AI API Cost Calculator

What does your AI feature actually cost?

Pick a model. Set your workload. See daily, monthly, and annual cost - with the real optimizations most teams miss.

Pricing verified: 2026-07-21 158 models across 17 providers Caching + batch API applied
What this calculator does

See exactly what an LLM workload will cost across 70+ models. Pick a model, enter your tokens per request and daily volume, get per-request / daily / monthly / annual cost. Caching and Batch API savings calculated automatically.

Why use it
  • Stop guessing — turn "AI is expensive" into a precise monthly number you can defend to finance
  • Compare 70+ models side-by-side at YOUR token shape, not vendor marketing examples
  • Spot the 30-90% savings opportunities (prompt caching, Batch API, model swap) before you ship
  • Re-cost instantly when a vendor changes rates — your numbers stay current

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.

▶Playground A quick, visual way to see which factors move your number the most. Open the playground → ▤Calculator Enter your real workload for a precise number you can apply to your own usage. Go to the calculator →
AI Cost Calculator Playground
See the key inputs that move the needle to reduce monthly cost.
What will this cost per month?

The three inputs that move the bill. We assume a balanced-tier model and ~30 working days.

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

How we got this estimate
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💡Cost = (input + output tokens) × the model’s rate × requests × ~30 days. Switch model tiers in the full calculator.

AI Cost Calculator

Enter your exact workload for precise daily, monthly & annual cost.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges) 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.
🎮 Interactive Guide & Calculator Playground →
⚙️ Calculator inputs — tap to edit your workload
🎛 CALCULATOR
Your workload

Estimate conservatively - we'll show you what caching + batch mode save below.

Load a typical workload, then tweak the numbers.
Hint: Pick the model you'll run. The note below shows its rates.
Hint: Words you send in: prompt + question + docs. Short ~250, doc-heavy a few thousand.
Hint: Words it sends back. A line ~40, a paragraph ~200, long ~1,000+.
Hint: Uses per day at your busiest. Multiplies everything.
Hint: How often it re-reads the same setup text. High for chatbots; 0 if unsure.
Hint: 30 for everyday apps; 22 for business-days-only.
Compare all models →

Results

📈 RESULTS
💰 Your estimated cost
📋 Example Workload - change any field in the calculator above to see AI costs for your workload
Loading…
Monthly cost
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Per request-
Per day-
Input tokens/day-
Output tokens/day-
Input cost share-
Output cost share-
Annual-
Monthly tokens-
📋 What now?
  • Compare models — switch the model dropdown to see the same workload across 70+ options
  • Lock in savings — toggle caching and Batch mode to surface the 30-90% reductions before you ship
  • Set your budget — use the monthly + annual numbers as defensible inputs for finance
Need help cutting your AI bill? 💼 Talk to a CloudIntelligence advisor →
Now that you have your number…

What this means + what to do next

💡 What to consider beyond this number for full TCO
  • Observability + logging (prompts, outputs, latency, errors) — typically adds 5-10% to inference cost at production scale
  • Eval pipelines + benchmark sets — $500-$5K/mo even without continuous evaluation; budget more if quality drift matters
  • Human-in-the-loop review for edge cases — $4K-$12K/mo per FTE reviewer for production AI features
  • Retry / fallback overhead — typically 3-15% on top of base inference depending on error rate and retry logic
  • Vendor lock-in cost — invisible until migration day, often $50K+ in re-prompting + re-eval + downtime risk
Rule of thumb: Multiply this number by 1.5–2.5× for production-ready TCO. Lower end (1.5×) = internal tools with low error tolerance and no compliance overhead. Higher end (2.5×) = customer-facing AI features with eval pipelines, compliance logging, and human review.
Quantify the hidden costs:
  • If your workload is multi-turn (chat, agents, tool-using), costs compound per turn — this baseline misses that Agent Loop Cost
  • Quantifies lock-in cost on the day you need to switch vendors Vendor Concentration Risk
  • If you're adding retrieval, the embedding + vector DB + rerank costs aren't in this baseline Rag Pipeline
$ How this fits your overall ROI

This calculator gives you the cost number. Here's how to turn that into an ROI story:

  • What revenue or cost-saved does this AI feature drive monthly?
  • How long until cumulative AI cost exceeds the value the feature generates?
  • How sensitive is your business to vendor price changes? (Last 12 months saw -50% to +25% swings across major vendors.)
Bridge to ROI:
  • Convert per-request cost into per-customer or per-feature margin Margin Calculator
  • Project 12 months out with growth + price-change assumptions Annual Cost Forecaster
  • See cost at 10× and 100× current usage — the discontinuities matter Scale Projection
Doing something different?

Doing something different? These calculators may fit better:

  • For multi-turn agent loops with tool calls Agent Loop Cost
  • For full RAG over a knowledge base with embeddings + retrieval Rag Pipeline
  • For image / multimodal workloads where pricing differs Vision Cost

Go deeper

Our playbooks on cutting this number.

💾
Prompt Caching
The 50-90% discount most teams miss
📉
Token Volatility
Hedge your AI unit costs
🧮
AI Unit Economics
Is your AI feature profitable?
🔁
Agent Loop Guardrails
Stop $20K overnight bills

Need help using this calculator for your workloads?

AICost.ai has 50+ calculators and playbooks. Schedule an AvatarVA meeting and we'll work through your real cost scenarios across AI & Cloud: visibility, cost reduction, optimization, forecasting and capacity planning, without sacrificing accuracy or performance.

📅 Schedule an AvatarVA meeting →
📖 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
Voyage AI
2026-07-21
https://docs.voyageai.com/docs/pricing

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 →