AI Cost Calculator - GPT-4, Claude, Gemini
Estimate monthly LLM API spend before you build. Configure prompt size, output length, request volume, and pick a model. Compare costs across OpenAI, Anthropic, and Google in one view.
Estimated Cost
Cost Comparison Across Models
| Model | Input ($/M) | Output ($/M) | Cost (your config) | vs Selected |
|---|
The Difference Between a Healthy AI Feature and a Runaway Bill
The most common AI-product failure mode in 2026 isn't accuracy or quality - it's unit economics. Teams ship features priced for an expected token-cost that turns out 3-5× higher in production because nobody modeled the full request lifecycle (system prompt + retrieved context + conversation history + completion). This calculator forces you to think through every cost lever before writing integration code.
Forecast Before You Build
Translate "we want to ship an AI feature" into a real monthly burn estimate before engineering picks up the ticket. Avoid the "POC was cheap, production blew the budget" pattern.
Compare Models Apples-to-Apples
GPT-4o-mini is 16× cheaper than GPT-4o. Claude 3.5 Haiku undercuts both. The right model depends on quality threshold, not list price - but the price gap should drive design decisions.
Set Pricing With Confidence
Know your unit AI cost per customer interaction. Required input for any SaaS pricing decision involving an AI feature - without it, gross margin is a guess.
Estimate Your Monthly AI Spend in 4 Steps
Pick a Use Case Preset (Optional)
Start with a typical configuration for chatbots, RAG, agents, code generation, or content creation. The preset loads sensible defaults for input/output token sizes you can tune from there.
Set Per-Call Token Sizes
Input = system prompt + retrieved context + conversation history + user message. Output = completion length. If you're not sure, use the AI Token Counter to measure a real prompt. For chat: input typically 500-2000 tokens; output 200-800 tokens.
Set Daily Call Volume
How many AI requests happen per day? For a B2B tool: ~5-20 per active user. For consumer chat: ~50-200 per active user. For background processing: based on event volume. Don't forget retries and failed calls - they cost too.
Compare Models
Switch between models and the cost updates instantly. The comparison table at the bottom shows your config priced across every model at once - useful for finding the cheapest option that meets your quality bar.
Who Needs an AI Cost Calculator
Founders Pitching AI Features
"Can we afford to ship this?" - answer in 30 seconds with concrete numbers, not feelings. Critical for board decks and pricing rounds.
Engineers Sizing Production
Validate that the AI feature scales economically before integration code is written. Cheaper than building it and discovering month-3 that it's unprofitable.
Finance & FP&A Teams
Build accurate cost-of-revenue models for AI products. Required input for board reviews, pricing-committee decisions, and investor updates.
Product Managers
Make data-informed decisions on which model tier to use per feature. GPT-4o for premium tier, GPT-4o-mini for free tier - quantify the gap to defend the choice.
Procurement Teams
Compare quoted vendor rates against published list prices. Vendors offering "AI features" should be paying these rates - verify their margin claims.
Anyone Learning LLM Economics
Build intuition. The same prompt costs $0.001 on Gemini Flash and $0.075 on GPT-4 - the ratio is more important than the absolute numbers.
Frequently Asked Questions
Prices are based on each provider's published list price as of 2026. They may change - always verify against the official pricing page before locking in budget commitments. Note: enterprise contracts often negotiate 20-40% off list, especially at high volumes.
Output generation requires more compute than input processing. Each output token requires a forward pass through the entire model; input tokens are processed in batch. Most providers price output 2-5× higher than input - making short, focused responses cheaper than long verbose ones.
No. The calculator uses standard list pricing. OpenAI's Prompt Caching, Anthropic's Prompt Caching, and Google's Context Caching can reduce repeated-prompt costs by 50-90% - these become significant for chatbot or long-context applications. To model with caching, reduce your effective input tokens by the cached portion (typically 70-90% of input is cacheable for chat).
Both OpenAI and Anthropic offer ~50% discount via async Batch APIs (results within 24h instead of seconds). For background processing - embedding generation, classification, summarization - Batch is the default-correct choice and halves your bill.
A simple rule: increase your effective call volume by 5-10% to account for retries from rate limits, timeouts, and content-filter failures. Higher for chat applications with rate limits, lower for batch processing where retries are rarer.
Often yes - but route smart. Most production systems use a 2-tier approach: a cheap, fast model (Haiku, GPT-4o-mini) for 80% of routine traffic, and a premium model (Sonnet, GPT-4o) for the 20% that needs higher quality. Build the routing logic into your application and pricing follows.
No. The calculator runs entirely in your browser. Your token counts and call volumes are never transmitted, logged, or stored.
Modeled the Cost. Now Build It Right.
Brainguru ships LLM applications with token economics modeled at architecture stage - not discovered in production. Eval suites, hallucination guards, model abstraction for cheap migration, and 24/7 monitoring built in.
