AI Agent Cost Estimator — LLM Workflow Pricing Calculator
Estimate the running cost of your AI agents across 5 workflow patterns (chatbot, coder, research, vision, multi-agent) and 4 providers including OpenAI, Anthropic, Google and DeepSeek — 7 models compared, ranked cheapest first.
Configure your agent
Coding agent — plan, generate and review code across 3 LLM calls.
Cost optimisations
Per query
$0.02
Per day
$90.00
Per month
$2,700
Per year
$32,850
Monthly tokens
300.0M in · 120.0M out
LLM calls / query
3
All models ranked cheapest
Monthly cost · 5,000 queries/day| Model | Per query | Monthly | Annual |
|---|---|---|---|
| GPT-4o-miniOpenAIcheapest | $0.0008 | $117.00 | $1,424 |
| Gemini 2.5 FlashGoogle | $0.0008 | $117.00 | $1,424 |
| DeepSeek V3DeepSeek | $0.0014 | $213.00 | $2,592 |
| Claude Haiku 3.5Anthropic | $0.0048 | $720.00 | $8,760 |
| Gemini 2.5 ProGoogle | $0.01 | $1,575 | $19,163 |
| GPT-4oOpenAI | $0.01 | $1,950 | $23,725 |
| Claude Sonnet 4Anthropic | $0.02 | $2,700 | $32,850 |
Switching to GPT-4o-mini would cut your monthly cost to $117.00.
Monthly token usage breakdown
How the estimator works
Choose your agent type
Pick chatbot, coder, research, vision or multi-agent. Each carries a realistic token footprint and number of LLM calls per query.
Set daily volume and model
Slide daily queries from 100 to 100,000 and select a model. Per-query, daily, monthly and annual costs update instantly.
Optimise and compare
Toggle prompt caching or batch pricing to see savings, and compare every model ranked cheapest-first for your workload.
Frequently asked questions
How is the monthly cost of an AI agent calculated?
Monthly cost = daily queries × (input tokens ÷ 1,000,000 × input price + output tokens ÷ 1,000,000 × output price) × 30. The estimator uses each agent type’s typical input and output token footprint per query, then multiplies by your daily volume to project a 30-day bill for every model.
Which agent type should I pick?
Chatbot is a single-turn assistant (500 input / 200 output tokens, 1 call). Coder covers plan-code-review loops (2,000 in / 800 out, 3 calls). Research covers search-analyse-summarise (5,000 in / 1,000 out, 5 calls). Vision handles image plus reasoning (1,000 in / 500 out, 2 calls). Multi-agent covers orchestrated workflows (10,000 in / 2,000 out, 10 calls). Pick the pattern closest to your workload.
What is the cheapest model for running AI agents?
For high-volume, low-complexity agents, budget models such as GPT-4o-mini, Gemini 2.5 Flash and DeepSeek V3 are usually the cheapest per query. The comparison table above ranks all models cheapest-first for your exact agent type and query volume, so you can see the winner instantly.
How do prompt caching and batch pricing reduce agent cost?
Prompt caching applies a discounted input rate when the same prompt prefix is reused across calls — useful for agents with large system prompts or retrieved context. Batch APIs (OpenAI, Anthropic) cut both input and output by roughly 50% in exchange for a 24-hour turnaround, which fits asynchronous agent pipelines well. Toggle both above to see the savings live.
Are these AI agent cost figures accurate?
All figures are estimates based on publicly available per-million-token pricing and typical agent token footprints. Real bills depend on your exact prompts, retrieval sizes, tool outputs and provider discounts. Use the estimator for planning and comparison, then confirm on each provider’s pricing page. Data was last updated on the date shown on this page.
Pricing data last updated 2026-07-26. All figures are estimates for planning only.
Ready to build? Start here
Once your agent costs are budgeted, ship it. These are the platforms and SDKs most teams use to deploy production AI agents.