Best LLMs for AI Agents in 2026
As of October 13, 2026, the top model among LLMs for AI agents on the BenchLM leaderboard is Claude Opus 5.5 with a score of 88.8.
The live table sets the model order. Test the model with your own tools, memory, permissions, and retries before choosing an agent setup.
This ranking moves when models ship. Get the releases, price changes and retirements that affect your shortlist. Follow model changes
Full Rankings (123 ranked, 1 tracked)
Public preview through Mistral Studio; Mistral says the weights are due by the end of October 2026.
—
tracked
Current position
Claude Opus 5.5 leads the live llms for ai agents ranking at 88.8 with Supported evidence.
Claude Fable 5.1 ranks #2 at 79.3 with Supported evidence.
Claude Opus 5 ranks #3 at 77.6 with Supported evidence.
How to choose
Key Takeaways
The top model is Claude Opus 5.5 by Anthropic with a BenchAlign v5.8 score of 88.8 and Supported evidence.
The best open-weight model is GLM-5.3 at position #11.
123 models are ranked here; 1 tracked model is listed without a position.
Score in Context
What these scores mean
The agentic category combines sourced model evidence under the public scoring contract. Each row shows its evidence status and conditional score interval; narrow point gaps need that context.
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.
About this ranking
Ranking data as of October 13, 2026
This page ranks model configurations by their agentic category score, using sourced terminal, browsing, computer-use, and tool-calling evidence. It helps choose a model for an agent whose model you can configure. It does not rank complete products such as Grok Bot, Hermes Agent, or OpenClaw.
Unless noted otherwise, ranking surfaces on this page use BenchLM’s provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Claude Opus 5.5 leads this ranking with a score of 88.8, followed by Claude Fable 5.1 (79.3) and Claude Opus 5 (77.6). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is GLM-5.3 (ranked #11 with a score of 67.6). Proprietary models hold a clear advantage in this category, though open-weight options may suffice for less demanding use cases.
This ranking uses provisional weighted averages across the scoring benchmarks in agentic. For detailed model profiles, click any model name above. To compare two specific models head-to-head, use the "vs #" links.
Questions
What is the best LLM for AI agents?
The first row of the agentic table above is the current answer, recomputed on every data refresh. The order can move when new evidence lands, so check the date on the ranking. For agent work, 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?
These scores describe specific tasks, environments, and scaffolds. They help form a model shortlist, but they do not establish that a packaged agent will stay reliable over days of work. Compare candidates on your own tasks with the same tools, permissions, success criteria, and counted interventions.
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.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
Read a sample issueJoin 5,500+ readers.
One email each week. Unsubscribe anytime.