AI Cost Estimator

Estimate your AI coding costs

AI Coding Cost Calculator

Calculate Your
AI Coding
Cost.

How much will it cost to build your app with Cursor, Claude Code, or Devin? Calculate token costs across 110+ models from 23 providers in seconds.

What Is AI API Cost?

When you use AI coding agents like Cursor, Claude Code, Devin, or Codex CLI to build software, every interaction with the underlying language model costs money. These tools make API calls to large language models (LLMs) such as GPT-4o, Claude Sonnet, Gemini Pro, or DeepSeek, and each call is billed based on the number of tokens processed.

Tokens are the fundamental unit of text that LLMs process. Roughly speaking, one token equals about 3-4 characters of English text, or about 0.75 words. Every prompt you send (input tokens) and every response the model generates (output tokens) is counted and billed separately, with output tokens typically costing 3-5x more than input tokens.

The hidden cost driver in AI-assisted coding is context accumulation. Unlike a simple chatbot conversation, coding agents need to read your entire codebase, understand file relationships, track previous changes, and maintain conversation history. As your project grows, input tokens per turn snowball dramatically — a 15,000 LOC project might require 50,000-200,000 input tokens per turn just for context.

Free AI Coding Cost Calculator

Our AI coding cost calculator gives you a realistic project budget before you write a single line of code. Unlike generic pricing pages that list per-token rates, this calculator models how real AI coding agents actually consume tokens — including context accumulation, tool calls, and iterative debugging loops that multiply your bill.

Simply answer four questions about your project (size, features, tooling, and quality level) and the calculator instantly compares costs across 110+ models from 23 providers. Whether you're evaluating Cursor vs Claude Code vs Devin, or deciding between GPT-5.6 and DeepSeek V3, this calculator shows you the real price difference — not just the per-token rate, but the total project cost including context overhead.

The calculator is free to use, requires no sign-up, and updates as providers change their pricing. Use it to set sprint budgets, justify tooling decisions to management, or simply understand where your AI coding dollars go.

How the Calculator Works

Our calculation model accounts for the unique way AI coding agents consume tokens. Rather than a simple “tokens per line of code” formula, we model the per-turn context growth that makes AI coding expensive.

  • Project scope determines the base lines of code (LOC) your agent needs to produce, from ~500 LOC for a micro app to ~50,000+ for an enterprise system.
  • Feature complexity adds a 15% overhead per feature integration (authentication, database, payments, etc.) because each feature requires additional context and iteration.
  • Agent tooling type dramatically affects token usage across five tiers: chat & copy-paste (~1x base turns), AI inside your code editor (~2x), hands-on coding agent in a terminal or desktop (~2.5x), set-goal-and-walk-away mode (~3.5x), and cloud-run autonomous sandboxes like Devin (~5x) due to fully unsupervised iteration.
  • Quality level multiplies iteration depth. Draft quality (1x) accepts first output, while enterprise TDD (2x) requires strict linting, full test suites, and edge case handling.

For each turn, we calculate input tokens as: base context window + codebase overhead + accumulated growth per turn, capped at the model's context window limit. This models the real-world behavior where each successive agent turn reads more code, more conversation history, and more system prompts. We then multiply total tokens by each model's published per-million-token pricing to produce the cost estimate across 110+ models from 23 providers.

Frequently Asked Questions

Why are input tokens so much more expensive than expected?

Every time an AI coding agent modifies a file, it must re-read the existing codebase structure, the system prompt, tool definitions, and recent conversation history. As the project grows, this context compounds. A project that starts with 5,000 input tokens per turn might require 150,000+ tokens per turn after 50 files have been created. This context accumulation is the primary cost driver — and why input costs often exceed output costs by 10-20x.

Which AI coding tool is cheapest to use?

Chat & copy-paste tools (ChatGPT, Claude.ai, Gemini) are cheapest in API costs because you manually wire code into your project, minimizing context overhead. For automated coding, hands-on agents like Claude Code, Codex CLI, or Aider using budget models (DeepSeek V4 Flash, GPT-5.4 Mini, Qwen3 Coder) offer the best balance of automation and cost. Set-goal-and-walk-away modes (Grok Build /goal, Claude Code auto) cost 30-50% more than hands-on use because of unsupervised verification loops. Cloud-run autonomous agents like Devin are the most expensive due to fully autonomous iteration and extreme context overhead.

How accurate are these estimates?

Our estimates model the general pattern of context growth in AI-assisted coding. Real-world costs can vary by 30-50% depending on prompt engineering efficiency, how well-structured the codebase is, caching (which can reduce costs significantly), and whether the agent encounters complex bugs requiring extra iteration. Use these estimates as a planning baseline, not a precise budget.

What is a “turn” in AI coding?

A turn is one complete request-response cycle with the AI model. In a coding context, a single turn might involve: reading 3 files, modifying 1 file, and running a linter check. CLI agents like Claude Code might execute 4-8x more turns than a web UI because they autonomously iterate — reading files, writing code, running tests, fixing errors, and repeating until the task is complete.

Can I reduce AI coding costs with caching?

Yes. Anthropic's prompt caching can reduce input costs by up to 90% for repeated context (system prompts, file contents that don't change between turns). OpenAI and Google offer similar caching mechanisms. Our estimates show uncached costs — with effective caching, your actual costs could be 40-60% lower for the input token portion.

Does model choice affect code quality?

Significantly. Premium models (Claude Opus, GPT-5.4, Gemini Pro) produce higher-quality code with fewer bugs, better architecture, and more thorough error handling — but cost 5-50x more than budget alternatives. Budget models (DeepSeek V3.2, Qwen3 30B, GPT-4.1 nano) work well for straightforward tasks but may require more iteration for complex logic, partially offsetting their cost savings.

Where does the pricing data come from?

Official provider pricing pages are the primary source for standard API rates. We use the OpenRouter API for model discovery and fallback pricing only when an official public API price is unavailable. Prices are in USD per million tokens. We update pricing data regularly as providers adjust their rates. Last verified: July 2026.