AI Cost Estimator

Estimate your AI coding costs

AI Cost Estimator

Estimate Your
AI Coding
Cost.

How much will it cost to build your app with Cursor, Claude Code, or Devin? Estimate token costs across 40+ models from 9 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.

How We Calculate Your Estimate

Our estimation model accounts for the unique way AI coding agents consume tokens. Rather than a simple “tokens per line of code” calculation, 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. A web UI copilot uses ~1x the base turns, while an autonomous sandbox agent like Devin uses ~8x due to containerized environments and extreme context overhead.
  • 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 40+ models from 9 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?

Web UI copilots (ChatGPT, Claude.ai) are cheapest in API costs because you manually copy-paste code, minimizing context overhead. However, they require the most manual effort. For automated coding, CLI agents like Claude Code or Aider using budget models (GPT-4.1 nano, DeepSeek V3.2, Qwen3 30B) offer the best balance of automation and cost. Autonomous sandboxes like Devin are the most expensive due to 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?

All pricing is sourced from the OpenRouter API (openrouter.ai/api/v1/models) and verified against each provider's official pricing pages. Prices are in USD per million tokens. We update pricing data regularly as providers adjust their rates. Last verified: April 2026.