AI Agent Cost Calculator
Estimate the true daily and monthly cost of running AI agents. Unlike standard calculators, this accounts for the agentic loop token snowball — the compounding context window that makes agents 5–20x more expensive than single API calls.
AI agents don't make one API call per task — they operate in multi-step loops (Think → Act → Observe → Repeat). Each step re-reads the entire conversation history plus tool outputs. A single 100-word prompt can snowball into 6,500+ input tokens over 4 steps. This calculator does that math for you.
Cost across all models
Same workload ( tasks/day, steps each), different models. Sorted cheapest first.
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How AI agents consume tokens
Standard API pricing calculators assume a simple request-response pattern: one prompt in, one completion out. AI agents like OpenClaw, Claude Code, Cursor, and custom ReAct frameworks work fundamentally differently. They operate in multi-step loops where each step builds on the previous context.
When an agent executes a task, it follows a Thought → Action → Observation cycle. At each step, the entire conversation history — including the system prompt, user request, all previous thoughts, actions, and tool outputs — must be re-read by the model. This creates an arithmetic progression where input token consumption grows with every step: I_total = N(S+U) + N(N-1)/2 × (O+R). A 100-word prompt that would cost fractions of a cent as a single API call can snowball into thousands of tokens across a multi-step agent task.
The agentic loop token snowball, explained
Consider a medium-complexity task with 4 steps. Step 1 reads the system prompt (1,000 tokens) plus the user prompt (100 tokens) = 1,100 input tokens. Step 2 must re-read all of that plus the agent's previous output (150 tokens) and any tool results (250 tokens) = 1,500 input tokens. By step 4, the input has grown to 2,050 tokens for that single step alone.
The total input for one 4-step task: 6,550 tokens. That's a 6x multiplier over what a naive "input = user prompt" estimate would predict. For an 8-step complex task with large tool outputs (code files, API responses), this snowball can exceed 22,000 input tokens per task. Multiply that by 50-200 tasks per day and you're looking at real infrastructure costs that belong on a budget spreadsheet.
Choosing the right model for your agent
Not all agent tasks need the most capable model. Simple tasks (file lookups, status checks, single-tool calls) can run on budget models like GPT-5 Mini or Gemini 2.5 Flash at a fraction of the cost. Reserve flagship models like Claude Opus 4.6 or Grok 4 for complex multi-step reasoning tasks where accuracy matters more than cost.
Many production agent systems use a tiered approach: a fast, cheap model for routing and simple tasks, and a more capable model for complex reasoning steps. This can reduce overall costs by 60-80% while maintaining quality where it matters. Use the comparison table above to find the optimal model for your workload profile.
Frequently asked questions
It depends heavily on the model, task complexity, and volume. A light-use personal assistant running 10 simple tasks/day on GPT-5 Mini costs around $0.07/day ($2/month). A heavy-use automated workflow doing 200 complex tasks/day on Claude Opus 4.6 can cost over $200/day. Use the calculator above for an estimate specific to your setup.
Because LLMs are stateless, an agent must resend the entire conversation history at every step. Each step adds its own output and tool results to the context, making subsequent steps progressively more expensive. A 4-step task doesn't cost 4x a single call — it costs roughly 6x due to this compounding effect.
GPT-5 Nano, DeepSeek V3.2, and Llama 4 Scout offer the lowest per-token rates. However, cheaper models may need more steps for complex tasks, partially offsetting savings. GPT-5 Mini and Gemini 2.5 Flash tend to offer the best balance of cost and capability for most agent workloads. Use the comparison table to see exact costs for your usage pattern.
A simple 2-step task uses roughly 2,500 input + 400 output tokens. A medium 4-step task uses ~6,500 input + 750 output tokens. A complex 8-step task with retries can use 22,000+ input + 1,500 output tokens. The key driver is the number of steps — each step re-reads the entire accumulated context.
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