--- 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 is the average pass rate for AI agents on Terminal-Bench 4.0? The average pass rate across 17 frontier models tested on 46 long terminal tasks is 6.4%. Under strict full-completion grading, 10 of the 17 models solve exactly zero tasks, and the best-performing model reaches only 28.3%, leaving roughly 7 out of 10 tasks unfinished. _Anyone weighing which AI agent to trust for complex automation can track benchmark results like this on daily.dev._ ### Why do AI agents fail at long multi-step terminal tasks? Most failures happen not from bad individual decisions but from running out of time mid-task — 79% of failed runs end because the clock runs out while the agent is still working. Individual steps tend to look locally reasonable, but agents struggle to convert hundreds of those steps into a coherent, finished result over a long horizon. _Developers building agentic workflows can follow long-horizon reasoning research and benchmarks on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 2 discussions and 10 comments across x (as of 2026-08-29). **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":"2026-08-29T13:28:02.380Z","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 is the average pass rate for AI agents on Terminal-Bench 4.0?","acceptedAnswer":{"@type":"Answer","text":"The average pass rate across 17 frontier models tested on 46 long terminal tasks is 6.4%. 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