Benchmark · agentic

SWE-rebench

12 results 12 models

SWE-rebench is a continuously refreshed variant of the SWE-bench coding benchmark that pulls tasks from recent GitHub issues to reduce the chance a model saw them during training. It reports the percentage of software-engineering tasks a model resolves (% resolved).

Read more
Example
A typical item hands the model a real bug report or feature request from a GitHub repository, and it must edit the project's code to resolve that issue.
Scoring
The metric is % resolved: the number of tasks the model fixes divided by the total number of tasks, expressed as a percentage.
Verification
A solution is accepted automatically when the repository's own test suite passes on the patched code — the fix must turn the failing tests green without breaking the others.
Why it matters
Because its tasks are refreshed from recent issues, it gives a cleaner read on genuine coding skill that is harder to inflate by memorizing older, public benchmark data.
Worked example
Task
SWE-rebench instance built from a real GitHub Python repo (python-slugify), pinned to a base commit. Issue: slugify('Hello, World!') returns 'hello--world' — consecutive separators yield a doubled hyphen instead of one, but the expected slug is 'hello-world'. Deliverable: a git-diff patch against the repo snapshot that makes the FAIL_TO_PASS test pass while all PASS_TO_PASS tests stay green.
Solution
--- a/slugify/slugify.py
+++ b/slugify/slugify.py
@@ def slugify(text):
     text = re.sub(r"[^\w\s-]", "", text).strip().lower()
-    return re.sub(r"[\s]+", "-", text)
+    text = re.sub(r"[\s]+", "-", text)
+    return re.sub(r"-{2,}", "-", text)
Walkthrough
The slug builder collapses whitespace to - but never dedupes repeated hyphens, so multiple separators leak through; adding re.sub(r'-{2,}', '-', text) normalizes them to a single -. SWE-rebench grades execution-based: it applies the patch in the repo container and marks the instance resolved only if the FAIL_TO_PASS test now passes and every PASS_TO_PASS test still passes (leaderboard = % resolved).
0 15 30 45 60 2026-02-01 2026-04-19 2026-07-05 MiniMax M2.5 · 39.6 · 2026-02-01 Claude Opus 4.6 · 51.7 · 2026-02-01 Claude Opus 4.8 xhigh · 56.5 · 2026-07-05 GLM-5.2 · 51.1 · 2026-07-05 Gemini 3.5 Flash · 49.5 · 2026-07-05 MiniMax M3 · 45.6 · 2026-07-05 DeepSeek-V4 Pro · 42.7 · 2026-07-05 MiMo V2.5 Pro · 42.4 · 2026-07-05 DeepSeek-V4 Flash · 38.4 · 2026-07-05 Qwen3.6-27B · 36.5 · 2026-07-05 Qwen3.6-35B-A3B · 33.8 · 2026-07-05 Gemma 4 31B · 16.5 · 2026-07-05
MiniMax M2.5 Claude Opus 4.6 Claude Opus 4.8 xhigh GLM-5.2 Gemini 3.5 Flash MiniMax M3 DeepSeek-V4 Pro MiMo V2.5 Pro DeepSeek-V4 Flash Qwen3.6-27B Qwen3.6-35B-A3B Gemma 4 31B
Timeline
Date Model Score Source
2026-07-05 Claude Opus 4.8 xhigh 56.5% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 GLM-5.2 51.1% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 Gemini 3.5 Flash 49.5% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 MiniMax M3 45.6% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 DeepSeek-V4 Pro 42.7% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 MiMo V2.5 Pro 42.4% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 DeepSeek-V4 Flash 38.4% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 Qwen3.6-27B 36.5% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 Qwen3.6-35B-A3B 33.8% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-07-05 Gemma 4 31B 16.5% SWE-rebench leaderboard adds GLM-5.2, Qwen3.6, Gemma 4 and improves UI
2026-02-01 MiniMax M2.5 39.6% Decontaminated benchmarks reveal 12-point gap between top AI coding models
2026-02-01 Claude Opus 4.6 51.7% Decontaminated benchmarks reveal 12-point gap between top AI coding models