What every dollar of AI coding spend actually buys you
25 popular models, real prices, real engineering scenarios.
Data Source
Pricing pulled weekly from BerriAI's LiteLLM dataset and overlaid with hand-curated Anthropic, xAI, and frontier overrides.
Notice
Token assumptions per scenario are medians from typical agentic API traces. Your real workload may run hotter or cooler.
What is the AI coding token cost calculator?
Budget translated into tangible engineering output
This calculator translates a USD or EUR budget into concrete engineering work — input tokens, output tokens, and the number of medium features, PR reviews, lines of code, doc pages, or emails you can produce on a chosen model. It exists because most management-level AI budgeting decisions are made without anyone knowing what one dollar actually buys.
Token math is straightforward once you separate input from output. Every model charges separately for the tokens you send in and the tokens it streams back. Input is usually 70–90 percent of an agentic coding workload because the agent reads many files per action; chat workloads flip that ratio. Prompt caching, where supported, drops the input rate by roughly ten times for the cached portion.
Input tokens = (Budget × Input share) ÷ Effective input rate per tokenTasks per budget = floor(Budget ÷ Cost per task)Walk through the same $6 budget on three different models so the gap is visible.
Pick the budget and mix
$6 per developer per day, coding-agent task mix (85% input, 50% cache hits when supported).
Claude Opus 4.7 — frontier tier
Pricing $15/$75 per 1M with cache. Cost per medium feature ≈ $0.36. Budget buys 16 features per day before the cap.
Claude Sonnet 4.6 — mid tier
Pricing $3/$15 per 1M with cache. Cost per medium feature ≈ $0.072. Budget buys 83 features per day — five times more than Opus.
DeepSeek V3 — budget tier
Pricing $0.27/$1.10 per 1M with cache. Cost per medium feature ≈ $0.0066. Budget buys ~900 features per day at acceptable quality.
The medium-feature count is the headline number because it maps closest to "what does my engineering team ship per day?" A typical autonomous coding agent on a real repo burns 25–35 thousand input tokens per feature (file reads + grep results) and produces a 1–2 thousand token diff. If your number is in the single digits, the budget is too low for the model — escalate to a cheaper tier or raise the cap. If your number is in the hundreds, you've over-provisioned and can drop down a tier without losing capability.
The PR-review and lines-of-TypeScript counts are sanity comparators. A pull-request review burns 10–15 thousand input tokens and writes 1.5 thousand output tokens of structured prose; raw TypeScript generation is closer to 12 tokens per line, so the lines-of-TS count is roughly your "raw code throughput" budget.
The token estimates assume median traces — your actual repo size, prompt overhead, and tool-use loops can shift the number 30 percent in either direction. Cache hit rates vary by how stable your system prompt is and how long the conversation runs; the calculator's defaults are conservative.
Pricing changes weekly
Prices change without notice as providers compete. The dataset refreshes weekly via the LiteLLM cron, but the verifiedAt date on each model is the source of truth. Always confirm with the vendor's pricing page before signing a contract.