--- title: SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall,... description: New SlopCodeBench results show gpt-6 Astra topping frontier coding agents with a 16.3% strict pass rate, up from gpt-5.5's 14.8% in May. The test ran 36... canonical: https://daily.dev/posts/slopcodebench-10-2026-opus-5-5-astra-wins-overall-xhigh-considered-harmful-mjhhblw2w twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful | daily.dev og:description: New SlopCodeBench results show gpt-6 Astra topping frontier coding agents with a 16.3% strict pass rate, up from gpt-5.5's 14.8% in May. The test ran 36... og:url: https://daily.dev/posts/slopcodebench-10-2026-opus-5-5-astra-wins-overall-xhigh-considered-harmful-mjhhblw2w og:image: https://api.daily.dev/og/posts/mjhhBlW2w.png og:image:alt: SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful **[Hacker News](https://daily.dev/sources/hn)** · 9 min read · 1 upvotes · 0 comments ## Summary New SlopCodeBench results show gpt-6 Astra topping frontier coding agents with a 16.3% strict pass rate, up from gpt-5.5's 14.8% in May. The test ran 36 problems and 196 checkpoints across Opus 5.5, Fable 5.1, SOL 6/6.1, Astra, and GLM 5.3, all scoring 12.2-16.3% strict. Higher 'XHigh' reasoning effort cost 2.11-4.22x more than Medium and was not a reliable way to get more strict passes, notably for Opus 5.5. Opus 5.5 Medium left the most leftover 'slop' code at 94.25% flagged lines, while Opus XHigh was cleanest at 77.75% despite fewer strict passes. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What strict pass rate did gpt-6 astra get on SlopCodeBench compared to gpt-5.5? Gpt-6 astra scored a 16.3% strict pass rate on SlopCodeBench, up from gpt-5.5's 14.8% recorded in May. Strict pass rate means a model passed every test case in a checkpoint including all regression tests. The October run covered 36 problems and 196 checkpoints, with frontier models including Opus 5.5, Fable 5.1, SOL 6/6.1, and GLM 5.3 scoring between 12.2% and 16.3% strict. _Developers tracking which coding agent to adopt can follow benchmark updates like this on daily.dev._ ### Does using higher reasoning effort (xhigh) improve coding agent benchmark scores? Not reliably. On SlopCodeBench, xhigh effort cost 2.11 to 4.22 times more than medium effort across three model pairs, but strict pass rates only improved for gpt-6 astra (29 to 32). Opus 5.5 actually scored worse on xhigh (25) than medium (28), despite xhigh producing cleaner code with a lower slop percentage of 77.75% versus 94.25% for medium. _Anyone tuning reasoning-effort settings for coding agents can weigh these cost and accuracy tradeoffs via daily.dev._ ### How much leftover junk or 'slop' code do AI coding agents leave in a codebase? A large share, measured as the percentage of source lines flagged by over 200 slop-detection linters for anti-patterns and repeated code. Opus 5.5 Medium had the highest slop presence at 94.25%, while Opus 5.5 XHigh had the lowest at 77.75%. Opus Medium also left 216,881 source lines in one run, including 45 scratch files totaling 41,265 lines. _Teams evaluating code cleanliness from AI agents can track findings like this on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion and 75 comments across hackernews (as of 2026-10-21). **TL;DR:** Discussion centers less on the benchmark's headline numbers and more on tangential debates about linking to X, and substantive threads about why agentic coding output tends to be 'sloppy' and whether higher reasoning effort (XHigh) is worth the cost; many report medium effort often outperforms or matches higher tiers. **Sentiment:** 15% positive · 55% mixed · 30% skeptical **The case for** - Several people confirm the benchmark's finding that medium effort often produces equal or better results than high/xhigh, matching their own experience. - Some find value in multi-agent/orchestrator workflows using different models for planning vs coding, reporting measurable quality improvements. - The benchmark is praised by some as a rare serious, reproducible evaluation with public methodology and repo. **The pushback** - Commenters argue the 'slop' problem stems from LLMs being overly defensive/verbose, adding code rather than simplifying or removing it. - Several say agents bias toward bolting on workarounds instead of refactoring existing code cleanly. - Skepticism that multi-agent 'team' orchestration setups are just elaborate LARPing with no proven benefit over a single frontier model working directly. - One commenter criticizes the benchmark's precision (five significant digits on only 36 tests) as statistically unjustified. - Astra's output is criticized for being hard to read, e.g., omitting line breaks, despite passing tests. **By community** - hackernews (mixed): A long tangent debates how to ethically link to X content, while the substantive threads are split between genuine interest in the benchmark methodology, skepticism about its statistical rigor, and practical disagreement over whether higher reasoning effort or multi-agent orchestration actually helps. **Hottest debate:** Whether complex multi-agent/orchestrator setups with different models playing different 'roles' provide real benefit over just using one frontier model directly, versus being an unproductive LARP. **Open questions** - Is there a way to run a representative subset of the benchmark without reproducing the full 36-problem suite? - Why does higher reasoning effort (XHigh) sometimes reduce strict pass rates instead of improving them? - Are the raw model outputs from the benchmark published for further analysis? **Highlights** > IMO slop usually comes from overly defensive programming from LLMs. They’re loath to remove code and simplify, because unless we humans tell them some code / use case is unimportant, they have no reason to not care about it. So agents can make these overly complex solutions trying to make bulletproof implementations and prevent regressions. It requires a human to look at code, ask about complexity, and guide simplification by subtracting requirements implicit in the code. > — [softwaredoug on hackernews · 1 comments](https://news.ycombinator.com/item?id=50173236) > No "may be" about it. There is no benefit to having bad models write your code, and there's no benefit to "plan" mode. Plan mode is an outdated 2025 concept that should be removed from standard harnesses. There are 2 scenarios for dumb builder agents: 1. Your "planner" actually implements the project almost completely, finds all the gotchas, solves all of the emergent problems, and fully plans the solution. 