The true cost of saying "Hi" to an AI agent
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Token prices are nearly negligible when using AI agents — the real cost is developer waiting time. A benchmark of 14 LLMs across 210 agentic trials using three prompts ('Hi', 'commit', 'WTF') reveals that latency, not API spend, dominates the total cost. Ambiguous prompts cause models to spiral into dozens of unnecessary tool calls: Claude Sonnet averaged 24 tool calls and 49 seconds just to respond to 'Hi', while some models failed entirely. A clear, specific prompt like 'commit' resolved cleanly every time across all models. At a $120K/year developer salary, each second of waiting costs $0.016 — making waiting 20x more expensive than tokens on the cheapest model. The takeaway: prompt clarity is the most impactful cost optimization available today, and model speed matters far more than token pricing for agentic workflows.
Table of contents
What the tokens costWhat the agent actually does with “Hi”Now price the waitingMethodologySo…Questions this post answers
why do some AI coding agents make dozens of tool calls just to respond to a simple greeting like "Hi"
Ambiguous prompts with no clear task cause some agents to over-explore rather than answer directly. In one benchmark, Claude Sonnet averaged 24 tool calls and 49 seconds responding to "Hi" in a git repo, including reading every file, running the app, searching the filesystem, and even making an unsolicited commit, while GPT-5.5 and Grok used only 2 tool calls. daily.dev surfaces practical findings like this for developers deciding how to prompt and budget for ai agents.
is token cost or waiting time the bigger expense when using AI coding agents
Waiting time is the bigger expense. In a benchmark of 14 models, token costs for responding to a greeting ranged from about $0.0025 to $0.07, but factoring in developer salary at $120,000 per year ($0.016 per second waited), total costs ranged from $0.08 to $1.39 depending on the prompt and model, with waiting sometimes accounting for 99% of the bill. track cost tradeoffs like this on daily.dev when choosing which ai agent fits your workflow.
which AI models fail or time out most often on ambiguous prompts in agentic coding tasks
Claude Haiku and MiniMax each failed 3 of 5 runs on the ambiguous "Hi" prompt, while responding to "WTF" caused DeepSeek to fail 4 of 5 runs, Gemini 3.1 Pro to loop until timeout on every run, and Fable to issue zero commands on every run. All 14 models passed the unambiguous "commit" prompt 5 out of 5 times. developers weighing agent reliability can follow benchmarks like this on daily.dev before picking a model.
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