Coding token spend
Code context (the files you attach) makes input tokens the heavy side for AI coding.
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AI & LLM Cost
AI Coding Cost Calculator
Budget AI assisted development for your team. Enter developers, prompts per day and token sizes, choose a coding model, and see the cost per developer and per month against every major 2026 model.
Estimated monthly cost
$0
Cost per developer / month—
Cost per prompt—
Prompts per month—
Input token cost—
Output token cost—
Annual estimate—
Your team priced across AI models
Same prompt volume, cheapest coding model first.
What the AI Coding Cost Calculator does
AI coding assistants read your files, tests and errors as context, then write code back. That context is large, so coding prompts are input heavy compared with ordinary chat. An AI coding cost calculator helps you see whether a usage based API bill undercuts or overshoots a flat per seat subscription for your team.
The tool multiplies your developer count by daily prompts and working days, prices the tokens on the model you pick, and reports a cost per developer you can compare directly to seat pricing. If the per developer figure lands well under a flat plan, usage based access is the cheaper route, and the reverse holds when your team prompts heavily all day.
Heads up on pricing. The rates built into this tool are public list estimates for July 2026 and can change without notice. Providers also offer batch discounts, prompt caching and volume tiers that lower real costs, so treat the output as a planning estimate, not a quote.
The formula
prompts per month = developers × prompts per dev per day × working days
cost = (prompts × input tokens ÷ 1M × input rate) + (prompts × output tokens ÷ 1M × output rate)
cost = (prompts × input tokens ÷ 1M × input rate) + (prompts × output tokens ÷ 1M × output rate)
Worked example
Ten developers send 40 prompts a day over 22 working days, so 8,800 prompts a month. At 6,000 input and 700 output tokens on Claude Sonnet 4.5, input is 52.8M × $3 + output 6.16M × $15 per million = $158.40 + $92.40 = about $251 a month, roughly $25 per developer.
AI model pricing used in this calculator (July 2026 estimates)
| Model | Provider | Input / 1M tokens | Output / 1M tokens |
|---|---|---|---|
| GPT-5 | OpenAI | $1.25 | $10.00 |
| GPT-5 mini | OpenAI | $0.25 | $2.00 |
| Claude Opus 4.1 | Anthropic | $15.00 | $75.00 |
| Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 |
| Claude Haiku 4.5 | Anthropic | $1.00 | $5.00 |
| Gemini 2.5 Pro | $1.25 | $10.00 | |
| Gemini 2.5 Flash | $0.30 | $2.50 | |
| DeepSeek V3 | DeepSeek | $0.27 | $1.10 |
| Grok 4 | xAI | $3.00 | $15.00 |
| Llama 4 Maverick | Meta | $0.20 | $0.60 |
Prices are public list estimates for planning as of July 2026 and change often. Providers bill per token, where roughly 1,000 tokens equals about 750 words. Always confirm live rates on the provider pricing page before you set a budget.
Ways to lower your AI costs
- Match the model to the task. Route simple requests to a small model like GPT-5 mini, Gemini Flash or DeepSeek V3, and keep premium models for complex reasoning where quality pays off.
- Cache repeated context. Prompt caching means you stop paying full price for the same system prompt or documents on every call.
- Cap output length. Output tokens cost several times more than input, so limiting response length is often the fastest saving you can make.
- Trim prompts and history. Summarize old conversation turns and drop context the model does not actually need to answer well.
- Batch non-urgent work. Many providers offer batch pricing at around half the standard rate for jobs that can wait a few hours.
Frequently asked questions
Is AI coding cheaper per seat or per token?
It depends on intensity. Light users are usually cheaper on usage based API access, while all day heavy users often come out ahead on a flat monthly seat. Compare your per developer figure above to the seat price.
Why are coding prompts so input heavy?
Because the assistant needs your code context, open files, related modules, tests and error output, which can be thousands of tokens before you type a word.
Which model is best for coding?
Strong general models like Claude Sonnet, GPT-5 and Gemini 2.5 Pro lead on code quality. For quick completions a smaller, cheaper model often suffices.
How do I keep AI coding costs down?
Attach only the files that matter, reuse cached context, and let the assistant edit diffs rather than reprinting whole files, which cuts output tokens.
Are these costs exact?
They are July 2026 estimates. Real usage varies with how much context each prompt carries, so treat the output as a planning guide.