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Eval Benchmark Cost - Generation + LLM-as-Judge + Human Review

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Meet Lena Park. ML eng running a model bake-off before a launch. "We want to eval two models on 1,000 examples with an LLM judge and spot-check 10% by hand. What does a cycle cost - and what dominates?"

🔥 Token cost looked trivial, then someone added human review and the number 50x'd.

The story

Eval has three cost layers: generating outputs (the models under test), LLM-as-judge scoring, and human review. The first two are usually cheap in tokens.

Human review dominates fast. Even 10% of examples at a few minutes each, at a reviewer's hourly rate, can outweigh all the model + judge tokens combined - which is exactly the trade-off this calculator surfaces.

🎮 Playground

Eval Benchmark Cost Playground

Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.

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

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Top 3 right now

Verified 13 hours ago

About this calculator: Eval Benchmark Cost - Generation + LLM-as-Judge + Human Review

Estimate what one eval/benchmark cycle costs: generating outputs across models, LLM-as-judge scoring, and human review - where labor usually dominates token cost.

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.

▸ Test examples: Number of examples each model is evaluated on.
How to choose: Use your real eval set size; bigger sets cost proportionally more per run.
▸ Models compared: How many models the suite is run against in one pass.
How to choose: Each extra model re-runs the whole suite — keep the comparison set tight.
▸ % human-reviewed: Share of outputs graded by a human rather than automatically.
How to choose: Reserve human review for ambiguous or high-stakes cases; it is the priciest part.
▸ Model under test: The model whose outputs you are evaluating.
How to choose: Match the model you plan to ship; its token price drives generation cost.
▸ Judge model: The LLM-as-judge that grades each output.
How to choose: A capable mid-tier judge (e.g. Sonnet) balances cost and grading quality.
▸ Avg input tokens / call: Average prompt size per eval example.
How to choose: Measure your real eval prompts; longer prompts raise generation and judge cost.
▸ Avg output tokens / call: Average completion size per eval example.
How to choose: Use your expected answer length; output tokens usually bill higher than input.
▸ Minutes / review: Human minutes spent grading each reviewed output.
How to choose: Time a few real reviews; labor scales with this and the reviewed share.
📋 Typical values & starting points Don’t know a value yet? Start with these broad, sourced ballparks — the calculator’s ▾ Typical menus pre-load the same options.

Context: 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.

Input Default Typical ballparks
exampleCount moves the needle 1,000 Golden-set standard · 200 = 200 · Thorough · 500 = 500 · Benchmark-scale · 2,000 = 2,000
modelsCompared moves the needle 2 A/B · 2 = 2 · Shortlist · 3 = 3 · Vendor bake-off · 5 = 5
humanReviewPct moves the needle 10 Spot-check · 5% = 5 · Calibration standard · 10% = 10 · High-stakes · 20% = 20
minutesPerReview 3 Quick verdicts · 2 = 2 · Typical · 3 = 3 · Expert/domain · 6 = 6
judgeOverheadTokens 200 Lean rubric · 200 = 200 · Detailed rubric · 400 = 400 · Multi-dimension rubric · 800 = 800
avgInputTokens 800 Chat turn · ~500 in = 500 · Typical task · ~2K in = 2,000 · RAG-heavy · ~3.5K in = 3,500 · Long-context · ~10K in = 10,000
avgOutputTokens 400 Classification / label · ~50 out = 50 · Short answer · ~300 out = 300 · Typical response · ~800 out = 800 · Long-form · ~1.5K out = 1,500
modelSlug claude-haiku-4-5 —
judgeSlug claude-haiku-4-5 —
judgeOutputTokens 120 —
reviewerKey reviewer —

Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers. Try them live in the calculator; API & agent users get the same data from the MCP resource aicost://input-reference/eval-benchmark-cost.

Most popular

Everything above is the 80% case. The last 20% is where the money is.

The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.

  • An hour with the people who built the engines
  • Report the same day
  • The fee credits toward any AICost plan
  • Two slots a week
Book a Solution Session: $299 → or $99 for small business →

Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →

Ready to run your own numbers?

You have seen the shape of it. Open the calculator with your model, your tokens, your volume.

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Methodology

Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/20/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

📖 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 →