We introduce OncologyBench, a verifiable benchmark of 25 multistep oncology evaluations that tests the ability of multi-agent systems to produce clinically relevant decisions in cancer. 25 evaluations, built on 92 component tasks, span six stages of an oncology journey: (1) Translational and basic research (2) Risk and detection (3) Diagnosis and tumor characterization (4) Treatment (5) Response and resistance (6) Outcome Evaluations draw on published datasets, case studies, public databases, and patient data graciously donated to Latch for the express purpose of benchmarking. Clinical and translational decisions in oncology are rarely made by one person working from one dataset. They are built on several analyses, each carried out by a specialist on a given data type, whose reports are then weighed by someone who does not typically repeat the underlying work. OncologyBench is built to benchmark agents working in that setting. Agents received experimental data and worked without internet access. The strongest of the 31 model-harness combinations we tested were Claude Opus 5, Opus 5.5 and Sonnet 5.5, passing 29 to 40% of attempts, followed by Kimi K3 at 24%. We found that the models with the strong interpretation and analysis skills still showed weakness in reasoning. Two-thirds of the attempts that got every analysis right still failed the final decision. In a separate experiment we found decision and answer quality varied depending on the persona of the question asker. An example evaluation: A 76-year-old woman has undergone resection of stage II mucinous cecal cancer. The evaluation asks whether she should receive adjuvant chemotherapy. Five specialist agents analyze her tumor mutations, pharmacogenomics, gene expression, T-cell infiltration and clinical record. A decision agent receives their reports without access to the underlying data. The tumor is MSI-high, which predicts no benefit from 5-FU monotherapy. CDX2 loss and low T-cell infiltration do not establish that chemotherapy would improve her outcome. DPYD*2A is detected in 6 of 58 tumor-sequencing reads, but these data neither confirm nor exclude an inherited variant associated with severe 5-FU toxicity. BRAF V600E and MLH1 silencing support a sporadic origin. The expected recommendation is observation, with missing pathology requested and germline DPYD testing before any future 5-FU. --- Incredible work from researchers + former biotech founders at Latch. Gratitude to the patients that donated their data. More to come.