Benchmarks
331 sources · 1,258 models × 2,980 benchmarks · 155,643 scoresLongtail-SWE (Nebius, the SWE-rebench team) asks coding agents to resolve real GitHub issues in 13 programming languages that existing issue-resolution benchmarks barely cover: Clojure, Dart, Elixir, Erlang, F#, Haskell, Julia, Kotlin, Lua, OCaml, R, Scala and Swift. Its 202 tasks from 129 repositories each come from an issue and its merged pull request, with a Docker environment and tests; a solver-judge-repairer pipeline revised descriptions and tests that rejected valid solutions or accepted wrong ones, and tasks that Qwen 3.8 27B solved in all three calibration attempts were generally dropped. An attempt succeeds only when every fail-to-pass and pass-to-pass test passes. Pass@1, the headline, is the success rate over three attempts per task. Scores are for a model in the authors' SWE-agent-like scaffold, not for the model alone.
No signal or difficulty estimate. Too few models have been measured on this benchmark to estimate its difficulty.
| # | Model | Setting | Score | Source |
|---|---|---|---|---|
| 1 | Claude Opus 5.5 | high · SWE-rebench swelike scaffold; 1M context | 81.5 | |
| 2 | GPT-6.1 Sol | high · SWE-rebench swelike scaffold; 1M context | 78.4 | |
| 3 | GPT-5.6 Sol | high · SWE-rebench swelike scaffold; 1M context | 75.7 | |
| 4 | GPT-5.6 Luna | x-high · SWE-rebench swelike scaffold; 1M context | 71.5 | |
| 5 | Kimi K3 | max · SWE-rebench swelike scaffold; 262K context | 63.7 | |
| 6 | GLM-5.3 Flash | not reported · SWE-rebench swelike scaffold; 262K context | 48.2 | |
| 7 | Qwen3.6 27B | thinking · SWE-rebench swelike scaffold; 262K context | 16.7 |