GoKawiilToast 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.
Waterfall trace of a Toast 1 agentic search: 16 tool calls across 3 rounds answering an employment-rate comparison query in just over 5 seconds. Expand the trace, then select a step to see the sub-query, grep pattern, or plan the agent produced at that point. “ How did the employment rate change in retail compares to the healthcare sector? ” 16 tool calls · 3 rounds · 5.33s · view trace
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.
Scatter plot of answer correctness versus cost per rollout on OfficeQA Pro V2, log-scale cost. GPT-5.6 Sol running in Codex with Toast 1 as a sub-agent reaches 70 percent correctness at about $1.20 per task, above the previous Pareto frontier from the Databricks evaluation, where Claude Fable 5 on Databricks Genie reaches 60 percent at about $4. Cost–quality Pareto on OfficeQA Pro V2 20 % 30 % 40 % 50 % 60 % 70 % $0.1 $0.2 $0.5 $1 $2 $5 $10 $20 $50 Answer correctness Cost per rollout (log scale) GPT-5.6 Luna GPT-5.6 Terra Claude Fable 5 GPT-5.6 Sol Kimi K3 GLM 5.2 Sonnet 5 GPT-5.6 Luna (Codex) GPT-5.6 Terra (Codex) GPT-5.6 Sol (Codex) Sonnet 5 (Claude Code) Claude Fable 5 (Claude Code) GPT-5.6 Sol Low (Codex + Toast 1) GPT-5.6 Sol High (Codex + Toast 1) Databricks Genie Model provider harness Pareto frontier (Databricks) Codex + Toast 1 Answer correctness vs. cost per rollout on OfficeQA Pro V2. Genie and harness numbers as reported by Databricks; Codex + Toast 1 runs are ours. Shaded region sits under the previous Pareto frontier.
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.
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