// HACKER NEWS — CYBERSECURITY
Introducing Toast 1
Toast 1, our first specialised search agent, is available today. It provides frontier search quality, matching or outperforming Claude Opus 5 and GPT-5.6 Sol while being up to 10× cheaper and 12× faster. It performs best with Mixedbread Search, but it can work with any search backend.
Today, frontier models are now able to perform real knowledge work. They can reason, analyse, and find information in complex document collections. But they are also the most expensive models in the stack. As intelligence is increasingly metered, the need for specialised agents able to match their capabilities at a fraction of the cost is greater than ever.
Toast 1 can run as a standalone specialized retrieval agent, or as one of many subagents your frontier model already knows how to rely on. It fully takes over the search loop: given an initial query, it decomposes it into subqueries, gathers evidence, inspects sources, and curates the relevant context before returning it. This lets your agent spend its context and compute on the task that requires a generalist, frontier-level model: reasoning, acting, and producing the final answers.
This specialisation of agentic labor results in considerably cheaper search, but also in better end-to-end results on many realistic tasks. We found that Toast 1 establishes a new Pareto frontier across agentic workloads across cost per task and speed per task.
OfficeQA Pro V2, released by Databricks, evaluates answer correctness across 90 questions in realistic, complex enterprise financial situations.
GPT‑5.6 Sol with Toast 1 made available as a sub-agent within Codex reaches 70% answer correctness at approximately $1.15 per task: that is the highest score among the systems evaluated by Databricks in the OfficeQA v2 release, establishing new state-of-the-art performance in both quality and efficiency.
By comparison, the previous best performer, Claude Fable 5 on Databricks Genie, reaches 60% correctness at approximately $4 per task, while GPT-5.6 Sol within Codex without Toast 1 only reaches 33% correctness.
This improvement stems from reformulating the economics of evidence gathering. Toast 1's specialization allows it to produce high-quality, token-efficient evidence packages, leaving ample resources for the reasoning process to reach the final answer.
Harvey LAB's Law Firm Knowledge benchmark seeks to evaluate how well an agent can search and use institutional legal knowledge at large, realistic scales.
Legal work, by nature, is context-heavy. You cannot outargue someone with access to better, more relevant precedents and details. But it is also noisy: many situations are similar but vary by simple details, making it tricky to collect high quality evidence packages without numerous false positives.