What does one eval cycle cost?

Estimate an eval/benchmark run: generating outputs across models, LLM-as-judge scoring, and optional human review.

What this calculator does

Estimates the cost to run an evaluation or benchmark suite — generating model outputs across a set of test examples for one or more models — plus optional human review of the results.

Why use it
  • Eval suites quietly get expensive when you re-run them across many models or on every release
  • Shows how example count, models compared, and human-review share each drive the bill
  • Model token prices come live from the pricing catalog
  • Shareable URL captures your eval design

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.

Eval Cost Playground
Drag to see one eval cycle's cost (Sonnet judge).
What will an eval run cost?

Running a benchmark across models, with optional human review. Model + judge default in the teaser.

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

How we got this estimate
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💡Cost scales with examples × models; human review adds a labour cost on top of the API spend.

Eval Cost Calculator

Generation + judge + human review.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges; 2026 eval standard: golden datasets of 200-500 examples built from REAL production failures (not synthetic); LLM judge calibrated to 85-90% agreement with a human-annotated reference set; humans review the gold-set, judges the sample, heuristics every span. EU AI Act makes judge calibration a compliance artifact, not a nicety.) 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 suite size, models compared, token sizes, and human-review intensity. Pick your model under test and judge below.

Results

Enter your eval and calculate to see the cost.
AICost.ai pricing data keeps our calculations accurate across top AI and cloud vendors · last updated 2026-10-22

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-22.
  • 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
2026-10-22
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 708 days captured
Anthropic Docs
2026-10-22
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 708 days captured
OpenAI
2026-10-22
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 709 days captured
Google AI
2026-10-22
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 684 days captured
Google Vertex
2026-10-22
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 684 days captured
DeepSeek
2026-10-22
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 623 days captured
xAI
2026-10-22
https://x.ai/api
Daily snapshot since Nov 2024 · 541 days captured
Mistral
2026-10-22
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 682 days captured
Cohere
2026-10-22
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 708 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.
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 →