AI Cost Intelligence
Token math, model trade-offs, and the real cost of running AI in production.
Every model provider publishes a per-token rate. Almost nobody publishes what their actual workload costs once you account for retries, context bloat, structured-output schemas, fine-tuning, and the difference between batch and real-time. We do.
- Available calculators
- 3
Tools
LLM API Cost Calculator
LiveCompare GPT-5, Claude, Gemini, Llama, and Mistral side-by-side. Token-level precision for input, output, and context caching.
Open calculator →AI Cost-per-Task Calculator
LiveStop guessing tokens. Pick a workload (chatbot, RAG, agent, batch, summarization, code) and see real monthly cost across every major LLM.
Open calculator →Self-Host vs API Breakeven
LiveAt what monthly volume does running your own GPU beat paying per-token? Modeled for A100, H100, and consumer-tier hosts.
Open calculator →
Popular model comparisons
Head-to-head API cost on the same real workloads — always current, priced live from our data.
How to use this section
Work through the numbers in the order that prevents bad decisions.
- 01
Start with task volume, not model names
A cheap model can still be expensive if it needs long prompts, retries, or verbose outputs. Use the cost-per-task calculator first to anchor the workload, then compare providers once the input/output shape is realistic.
- 02
Separate prototype cost from production cost
A prototype usually optimizes for speed and quality. Production needs caching, batching, fallbacks, and a monthly error budget. The API calculator is built to make those operating assumptions visible before the invoice arrives.
- 03
Only rent GPUs after a breakeven check
Self-hosting looks cheaper per token only when utilization is high enough. If a GPU sits idle, the hourly bill keeps running. Use the self-host vs API page before turning cloud GPU pricing into a commitment.
Guides & deep dives
Long-form explainers that go deeper than the calculators — the hidden traps, the real math, and how operators actually cut the bill.
2026 LLM API Pricing Study: Real Cost Per Task
GuideIndependent data study pricing one identical task across the current LLM dataset, ranked from cheapest to most expensive with transparent methodology and source links.
Read guide →How Much Does It Cost to Run an AI Chatbot?
GuideReal 2026 monthly cost of running a production AI chatbot — model choice, prompt caching, conversation length, and the mistakes that triple your bill.
Read guide →How to Cut Your AI API Costs 50%+ Without Losing Quality
GuideThe seven levers that actually reduce LLM API spend — model tiering, prompt caching, batch, output control, lean RAG — with a decision framework and a worked example. Vendor-neutral, operator-tested.
Read guide →
Why this category exists
Pricing changes every quarter
Anthropic, OpenAI, and Google have each adjusted their pricing structure at least twice in the past 18 months. A calculator that doesn't track these changes is worse than no calculator.
Token counts are not intuitive
A 500-word document is not 500 tokens. Whether you're paying for input, output, or cached context matters enormously. We expose all three.
Vendor calculators undercount
Most vendor estimators show you the per-call cost of a single prompt and stop there. We model your monthly workload including retries, error budget, and the cost of switching providers later.
FAQ
- How often is pricing data updated?
- LLM pricing is reviewed on a seven-day cadence against public vendor pricing pages. The last completed verification date appears on each tool page.
- Do you include cached input discounts?
- Yes. Anthropic's prompt caching, OpenAI's batch API, and Gemini's context cache are all modeled in the relevant calculators.
- What about open-source models on Together / Replicate?
- The calculator includes the open-model providers listed in its current model table. Provider coverage and source links are visible directly in the calculator.