Let the tokens flow. Sail powers efficient, reliable deep research

Sail reports that its agentic AI research system achieved 90.72% accuracy and 84.31% recall on the BrowseComp-Plus benchmark, approaching or exceeding cited closed-model results while claiming inference costs of about $0.15 per query, 6 to 35 times lower than several alternative providers. Its approach uses an orchestrator model, GLM-5.1, to formulate searches and reason over condensed findings, while a large parallel swarm of less expensive GPT-OSS-120B reader agents examines retrieved documents, filters irrelevant material, summarizes evidence, and requests additional context when needed. Sail argues that this division prevents the main agent’s context from becoming overloaded, allows broader retrieval with simpler tools such as Qwen3-Embed-8B and BM25, and makes token-intensive background research more economically feasible. The reported run processed roughly 6.47 billion tokens, with nearly all of them consumed by reader agents, illustrating the company’s view that efficient infrastructure for long-running, unattended agent workflows will be increasingly important as deep research systems scale.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.