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AI LLM Token Cost & Multi-Provider Pricing Calculator

OpenAI, Claude, DeepSeek

Calculate and compare API token costs across OpenAI, Anthropic, Google Gemini, and DeepSeek directly in your browser. Model monthly query volumes, input/output token ratios, and prompt caching discounts to forecast AI infrastructure budgets with zero cloud uploads.

100% Client-Side Sandbox
Zero Data Ingestion
Zero Server Uploads

AI LLM Token Cost & Multi-Provider Pricing Calculator

Compare token pricing across OpenAI, Anthropic, DeepSeek, Google, and Meta models with prompt caching and batch discounts.

100% Client-Side Memory

Interactive AI LLM Token Cost & Multi-Provider Pricing Calculator Workspace

Calculate and compare API token costs across leading AI model providers (OpenAI GPT-4o, o1, o3-mini, Anthropic Claude 3.7 / 3.5 Sonnet, DeepSeek V3, DeepSeek R1, Google Gemini 2.0 Flash/Pro, Llama 3.3). Features prompt caching simulation, 50% Batch API discounts, per-call and monthly budget projections 100% in-browser.

How This Tool Works (100% Client-Side Engine)

The tool multiplies daily query volume by input tokens and output tokens, scaling to monthly totals. It applies official per-million-token pricing rates for each model: Cost = (InputTokens * InputRate) + (OutputTokens * OutputRate) - (CachedTokens * CacheDiscountRate). Results are charted side-by-side.

100% in-browser token calculation.
Supports flagship frontier, fast lightweight, and reasoning models.
Zero server roundtrips or API key requirements.

Step-by-Step Guide

Set Query Volume & Token Averages

Enter estimated queries per day (or month), average input tokens, and average output tokens per request.

Configure Prompt Caching Rate

Set estimated prompt cache hit percentage (e.g. 50%) for workflows reusing identical system prompts.

Compare Provider Costs Side-by-Side

Review the interactive comparison table and cost chart showing monthly projected bills across OpenAI, Anthropic, Google, and DeepSeek.

Why Use This Tool: Privacy & Performance

Building commercial AI applications requires evaluating the financial impact of model selection. API pricing differs radically across providers: input tokens cost significantly less than output tokens, reasoning models (like OpenAI o1 or DeepSeek R1) consume hidden "thinking" tokens, and prompt caching features offer up to 75-90% discounts on repeated system prompt tokens. Forecasting monthly infrastructure costs in spreadsheets is tedious and quickly becomes outdated. ToolByte AI LLM Token Cost & Multi-Provider Pricing Calculator maintains an up-to-date database of official API rates across OpenAI (GPT-4o, GPT-4o-mini, o1), Anthropic (Claude 3.5 Sonnet, Claude 3.5 Haiku), Google (Gemini 1.5 Pro, Flash), and DeepSeek (V3, R1). Enter expected daily user volume, average input/output tokens per query, and prompt cache hit rates to immediately compare cost projections across all major providers in your browser.

Common Use Cases & Workflows

Budgeting Enterprise AI SaaS Applications

Forecast monthly API bills based on expected active users, daily query volume, and average conversation length.

Comparing Frontier vs. Lightweight Model Costs

Evaluate whether routing 80% of routine queries to GPT-4o-mini or Claude 3.5 Haiku cuts operating costs by 90%.

Modeling Prompt Caching Financial Savings

Calculate how Anthropic and OpenAI prompt caching features reduce expenses on large static system instructions.

DeepSeek R1 vs. OpenAI o1 Reasoning Cost Analysis

Compare API expenditures for reasoning-heavy workflows that generate thousands of internal thinking tokens.

Technical Limitations & Operational Boundaries

API pricing tables reflect public published rates; enterprise volume discount agreements may provide lower custom rates.
Reasoning models (like o1 or R1) generate variable internal reasoning tokens that are billed as output tokens.
Cloud provider hosting markups (e.g. AWS Bedrock or Azure OpenAI) may differ slightly from direct provider APIs.

Frequently Asked Questions

Output tokens require sequential autoregressive generation where the GPU must execute a forward pass for every single generated token. Input tokens are processed in parallel in a single compute pass, making them much cheaper to compute.
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