--- title: Terminal-Bench 4.0 reveals how badly frontier AI agents... description: Terminal-Bench 4.0 tested 17 frontier AI models on 46 long, multi-step terminal tasks and found an average pass rate of just 6.4%, with 10 of 17 models solving... canonical: https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Terminal-Bench 4.0 reveals how badly frontier AI agents fail at long multi-step tasks | daily.dev og:description: Terminal-Bench 4.0 tested 17 frontier AI models on 46 long, multi-step terminal tasks and found an average pass rate of just 6.4%, with 10 of 17 models solving... og:url: https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx og:image: https://api.daily.dev/og/posts/aZkZktpsX.png og:image:alt: Terminal-Bench 4.0 reveals how badly frontier AI agents fail at long multi-step tasks 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. # Terminal-Bench 4.0 reveals how badly frontier AI agents fail at long multi-step tasks **[Collections](https://daily.dev/sources/collections)** · 1 min read · 1 upvotes · 0 comments ## Summary Terminal-Bench 4.0 tested 17 frontier AI models on 46 long, multi-step terminal tasks and found an average pass rate of just 6.4%, with 10 of 17 models solving zero tasks under strict grading. The best model reached only 28.3% completion. Most failures (79%) happened because agents ran out of time mid-task rather than making obviously wrong moves — individual steps looked reasonable, but agents couldn't sustain coherent progress across hundreds of steps toward a distant goal. The results highlight a long-horizon coherence gap that shorter benchmarks don't expose. ## Content A new benchmark called Long-Horizon-Terminal-Bench tested 17 frontier models across 46 long terminal tasks, and the results are pretty damning: the average pass rate is 6.4%. Under strict full-completion grading, 10 of the 17 models solve exactly zero tasks. Of the runs that fail, 79% end with the agent simply running out of time while still working. The best model in the group hits 28.3% — meaning it still leaves 7 out of every 10 tasks unfinished. The pattern that keeps showing up: models can execute locally reasonable steps. They're not making obvious errors early on. They just can't string hundreds of those steps together into a finished result. Somewhere across that long chain, things fall apart. This is the long-horizon problem in concrete terms. It's one thing to say agents struggle with complex multi-step tasks — it's another to watch 10 of 17 frontier models score zero under real completion criteria. Terminal-Bench 4.0 is the version that produced these results, and it's a useful reminder that benchmark development is still catching up to where model marketing already is. ## Questions this post answers ### What pass rate did frontier AI models achieve on the Long-Horizon-Terminal-Bench benchmark? The average pass rate across 17 frontier models was 6.4% on 46 long, multi-step terminal tasks under strict full-completion grading. Ten of the 17 models solved zero tasks entirely, and the best-performing model reached only 28.3%, leaving roughly 7 out of 10 tasks unfinished. _Anyone weighing which agent to trust for long automation runs can track benchmark results like this via daily.dev._ ### Why do AI agents fail at long multi-step terminal tasks even when individual steps look correct? Terminal-Bench 4.0 results show agents typically fail not from early mistakes but from running out of time while still working — accounting for 79% of failed runs. Models execute locally reasonable steps but cannot chain hundreds of them together into a finished, completed result over long task horizons. _Developers debugging long-running agent workflows can follow findings like this on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 2 discussions and 10 comments across x (as of 2026-09-13). **TL;DR:** Replies largely accept the benchmark's core finding that agents lose coherence over long horizons, though several push back on whether 'running out of time' really means failure or just a benchmark/harness limitation. **Sentiment:** 5% positive · 35% mixed · 60% skeptical **The case for** - Some see the coherence gap as a scale problem likely to improve quickly, drawing an analogy to earlier models struggling with short-paragraph coherence. **The pushback** - Several argue agents lose the big-picture goal and can't correct sub-tasks accordingly. - Some contend the high 'ran out of time' rate suggests agents were still progressing rather than stuck, questioning whether the benchmark is really testing speed rather than capability. - One commenter blames inadequate agent harnesses rather than model capability for the failures. - Context