--- title: From Leaderboards to Model Profiles: A Deep Dive... description: JetBrains presents an evaluation pipeline for agentic coding LLMs that goes beyond binary resolve rate by analyzing execution trajectories, patch quality, and... canonical: https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding | daily.dev og:description: JetBrains presents an evaluation pipeline for agentic coding LLMs that goes beyond binary resolve rate by analyzing execution trajectories, patch quality, and... og:url: https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh og:image: https://api.daily.dev/og/posts/uohvuwpJh.png og:image:alt: From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding 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. # From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding **[JetBrains](https://daily.dev/sources/jetbrains)** · 12 min read · 0 upvotes · 0 comments ## Summary JetBrains presents an evaluation pipeline for agentic coding LLMs that goes beyond binary resolve rate by analyzing execution trajectories, patch quality, and process across four dimensions: outcome, efficiency, patch quality, and process quality. Testing Claude Opus 4.7 and Gemini 3.5 Flash on 523 tasks in Junie showed near-identical resolve rates (51.1% vs 48.6%) but very different behavioral profiles: Opus reached correct diagnoses more often but skipped executable validation in 123 runs, while Gemini validated more consistently but showed higher hallucination (37.3% vs 24.7%) and redundancy rates. A broader comparison including GPT-5.5 and Qwen 3.6 27B FP8 found GPT-5.5 topped resolve rate while always running executable checks, Opus led patch quality metrics, and Qwen delivered 38.9% resolve rate at a fraction of the cost. The authors argue leaderboard resolve rates alone can mask meaningfully different model behaviors relevant to model selection. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why do Claude Opus and Gemini get similar coding benchmark scores but behave so differently? Resolve rate alone hides differences in execution behavior. In a JetBrains Junie evaluation across 523 tasks, Claude Opus 4.7 and Gemini 3.5 Flash scored 51.1% and 48.6% respectively, but Opus averaged 184 steps costing $2.79 per run while Gemini averaged 271 steps costing $1.24, with Opus skipping executable validation in 123 runs and Gemini showing hallucination in 37.3% of runs versus 24.7% for Opus. _Comparing coding models on more than pass rate is easier when discussions and benchmarks are tracked on daily.dev._ ### How often does Claude Opus 4.7 skip running tests before finishing a coding task? Claude Opus 4.7 performed no executable validation in 123 runs during a JetBrains benchmark evaluation, including 68 runs that still resolved the task despite skipping validation. This means the patch's correctness was never confirmed by actually running the code, leaving undetected risk of regressions or edge-case failures even when the task was marked resolved. _Developers weighing model reliability for autonomous coding tasks can follow this kind of analysis on daily.dev._ ### How does Qwen 3.6 27B FP8 compare to GPT-5.5 on coding benchmark cost and accuracy? Qwen 3.6 27B FP8 resolved 38.9% of coding tasks at roughly three percent of GPT-5.5's cost per run, versus GPT-5.5's 51.5% resolve rate, the highest among four models tested (GPT-5.5, Claude Opus 4.7, Gemini 3.5 Flash, and Qwen 3.6 27B FP8). Qwen also scored weakest at identifying root causes, but its low cost per failed run makes it viable when the run itself is the expensive part. _Choosing a cost-effective coding model trade-off gets easier when comparisons like this surface on daily.dev._ ## Similar posts on daily.dev - [How to choose your LLM without ruining your Java code](https://daily.dev/posts/how-to-choose-your-llm-without-ruining-your-java-code-e02dc9ojz) · Foojay.io · 3 upvotes · 0 comments - [GPT-5.4 vs Claude Opus 4.6: a guide to choosing the right model](https://daily.dev/posts/gpt-5-4-vs-claude-opus-4-6-a-guide-to-choosing-the-right-model-bhtobehwf) · portkey · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#claude](https://daily.dev/tags/claude), [#google-gemini](https://daily.dev/tags/google-gemini) [View this post on daily.dev](https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh) ```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":"From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding","url":"https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh"},"datePublished":"2026-08-31T11:21:00.964Z","dateModified":"2026-08-31T11:21:28.173Z","description":"JetBrains presents an evaluation pipeline for agentic coding LLMs that goes beyond binary resolve rate by analyzing execution trajectories, patch quality, and...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/67e3065d0da3e202f9f960dee4247836?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/67e3065d0da3e202f9f960dee4247836?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"JetBrains","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"JetBrains","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa","url":"https://daily.dev/sources/jetbrains"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,claude,google-gemini","timeRequired":"PT12M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"JetBrains","item":"https://daily.dev/sources/jetbrains"},{"@type":"ListItem","position":3,"name":"From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for-agentic-coding-uohvuwpjh#faq","mainEntity":[{"@type":"Question","name":"Why do Claude Opus and Gemini get similar coding benchmark scores but behave so differently?","acceptedAnswer":{"@type":"Answer","text":"Resolve rate alone hides differences in execution behavior. In a JetBrains Junie evaluation across 523 tasks, Claude Opus 4.7 and Gemini 3.5 Flash scored 51.1% and 48.6% respectively, but Opus averaged 184 steps costing $2.79 per run while Gemini averaged 271 steps costing $1.24, with Opus skipping executable validation in 123 runs and Gemini showing hallucination in 37.3% of runs versus 24.7% for Opus. Comparing coding models on more than pass rate is easier when discussions and benchmarks are tracked on daily.dev."}},{"@type":"Question","name":"How often does Claude Opus 4.7 skip running tests before finishing a coding task?","acceptedAnswer":{"@type":"Answer","text":"Claude Opus 4.7 performed no executable validation in 123 runs during a JetBrains benchmark evaluation, including 68 runs that still resolved the task despite skipping validation. This means the patch's correctness was never confirmed by actually running the code, leaving undetected risk of regressions or edge-case failures even when the task was marked resolved. 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