BenchLM recommendation
Best LLMs for AI Agents in 2026
As of July 23, 2026, the top model in best llms for ai agents on the BenchLM leaderboard is GPT-5.6 Sol with a score of 75.2.
Last verified: July 23, 2026
Agentic capability is BenchLM's heaviest-weighted category (22% of the overall score) because it is where models differ most in practice: tool calling, browsing, terminal work, and multi-step task execution. This page ranks models purely by their agentic category score, built from benchmarks like Terminal-Bench 2, BrowseComp, OSWorld-Verified, and tau2-bench.
Unless noted otherwise, ranking surfaces on this page use BenchLM's provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Bottom line: Claude Mythos 5 and Claude Fable 5 lead agentic work, with GPT-5.5 the strongest non-Anthropic row. Agent harness quality matters as much as the model — test with your own scaffold.
GPT-5.6 Sol leads this ranking with a score of 75.2, followed by GPT-5.6 Terra (73.1) and Muse Spark 1.1 (68.6). There is meaningful separation between the top models, suggesting genuine performance differences.
The best open-weight option is GLM-5 (ranked #23 with a score of 54.8). 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 provisional weighted averages across the scoring benchmarks in agentic 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.
What changed
Claude Mythos 5 tops the agentic category with a perfect weighted score.
Claude Fable 5 within a point of the lead — the strongest generally available agentic model.
GPT-5.5 the best non-Anthropic agentic row at 96.1.
How to choose
Best agentic model you can use today?
Claude Fable 5 — generally available, near-perfect agentic score
Building coding agents?
See the coding leaderboard — agentic + coding overlap heavily
Budget agent loops?
Route simple steps to cheaper models — see the cost calculator
Full agentic benchmark detail?
The agentic category page has every benchmark
Full Rankings (119 models)
Key Takeaways
The top model is GPT-5.6 Sol by OpenAI with a BenchAlign v5 score of 75.2 and Supported evidence.
The best open-weight model is GLM-5 at position #23.
119 models are included in this ranking.
Score in Context
What these scores mean
The agentic score blends tool-use, browsing, computer-use, and long-horizon execution benchmarks. It carries 22% of the overall BenchLM score — the largest single category weight.
Known limitations
Agentic benchmarks bundle the model with a harness (scaffold, tools, retries). Your agent stack can move results by more than the gap between adjacent models here.
Best LLMs for AI Agents FAQ
What is the best LLM for AI agents?
Claude Mythos 5 and Claude Fable 5 currently top BenchLM's agentic category, with GPT-5.5 the strongest non-Anthropic option. The leaderboard above recomputes on every data refresh. For agent work specifically, weight Terminal-Bench 2, BrowseComp, and tau2-bench scores over general benchmarks.
What is the best AI agent?
This page ranks the models that power agents rather than packaged agent products. An agent product is a model plus a harness — tools, memory, retries — so the same model can perform very differently across products. Start from the strongest agentic model, then evaluate harnesses on your own tasks.
Do agentic benchmarks predict real-world agent performance?
Directionally yes, precisely no. Benchmarks like OSWorld-Verified and tau2-bench correlate with practical reliability, but they test specific environments with specific scaffolds. A 10-point gap usually matters; a 2-point gap can vanish under your own harness.
What is the cheapest good model for agents?
Agent loops burn tokens fast, so effective cost matters more than list price. Check the price-vs-performance view and the provider pricing hubs for cached-input rates — caching discounts of up to 90% dominate agent economics because loops resend the same context every step.
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