drift after many turns is cited as breaking current agent setups. - One reply frames the failures as agents abandoning tasks rather than asking for help when stuck. **By community** - x (mixed): Commenters mostly agree long-horizon coherence is the real bottleneck, but disagree on whether that's a fundamental limit, a harness problem, or just a matter of scale and time. **Hottest debate:** Whether the 79% 'ran out of time' failures indicate a genuine coherence limit or just mean the benchmark is measuring speed/harness quality rather than true capability. **Open questions** - Is the coherence gap fixable with more scale/time, or does it require a different architecture or harness? - Would agents perform better if allowed to ask for help instead of silently abandoning tasks? **Highlights** > @rohanpaul_ai 79% running out the clock means they were still making progress not stuck. the benchmark is measuring speed as much as capability > — [Chahatusharma on x](https://x.com/Chahatusharma/status/2093500966603506089) > @rohanpaul_ai the clock running out at 79% failure is the killer detail. agents don't get stuck in a loop, they just run out of patience. they abandon the field instead of asking for help > — [isofunds on x](https://x.com/isofunds/status/2093511167687659934) > @rohanpaul_ai so what's the reason that they fail? is it because they can't see the big picture? and therefore, they can't correct the sub-tasks for the big picture goal? that would be my guess, but I have no data. > — [nickkirt on x](https://x.com/nickkirt/status/2093497310646808883) > @rohanpaul_ai They don't have the proper harness > — [SergioCastroR on x](https://x.com/SergioCastroR/status/2093503752267354370) > @rohanpaul_ai Context drift after a hundred turns breaks every current agent setup. > — [gurtej\_\_gill\_ on x](https://x.com/gurtej__gill_/status/2093655163009646724) **Source threads** - [x](https://x.com/yacineMTB/status/2093497976148611286) · 0 points · 0 comments - [x](https://x.com/rohanpaul_ai/status/2093495711618969774) · 0 points · 10 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents) [View this post on daily.dev](https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx) ```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":"Terminal-Bench 4.0 reveals how badly frontier AI agents fail at long multi-step tasks","url":"https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx"},"datePublished":"2026-08-29T00:36:53.547Z","dateModified":"2026-09-13T20:04:00.359Z","description":"Terminal-Bench 4.0 tested 17 frontier AI models on 46 long, multi-step terminal tasks and found an average pass rate of just 6.4%, with 10 of 17 models solving...","image":"https://pbs.twimg.com/media/HQ2SiIYXwAEQZGM.jpg","thumbnailUrl":"https://pbs.twimg.com/media/HQ2SiIYXwAEQZGM.jpg","isAccessibleForFree":true,"articleSection":"Collections","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":"Collections","logo":"https://media.daily.dev/image/upload/s--fk_6ycEi--/f_auto,q_auto/v1780996001/logos/collections?_a=BAMAMiWQ0","url":"https://daily.dev/sources/collections"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,ai-agents","timeRequired":"PT1M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Collections","item":"https://daily.dev/sources/collections"},{"@type":"ListItem","position":3,"name":"Terminal-Bench 4.0 reveals how badly frontier AI agents fail at long multi-step tasks"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/terminal-bench-4-0-reveals-how-badly-frontier-ai-agents-fail-at-long-multi-step-tasks-azkzktpsx#faq","mainEntity":[{"@type":"Question","name":"What pass rate did frontier AI models achieve on the Long-Horizon-Terminal-Bench benchmark?","acceptedAnswer":{"@type":"Answer","text":"The average pass rate across 17 frontier models was 6.4% on 46 long, multi-step terminal tasks under strict full-completion grading. Ten of the 17 models solved zero tasks entirely, and the best-performing model reached only 28.3%, leaving roughly 7 out of 10 tasks unfinished. Anyone weighing which agent to trust for long automation runs can track benchmark results like this via daily.dev."}},{"@type":"Question","name":"Why do AI agents fail at long multi-step terminal tasks even when individual steps look correct?","acceptedAnswer":{"@type":"Answer","text":"Terminal-Bench 4.0 results show agents typically fail not from early mistakes but from running out of time while still working — accounting for 79% of failed runs. Models execute locally reasonable steps but cannot chain hundreds of them together into a finished, completed result over long task horizons. Developers debugging long-running agent workflows can follow findings like this on daily.dev."}}]} ```