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Agent LLMs don’t just chat - they plan, call tools, and run multi-step tasks on their own. The catch: a high benchmark score can still hide tool hallucination, the failure that quietly derails unattended runs. We compared 15 models on agent-specific benchmarks.

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Best LLMs for Agents

#ModelBest for

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How to Choose

When choosing between these models, consider:
  • Access: First decide whether you want the model in an app, called through an API, or running locally, because those paths change cost, privacy, latency, and setup work. Only Qwen3.6 27B here is a realistic single-machine option; the other open-weight picks need hosted or server-grade infrastructure, and the proprietary models rely on hosted apps or APIs.
  • Quality: We use a combined Agent Arena and Artificial Analysis score as the main number, blending Agent Arena’s Net Improvement signal with Artificial Analysis’s Agentic Index into one normalized figure where higher is better.
  • Price: We use cost per agentic task, drawn from Artificial Analysis where published, for the cleanest cross-model comparison. Some models don’t publish a comparable task cost, so we mark those unavailable.
  • Tool Hallucination: A causal signal from Agent Arena for whether a model avoids calling tools that don’t exist. Positive means fewer hallucinated tool calls than the average model, negative means more. It’s not a raw error rate, so weigh it alongside recovery behavior and your own tests for anything you won’t be watching.

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Other Models We Considered

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Frequently Asked Questions

Claude Fable 5 has the highest ceiling for the hardest, longest autonomous runs. But Opus 4.8 is the better default for most work - nearly as capable, cheaper, and unusually disciplined about tool calls and flagging its own mistakes.
Opus 4.8. It’s the most reliable all-rounder for tool use, computer use, and long tasks. If you run agents at high volume and want to spend less, Sonnet 5 gives you most of that reliability at a lower per-task cost.
GLM-5.2 is the strongest open-weight agent model here and the clearest value against the proprietary frontier. If cost is the priority, DeepSeek V4 Pro delivers similar-tier capability for far less. Both need server-grade infrastructure to self-host.
DeepSeek V4 Flash is effectively free per task and fine for simple, high-volume tool loops. DeepSeek V4 Pro costs a little more and is far more capable, so it’s usually the smarter cheap pick.
Qwen3.6 27B. It’s a dense 27B model that runs on a single high-end GPU or a top-spec Apple-silicon Mac, and it can read images too. Everything more capable here is either proprietary or too large to run outside a server cluster.
They’re close. GPT-5.5 has a slight edge on terminal-style coding and precise tool selection across large tool lists. Opus 4.8 is stronger on computer use, error recovery, and catching its own mistakes, which makes it the safer choice for unsupervised runs.
Roughly, for capability. But a high score doesn’t guarantee reliable tool use - some strong models still invent tool calls, which quietly derails unattended agents. That’s why we track tool hallucination separately; weight it heavily for anything you won’t be watching.
Reliability under autonomy, not just raw score. Decide your access route first, then weigh tool-call discipline and error recovery for unsupervised work, and match cost to your task volume. Peak capability matters least if the model drifts the moment you look away.