Calculate, compare, and
optimize AI model API costs
A free calculator, a manually reviewed model pricing comparison, and a growing library of engineering guides — everything you need to understand and reduce your large language model spend.
AI token cost management is now an engineering discipline
As LLM features move from prototype to production, token spend becomes one of the most volatile lines in a company's budget. Understanding it early is the difference between scaling profitably and being surprised by the invoice.
Costs scale faster than usage
Output tokens cost several times more than input, and agent loops re-send context on every step. A small feature can quietly become your largest cloud line item.
Every model prices differently
Input, output, and cached-input rates vary widely between GPT, Claude, Gemini and DeepSeek. The cheapest model per token is rarely the cheapest per task.
Small changes compound
Trimming a prompt by 500 tokens sounds trivial until you multiply it across a million monthly requests. Measuring first turns guesswork into engineering.
Everything you need to manage LLM spend
Token Cost Calculator
Estimate the exact cost of prompts, API calls, and long documents. Model input, output, and cached tokens across every major provider.
Open calculatorAI Model Cost Comparison
Compare per-million-token pricing, context windows, and ideal use cases for GPT, Claude, Gemini, DeepSeek and more, side by side.
Compare modelsCost Optimization Guides
Actionable playbooks on prompt caching, model routing, RAG efficiency, and agent budgets to cut spend without losing quality.
Read the guidesAI Engineering Insights
In-depth articles on LLM engineering, retrieval, agents, and infrastructure maintained by the TokenCostAI editorial team.
Visit the blogPricing for the models teams actually ship
The local catalog covers flagship, mid-tier, and low-cost models across major providers so you can estimate candidates against your workload. Verify the linked official source before making a purchasing decision.
View full pricing comparisonFrom the AI Cost Optimization Blog
English vs Chinese vs Japanese Tokens: A Reproducible Experiment
Compare nine official Gemini token counts against one character heuristic—and see why the frozen Japanese sample was underestimated by 13.5% to 25.6%.
10 min readAI API Cost OptimizationBatch API vs Standard API: The Real Cost After Reruns
See how much of the 50% Batch token discount remains after application quality checks, selective reruns, result reconciliation, and provider-specific operating limits.
11 min readAI API Cost OptimizationPrompt Cache Break-Even: When Caching Actually Saves Money
Calculate how many successful cache reuses are needed to offset write premiums or TTL storage—and why best-effort caching must be modeled from observed hit rates.
11 min readWhy this tool was created
TokenCostAI Editorial Team
Editorial team
TokenCostAI was created to make AI API cost estimation easier to understand before implementation. It pairs a browser-based calculator with transparent reference data, source links, and practical engineering context.
Read the full storyHow the calculations work
Cost is computed as (tokens ÷ 1,000,000) × price per million, applied separately to input, output, and cached tokens. Token counts for documents are estimated from character analysis, counting dense scripts such as Chinese separately from Latin text.
Data source explanation
Pricing is compiled from official provider pages and published as manually reviewed reference data. Because rates change frequently, every model links back to its official source so you can verify before you commit.
Privacy statement
Everything runs in your browser. Prompts, pasted text, and uploaded files are never sent to a server or stored. Read the full Privacy Policy.
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