AI Agent Token-Cost Estimator: Claude vs GPT vs Gemini (2026)
An agent run is a loop, not a single call. 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
- Gemini Pro classCheapest agent
- Per run
- $0.088
$44.00/mo - Claude Sonnet 4.x
- Per run
- $0.12
$60.00/mo - GPT-5-class (flagship)
- Per run
- $0.22
$110.00/mo - Rills workflowCheaper + supervised
- AI credits
- 1,000 / 200 included
- Workflow credits
- 3,000 / 5,000 included
Save $13.00/mo vs running this as an agent on Gemini Pro class — with human supervision built in.
Credit estimate based on Gemini Pro class pricing
AI overage: 800 credits × $1.50 per 100
Triggers, logic, and human approvals never consume credits · $0 while workflows are paused awaiting approval.
$31.00/mo
The supervised workflow comes out cheaper than the agent loop — with every consequential step waiting on your approval. Start your trial →
Prices are estimates, last verified 2026-06-17 — confirm against provider pricing pages before budgeting.
Methodology
- FormulaThe math is calls times tokens times rate.Monthly calls = runs × calls per run. Monthly cost = input tokens ÷ 1M × input rate, plus output tokens ÷ 1M × output rate. Cost per run divides the total by runs.
- CachingCaching discounts repeated context.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.
- Rills rowThe Rills row is deliberately conservative.Each AI step is priced at the same tokens as one agent call, on the cheapest model you selected. Savings come only from making fewer calls (logic routes for free), never from smaller prompts. When the workflow costs more on small workloads, the tool says so. It compares agent against workflow, 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 | 2026-06-17 | verify |
| Claude Sonnet 4.x | $3 | $15 | $0.3 | 2026-06-17 | verify |
| Claude Haiku 4.x | $1 | $5 | $0.1 | 2026-06-17 | verify |
| GPT-5-class (flagship) | $5 | $30 | $0.5 | 2026-06-17 | verify |
| GPT-5 mini class | $0.75 | $4.5 | $0.075 | 2026-06-17 | verify |
| Gemini Pro class | $2 | $12 | $0.2 | 2026-06-17 | verify |
| Gemini Flash class | $1.5 | $9 | $0.15 | 2026-06-17 | verify |
How the Rills row is derived
Rills meters AI work in credits, then shows the cheapest plan that fits your workload.
- AI credit
- $0.01 of model cost, rounded up per call, 1 credit minimum.
- Action step
- 2 workflow credits each (API calls, integrations).
- Free
- Triggers, logic, and human approvals; a paused workflow costs $0.
- Tier shown
- Cheapest plan whose pools plus overage cover you, with overage inside the highest spending cap that plan allows. New subscriptions start at half that, adjustable in settings. Above the largest cap, the tool shows "Contact sales."
- Starter: $19/mo · 5,000 workflow credits (overage $1.50 per 1,000) · 200 AI credits (overage $1.50 per 100)
- Professional: $99/mo · 50,000 workflow credits (overage $1.10 per 1,000) · 1,500 AI credits (overage $1.40 per 100)
- Business: $349/mo · 250,000 workflow credits (overage $0.85 per 1,000) · 5,000 AI credits (overage $1.25 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.