SubQ Snap does well in what TypeSafe calls the "Danger Zone", context above 32k. 85% of tokens processed by Snap are from requests above 32k, with many shipping data-intensive features to prod. SSA keeps long context fast and cheap. We are open-sourcing Decision LCR (Snap 74%, Jev 58%), which mimics real-world tasks and is adapted from AA-LCR. Because long-context benchmarks for decision models don't really exist, we built one of our own by adapting Artificial Analysis' Long-Context Reasoning benchmark, which we think does a better job mimicking real-world tasks than many LLM long-context benchmarks, to a decision format. We are open-sourcing this today. Decision LCR: Adapted from Artificial Analysis's AA-LCR. 100 human-written questions over real 72K–115K-token document sets, converted into 118 decisions, each run with three option orderings to control for position bias. Chance is 19.2, and models score 24–25 with the documents removed. We'd love for others to run it, use it and improve it. If you work on long-context evaluation or decision models, we'd value your feedback and collaboration. To learn more about SNAP and our evaluation process, check out the blog: