Is your AI seat spend actually worth it?

Live seat prices across 29+ developer plans vs the value of the hours it saves — ROI multiple, break-even hours per user, and the honest adoption caveat.

What this calculator does

Prices your AI coding-assistant seats from the live plan catalog and weighs them against the value of the hours they save — the ROI multiple, the break-even hours per active user, and a cross-plan cost comparison.

Why use it
  • 2026's budget question is no longer 'what do tokens cost' but 'is the spend producing output' — this gives the renewal conversation a number instead of a debate.
  • Break-even is usually a fraction of an hour per active user per week; the honest risk is adoption, not the seat price — idle seats cost money and create no value.

New to this calculator? Start with the ⚡ Playground — a few sliders, instant ballpark. Then switch to the 🧮 Calculator for your exact number.

Two ways to use this: visualize in the Playground, then get your number in the Calculator.

Seat Cost Playground
Drag seats + hours saved to see the monthly bill you'd need to justify.
What does the AI seat bill run — and what would justify it?

Drag seats and team cost to see the monthly seat bill from live catalog prices. The full calculator adds adoption + hours saved for the ROI verdict.

Estimated monthly cost
Change the input sliders below to see new estimates.

How we got this estimate

💡Seats set the bill linearly; the loaded hourly cost sets how little time each active user must save to cover it — for most teams the break-even is minutes per week, not hours.

AI Seat ROI Calculator

Your plan, seats, adoption, and hours saved → ROI multiple + break-even.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges; Measured assistant productivity 2026: ~2-4 hrs/wk defensible for most roles (Vodafone Copilot avg 3 hrs/wk; dev tools ~3.6 hrs/wk; M365 studies 14-26 min/day). Enterprise daily-active adoption commonly 40-70%; best-run programs exceed 90% MAU.) so you can move forward now — your result gets more accurate as you replace them with your own measured numbers. Values marked * are rough estimates.

Sets seats, adoption, hours saved, and loaded cost. Pick your plan below.

Hint: The plan your team is on (or considering). Prices are live from our daily catalog.
Hint: How many people are on the plan — including the ones who never opened it.
Hint: Share of seats used in a normal week. 60-80% is typical after rollout; pilots run lower.
Hint: 2-5 hours is commonly reported for active users. Use your own survey if you have one.
Hint: Pay + overhead, roughly 1.4× wages. A fully-loaded engineer runs about $100-150/hour.
Hint: Premium-request overages or API credits on top of the seat. Leave 0 if none.

Results

Pick your plan, team size, adoption, and hours saved, then calculate to get the ROI multiple, break-even hours, and the cross-plan comparison.
📖 Data sources & methodology 158 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-21

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-21.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-07-21
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 624 days captured
Anthropic Docs
2026-07-21
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 624 days captured
OpenAI
2026-07-21
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 625 days captured
Google AI
2026-07-21
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 600 days captured
Google Vertex
2026-07-21
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 600 days captured
DeepSeek
2026-07-21
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 539 days captured
xAI
2026-07-21
https://x.ai/api
Daily snapshot since Nov 2024 · 457 days captured
Mistral
2026-07-21
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 598 days captured
Cohere
2026-07-21
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 624 days captured

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →