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Benchmarks/deepswe-v1-1

DeepSWE v1.1

2026-09-22 · General long-horizon engineering

What it measures
General long-horizon engineering: feature-sized changes in real open-source repos. 113 tasks across Go, Python, TypeScript, JavaScript, Rust, and more, spanning 91 active repositories. The agent commits in an isolated environment and is graded in a pristine container. On this site it is a general board and appears in the Web frontend, Android, and CLI / DevOps scenarios.
How
Datacurve's DeepSWE v1.1. Tasks are written from scratch, not adapted from existing commits or PRs. All public scores use Pier with mini-swe-agent. This site follows the official Best view: one row per model at its highest Pass@1 reasoning tier; ties break toward higher effort.
What the score means
The percent is Pass@1: attempt pass rate over scored rollout attempts. Context-window failures and agent timeouts count as failures; provider, verifier, and network errors are excluded. Higher is better.
Caveats
The public Best chart hides some older models; this page keeps all 28 models at their Best config. Every row uses mini-swe-agent, so native CLIs such as Claude Code or Codex are not compared. The cost column is mean per-task cost from the source JSON; the site UI may round costs differently.
Updates
The source board is marked updated 2026-09-22; artifact generated_at is 2026-09-22T06:27:15Z. This page synced 28 Best-config rows from https://deepswe.datacurve.ai/artifacts/v1.1/leaderboard-live.json on 2026-10-11.

Full board

28

#ModelScore

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