to the agent’s browsing history

Increasing the amount of text it could retain

Removing screenshots in batches rather than at every step

GPT‑6 Astra began with quick tests to establish which variables mattered. Because the code was not designed for controlled experiments, it then refactored the code so one frontend and backend could support many workflows in parallel, each with its own settings.

Astra conducted the full study: history budgets of 120,000 and 480,000 characters and six caching and screenshot policies, each tested three times on each of the four models (see the table below). The best-performing policy allowed screenshots to accumulate to 20 before cutting back to the most recent one. This kept earlier history unchanged for longer stretches between removals. Combined with the larger history budget, it became the optimized workflow. Each configuration performed the same task: collecting six fields for each of 32 books from a public demo catalog, representative of what some Asana customers run in StackAI.

Model A — A smaller, less expensive model from another frontier lab, released Fall 2025; Half the price of GPT‑6.1 Sol

Model B — The model originally used in production, from the same lab as Model A, released Summer 2026; Same price as GPT‑6.1 Sol

Model C — An updated version of Model B, released Fall 2026; Same price as GPT‑6.1 Sol

“This would have taken me one to two months by hand. With GPT-6 Astra in Codex, it took about a week: I’d set a /goal before going to bed and review the results in the morning.”

Bringing model cost below $0.50 per run

For Model B, the optimization reduced estimated model cost from at least $36.21 (some original runs reached the step limit before they finished) to $1.24 per run, a reduction of 29x. The optimized workflow on GPT‑6.1 Sol was 2.6x cheaper still, at $0.47. Every run in the optimized workflow completed the task and returned the correct answer.

Means of 3 runs. ≥: the baseline includes capped runs, so its mean is a lower bound.

The two right folds compare against Model B optimized. Model B ran in phase 1, Model C and Sol 6.1 in phase 2 of the same study (dotted line).

On GPT‑6.1 Sol alone, with the larger history budget, the new caching and screenshot policy reduced cost 4x, from $1.97 to $0.47 per run. Each call was about 3x cheaper, because 89% of the input came from cache at 5% of the uncached price. Runs also became faster: at least 22.5 minutes on the original setup on Model B, roughly four minutes with the optimized workflow on GPT‑6.1 Sol.

Mean of 3 runs, SD whiskers. ≥: mean includes a capped or unfinished run, so the true value is at least this large.

Bars use the blue theme. Read caching effects against the 480k larger-budget bar.

Run markers and SD whiskers are approximate reconstructions from the source image; underlying run values and standard deviations were not available.

The investigation also showed how history management affected whether the agent produced an answer at all. Giving GPT‑6.1 Sol more room to retain its browsing history increased the number of runs that produced an answer from three of 18 with the smaller history budget to all 18 with the larger budget, each with the correct answer. For Hidalgo, the business value is giving customers access to faster, more capable models while keeping operating costs sustainable.

“Cost used to limit which models we could offer customers for these workloads. By making the agent more efficient, we can give customers a better, faster model while lowering our operating costs.”

Scaling experimentation and product testing

Asana has released the changes to browser navigation in StackAI and is developing tools to make similar experiments easier to repeat. Over time, the team plans to incorporate this testing into the platform’s evaluations, so customers and internal teams can compare cost, runtime, and answer quality when configuring their agents.

“Shipping speed is no longer the bottleneck; human attention is. We’re close to a world where every engineer is a PM leading a fleet of agents.”

Asana is now using GPT‑6 Astra in Codex to test product features before release: Astra navigates the platform, tries different inputs and reports bugs for human QA reviewers. Hidalgo views this as the foundation for a new software development lifecycle, with many cloud agent sessions testing features in parallel.

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Anthropic's Claude can now orchestrate up to 1,000 AI agents in parallel through dynamic workflows

Credited to The Decoder · the-decoder.com · 5h ago

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Anthropic is adding dynamic workflows to its Claude Managed Agents, bringing multi-agent orchestration to the platform. The managed agent infrastructure has been around for a while, but dynamic workflows are new. A lead agent creates a plan, distributes tasks to sub-agents, and merges the results when they're done. Up to 1,000 agents can run in parallel per execution.

Whether this is cost-effective seems debatable. A senior OpenAI engineer recently called agent swarms a massive waste of tokens, but Anthropic points to its own testing: The team hid 70 bugs in a 116,000-line codebase and found that a single agent caught between 14 and 27 per run, while the dynamic workflow consistently hit 66. Whether those gains hold across different task types remains to be seen, so, as always, it's worth testing with your own workloads.

To activate dynamic workflows, select the "multiagent_20261001" agent type. Because these workflows can burn through "a lot of tokens," Anthropic recommends starting small. You can get going through the documentation or by running "/claude-api managed-agents-onboard" in Claude Code. Ad Ad

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