AI Agent Token-Cost Estimator: Claude vs GPT vs Gemini (2026)
An agent run is not one model call — it is a loop of reasoning, tool use, and retries, each call carrying growing context. Enter your workload to see the monthly token bill per model, and what the same job costs as a supervised Rills workflow.
03. What it costs
- GPT-5-class (flagship)Cheapest
- Per run
- $0.065
$65.00/mo - Gemini Pro class
- Per run
- $0.065
$65.00/mo - Claude Sonnet 4.x
- Per run
- $0.12
$120.00/mo - Rills workflow
- AI credits
- 2,000 / 5,000 included
- Workflow credits
- 6,000 / 50,000 included
$34.00/mo more than an agent on GPT-5-class (flagship) — buys supervision, free approvals, and $0 pauses.
Credit estimate based on GPT-5-class (flagship) pricing
Within included credits
Triggers, logic, and human approvals never consume credits · $0 while workflows are paused awaiting approval.
$99.00/mo
An agent loop is cheaper at this volume. The workflow buys supervision, free approvals, and $0 pauses — the calculator won't pretend otherwise. Start your trial →
Prices are estimates, last verified 2026-06-05 — confirm against provider pricing pages before budgeting.
Methodology
Monthly calls = agent runs × calls per run. Monthly cost = (input tokens ÷ 1M × input rate) + (output tokens ÷ 1M × output rate); cost per run divides the total by runs. When a model publishes a cached-read rate and you set a cache share, that fraction of input tokens is priced at the cached rate — models without a published rate ignore the slider.
The Rills comparison is deliberately conservative: each AI step is priced at the same input and output tokens as one agent call, using the cheapest model you selected. The savings come only from making fewer model calls — deterministic logic does the routing for free — never from assuming smaller prompts. When the workflow costs more (small workloads where the base subscription dominates), the tool says so plainly. The Rills row is the agent-vs-workflow comparison, not a raw API rate, so it never competes for the cheapest-model badge.
Model prices
Token prices change frequently. These are estimates — always confirm against the provider's current pricing page before budgeting.
| Model | Input $/MTok | Output $/MTok | Cached input $/MTok | Last verified | Source |
|---|---|---|---|---|---|
| Claude Opus 4.x ⚠ | $5 | $25 | $0.5 | 2026-06-05 | verify |
| Claude Sonnet 4.x ⚠ | $3 | $15 | $0.3 | 2026-06-05 | verify |
| Claude Haiku 4.x ⚠ | $1 | $5 | $0.1 | 2026-06-05 | verify |
| GPT-5-class (flagship) ⚠ | $1.25 | $10 | $0.125 | 2026-06-05 | verify |
| GPT-5 mini class ⚠ | $0.25 | $2 | $0.025 | 2026-06-05 | verify |
| Gemini Pro class ⚠ | $1.25 | $10 | not published | 2026-06-05 | verify |
| Gemini Flash class ⚠ | $0.3 | $2.5 | not published | 2026-06-05 | verify |
How the Rills row is derived
Rills meters AI operations in AI credits: 1 credit = $0.01 of model cost, rounded up per call, minimum 1 credit. Billable action steps (API calls, integrations) cost 2 workflow credits each. Triggers, logic, and human approvals never consume credits, and workflows cost $0 while paused awaiting approval.
The tier shown is the cheapest plan whose included credit pools plus overage cover your workload, where combined overage stays within each plan's default spending cap (50% of the base price — adjustable in the product). Above the largest plan's cap, the tool shows "Contact sales."
- Hobby: $29/mo · 10,000 workflow credits (overage $ 1.50 per 1,000) · 1,000 AI credits (overage $ 1.50 per 100)
- Professional: $99/mo · 50,000 workflow credits (overage $ 1.00 per 1,000) · 5,000 AI credits (overage $ 1.25 per 100)
- Business: $349/mo · 200,000 workflow credits (overage $ 0.75 per 1,000) · 20,000 AI credits (overage $ 1.00 per 100)
Frequently asked questions
How do I estimate AI agent costs?
Multiply your monthly agent runs by the LLM calls each run makes, then by the tokens per call: input tokens are billed at the model's input rate per million tokens and output tokens at its output rate. Agent loops surprise people because every reasoning step, tool call, and retry is its own model call — five calls per run at 4,000 input tokens each is 20 million input tokens per thousand runs.
Is Claude or GPT cheaper for agents?
It depends on the model class, not the vendor. Each provider's fast tier (Haiku-class, mini-class, Flash-class) costs a fraction of its frontier tier, and output tokens are typically 3–8× input price everywhere. Enter your own workload above — the cheapest badge goes to whichever model genuinely wins for your numbers.
Is a Rills workflow cheaper than running an AI agent?
Usually, at real volume — because of structure, not rates. An autonomous agent spends most of its model calls deciding what to do next; a Rills workflow encodes that routing as deterministic logic, which is free, and calls the model only for steps that genuinely need AI. Fewer model calls means a smaller bill, and every consequential step can wait for your approval at $0. At tiny volumes the base subscription can cost more than the raw token bill — the calculator shows that honestly.
What is an AI credit on Rills?
One AI credit covers $0.01 of underlying model cost. Each model call is rounded up to a whole credit with a one-credit minimum. Every plan includes a monthly credit pool; beyond it, overage is billed per 100 credits at your plan's published rate. Triggers, logic, and human approvals never consume credits, and a workflow paused for approval costs $0.
Why is my agent more expensive than a single chat call?
A chat question is one model call. An agent run is a loop: it reasons, calls tools, reads results, and reasons again — commonly five or more model calls per run, each carrying the conversation context as input tokens. Cost grows with calls per run, and with context size as the loop accumulates history.
Does prompt caching reduce cost?
Where a provider publishes a cached-read rate, input tokens served from cache cost roughly 10% of the full input price. Agent loops re-send a lot of identical context, so a high cache share can cut input cost substantially. Use the cache slider above to model it; models without a published cached rate ignore the slider.
Stop paying for reasoning loops.
Encode the routing as free logic, run the model only where it earns its keep, and keep every consequential step behind your approval.