2. Your "planner" takes a half-assed first attempt, solving none of the major problems that will arise during implementation In scenario 1 -- just pay cache output on a few more tokens to finish the project. No need to spend twenty minutes pumping a few million tokens through deepseek regardless of how cheap it is to do so. In scenario 2 -- good luck getting quality output I do think this "software team" LARPing is an engineering version of AI psychosis. You can get all of the little parts humming along, sending messages, and looking really cool. You can even convince yourself that all of these busy little bees actually make better product. But a frontier model working alone can produce better output in a tiny fraction of the time. Just build the thing. > — [criley2 on hackernews](https://news.ycombinator.com/item?id=50174800) > Wouldn't it be better to enforce pass via the harness and then measure cost/quality since that's more what people are likely to do? > — [foota on hackernews](https://news.ycombinator.com/item?id=50172517) > There are 36 tests. You can pass 0 or 36 or any number inbetween. Lets give five significant digits of how accurate solving was. That is not how data physics work. It should not be how data science works. > — [a3w on hackernews](https://news.ycombinator.com/item?id=50172783) > Astra writes well (which is why I often like to work with it over Claude), but it tends to produce code golf that's borderline unreadable, like completely omitting line breaks. I thought I had something funky with my config, but it seems to be a known issue. https://lucumr.pocoo.org/2026/9/7/astra-why/ [Sorry for X] https://x.com/tenobrus/status/2096636223414808719 The generated code certainly does what's asked of it, but it's as sloppy as anything else I've seen. I don't think this benchmark can control for that. Maybe if it had a metric for minimum diff to satisfy the new requirement with the existing constraints on dead code etc? > — [throwaway219450 on hackernews · 4 comments](https://news.ycombinator.com/item?id=50171776) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=50108717) · 157 points · 75 comments ## Similar posts on daily.dev - [advanced-context-engineering-for-coding-agents/benchmarking-opus-5-on-slop-code-bench.md at main · humanlayer/advanced-context-engineering-for-coding-agents](https://daily.dev/posts/advanced-context-engineering-for-coding-agents-benchmarking-opus-5-on-slop-code-bench-md-at-main-h-l5bkkqkom) · Hacker News · 2 upvotes · 0 comments - [What 9,050 AI-Written Scripts Taught Me About Slop](https://daily.dev/posts/what-9-050-ai-written-scripts-taught-me-about-slop-il5in6rbj) · DEV · 0 upvotes · 0 comments - [The Agentic Analytics Benchmark, round two: GPT-6, Claude Opus 5.5, Claude Sonnet 5.5](https://daily.dev/posts/the-agentic-analytics-benchmark-round-two-gpt-6-claude-opus-5-5-claude-sonnet-5-5-l8whcfkkx) · ClickHouse · 1 upvotes · 0 comments - [AI’s best coding agent fails 60% of the time — and the data backs it up](https://daily.dev/posts/ai-s-best-coding-agent-fails-60-of-the-time-and-the-data-backs-it-up-ziebak7ti) · The New Stack · 2 upvotes · 0 comments --- Tags: [#ai](https://daily.dev/tags/ai), [#ai-agents](https://daily.dev/tags/ai-agents), [#claude](https://daily.dev/tags/claude), [#gpt](https://daily.dev/tags/gpt) [View this post on daily.dev](https://daily.dev/posts/slopcodebench-10-2026-opus-5-5-astra-wins-overall-xhigh-considered-harmful-mjhhblw2w) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful","url":"https://daily.dev/posts/slopcodebench-10-2026-opus-5-5-astra-wins-overall-xhigh-considered-harmful-mjhhblw2w","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/slopcodebench-10-2026-opus-5-5-astra-wins-overall-xhigh-considered-harmful-mjhhblw2w"},"datePublished":"2026-10-20T21:00:23.094Z","dateModified":"2026-10-21T01:21:24.307Z","description":"New SlopCodeBench results show gpt-6 Astra topping frontier coding agents with a 16.3% strict pass rate, up from gpt-5.5's 14.8% in May. 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Strict pass rate means a model passed every test case in a checkpoint including all regression tests. The October run covered 36 problems and 196 checkpoints, with frontier models including Opus 5.5, Fable 5.1, SOL 6/6.1, and GLM 5.3 scoring between 12.2% and 16.3% strict. Developers tracking which coding agent to adopt can follow benchmark updates like this on daily.dev."}},{"@type":"Question","name":"Does using higher reasoning effort (xhigh) improve coding agent benchmark scores?","acceptedAnswer":{"@type":"Answer","text":"Not reliably. On SlopCodeBench, xhigh effort cost 2.11 to 4.22 times more than medium effort across three model pairs, but strict pass rates only improved for gpt-6 astra (29 to 32). Opus 5.5 actually scored worse on xhigh (25) than medium (28), despite xhigh producing cleaner code with a lower slop percentage of 77.75% versus 94.25% for medium. Anyone tuning reasoning-effort settings for coding agents can weigh these cost and accuracy tradeoffs via daily.dev."}},{"@type":"Question","name":"How much leftover junk or 'slop' code do AI coding agents leave in a codebase?","acceptedAnswer":{"@type":"Answer","text":"A large share, measured as the percentage of source lines flagged by over 200 slop-detection linters for anti-patterns and repeated code. Opus 5.5 Medium had the highest slop presence at 94.25%, while Opus 5.5 XHigh had the lowest at 77.75%. Opus Medium also left 216,881 source lines in one run, including 45 scratch files totaling 41,265 lines. Teams evaluating code cleanliness from AI agents can track findings like this on daily.dev."}}]} ```