Best LLM for tool-using agents
Autonomous agents calling tools / functions across multiple turns.
Why this ranking is opinionated
Agent loops chain many tool calls; reliability of function calling is the main differentiator. Long context matters because tool outputs accumulate. Cheap models often fail silently mid-loop — pay for reliability.
Top 5 recommendations
ranked by monthly cost at this workload- · Cheapest qualifying option at this workload (~$0.00/mo).
- · 262,144 tokens of context — far above this use case's 64,000-token minimum.
- · Missing preferred: json_mode — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · Missing preferred: json_mode — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 1,048,576 tokens of context — far above this use case's 64,000-token minimum.
- · Missing preferred: json_mode — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 262,144 tokens of context — far above this use case's 64,000-token minimum.
- · Supports preferred capabilities: json_mode.
- · ~$0.00/mo (+0% over the cheapest option).
- · 262,144 tokens of context — far above this use case's 64,000-token minimum.
- · Supports preferred capabilities: json_mode.
Frequently asked questions
What makes a good LLM for tool-using agents?
Agent loops chain many tool calls; reliability of function calling is the main differentiator. Long context matters because tool outputs accumulate. Cheap models often fail silently mid-loop — pay for reliability.
What capabilities matter most for tool-using agents?
For tool-using agents the typical filters are: tools, and a context window of at least 64k tokens. The ranking on this page weights monthly cost (at the workload defaults shown above) most heavily, then capability fit.
What is currently the cheapest LLM for tool-using agents?
At the typical workload defaults, Trinity Large Thinking (free) from Arcee AI ranks cheapest right now (~$0 / month). Plug your own monthly token volumes into the calculator on this page for a workload-specific number.
Is the cheapest LLM always the right choice for tool-using agents?
Not always. Cheap models often trade off reasoning quality, tool reliability, or context size. Use the cheapest as a baseline and benchmark against a tier-up model on your own evaluation set before committing to a contract — quality differences compound over millions of tokens.