Agentic capability is the single biggest factor in BenchLM.ai's overall ranking at 22% weight. It measures what matters most for production AI systems: whether a model can complete multi-step workflows — not just answer questions. Terminal-Bench 2.0 tests coding and shell tasks, BrowseComp measures web research and evidence gathering, and OSWorld-Verified tests computer-use reliability across real software interfaces. Models that lead here can browse, plan, use tools, and recover from mistakes without hand-holding. This is the most predictive category for real-world AI agent performance.
According to BenchLM.ai, GPT-5.3 Codex leads this ranking with a score of 88.1, followed by GPT-5.4 (87.8) and GPT-5.4 Pro (87.4). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is GLM-5 (Reasoning) (ranked #12 with a score of 78.3). Proprietary models hold a clear advantage in this category, though open-weight options may suffice for less demanding use cases.
This ranking is based on average scores across all agentic benchmarks tracked by BenchLM.ai. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
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