If you’ve been tracking coding-agent leaderboards and wondering why the headline numbers (75%+ on SWE-bench Verified) don’t seem to match the actual experience of asking an agent to do a large refactor, a new benchmark published this week has an answer: the easy benchmarks are saturated, and the hard, realistic ones aren’t close.

The Problem With the Old Benchmarks

SWE-bench Verified — the standard for measuring AI coding agents on real GitHub issues — has become a victim of its own success. A recent audit cited in the new paper found that nearly 60% of unsolved SWE-bench Verified instances contain flawed tests: either overly narrow tests that reject perfectly correct solutions, or overly broad tests that check unstated requirements. On top of that, frontier models have been shown to reproduce gold patches from training data verbatim on some instances — meaning high scores partly reflect memorization, not capability.

Meanwhile, most of SWE-bench’s tasks are narrow by design: fix a bug in a file or two. Real-world software engineering work — especially refactoring, where you’re making coordinated, behavior-preserving changes across a whole codebase — looks nothing like that.

Enter SWE-Bench ProMax

A team of researchers (the paper is targeting COLM 2026 and is affiliated with ByteDance) built SWE-Bench ProMax, a new benchmark of 170 expert-curated instances drawn from real commits across seven programming languages: Python, Java, TypeScript, Go, C, C++, and Rust.

The construction pipeline is notably rigorous. Starting from an initial pool of 29,782 candidate commits pulled from repositories with at least 500 GitHub stars, an approved open-source license, and a keyword match for “refactor” (while excluding “bug fix”), the team filtered down through three stages: automated environment construction and validation, then human-expert curation that rewrote every issue description from scratch and manually reviewed every test suite to strip out overly narrow or overly broad tests. Only 170 of the original ~30,000 candidates survived — about a 0.6% yield.

The result is a benchmark of genuinely large-scale tasks: gold patches average 11.4 modified files and 261.6 lines of code, with the most complex instances touching up to 182 files. That dwarfs prior refactoring-specific benchmarks — RefactorBench, for comparison, averages just 4.3 modified files and is limited to Python.

The Results: A Real Ceiling

The paper evaluated six frontier models — Gemini 3 Pro, Claude Sonnet 4.6, and GPT-5.2 among proprietary models, plus GLM-5, Kimi-K2.5, and Qwen3.5 among open-weight models — under two different agent scaffolds: mini-SWE-Agent (a minimal, widely-used reimplementation of the SWE-agent pattern) and OpenHands (a fuller agent platform with sandboxed execution and structured file-editing tools).

The headline number: the best-performing configuration — GPT-5.2 running under the OpenHands scaffold — achieved a 41.2% overall resolve rate. That’s the ceiling across all six models and both scaffolds tested. For context, that’s roughly half of what frontier models routinely score on the now-saturated SWE-bench Verified.

A few patterns stand out in the full results table:

  • Scaffold matters as much as model choice. Several models — including GPT-5.2 and Claude Sonnet 4.6 — scored dramatically higher under OpenHands than under mini-SWE-Agent. Claude Sonnet 4.6 went from 30.6% to 38.8% just by switching scaffolds; GPT-5.2 jumped from 21.8% to 41.2%.
  • Open-weight models are competitive but not leading. GLM-5 (36.5% under OpenHands) and Qwen3.5 (36.5%) trailed the top proprietary scores but weren’t far behind Claude Sonnet 4.6.
  • Performance varies heavily by language. Every model did noticeably better on C refactoring tasks than on Go or TypeScript, suggesting current agents still have uneven cross-language refactoring competence rather than a uniform skill level.
  • Cost scales with capability, but not cleanly. Claude Sonnet 4.6 under OpenHands was the most expensive configuration tested (~$4.77 per instance on average), while GLM-5 achieved a comparable resolve rate for a small fraction of the cost.

Why This Matters for Anyone Evaluating Coding Agents

The practical takeaway is simple: if a coding agent’s marketing leans on SWE-bench Verified scores, treat that number as a measure of single-file bug-fixing competence, not a proxy for how the agent will perform on the multi-file refactors that make up a large share of real engineering work. SWE-Bench ProMax is explicitly designed to be unsaturated — it’s meant to stay a meaningful, discriminating test for a while, rather than becoming another benchmark that frontier models game within a year.

It’s also a useful reminder that agent scaffolding — the harness, tools, and execution environment wrapped around a model — can move resolve rates by ten or twenty percentage points on its own. Model choice and scaffold choice are not separable variables when you’re evaluating what an agent can actually do on your codebase.

Sources

  1. SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring — Hugging Face paper listing / arXiv, August 2026
  2. SWE-bench Verified quality audit reference — cited within the SWE-Bench ProMax paper

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260811-2000

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