Guides → Playground & Guide → Eval / Anti-Hallucination Cost - Continuous Scoring to Catch Silent Regressions

Eval / Anti-Hallucination Cost - Continuous Scoring to Catch Silent Regressions

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Meet Tom Becker. Engineering lead protecting agent quality across releases. "We ship prompt and model changes constantly. What does it cost to score a golden set every release so we catch regressions early?"

🔥 A model swap silently dropped answer quality and we found out from a customer, not a dashboard.

The story

Agents regress silently. A prompt tweak or model swap can quietly degrade quality, and without continuous eval you find out from complaints, not metrics.

This calculator costs an LLM-as-judge pipeline: a golden set scored on every release, sized by set size, release cadence, and judge-model tier.

The number is usually small next to the cost of a silent regression - and seeing it makes continuous eval an easy line to defend.

🎮 Playground

Eval / Anti-Hallucination 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 continuous eval cost?

Golden-set size x runs per month x tokens, with an LLM judge. This is the model-drift detect bill.

Estimated monthly cost
—

💡Cost = items x runs x (agent + judge tokens) x rates. The judge often dominates - see the full calculator.

Ready to run the numbers?

Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

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Top 3 right now

Verified 13 hours ago

About this calculator: Eval / Anti-Hallucination Cost - Continuous Scoring to Catch Silent Regressions

Cost a continuous evaluation pipeline: a golden set scored every release by an LLM-as-judge to catch silent quality regressions before users do.

🎛 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.

▸ Agent model (under test): The model whose outputs you are evaluating; its rates price the generation step.
How to choose: Use the model your agent actually runs in production.
▸ Judge model (LLM-as-judge): The model that scores each output; its input rate dominates because it re-reads item, response, and rubric.
How to choose: A strong general model; it can equal the agent or be a step up for hard cases.
▸ Golden set size (items): Number of representative eval cases scored each run.
How to choose: Start around 100-300 covering your real failure modes; grow as you find gaps.
▸ Eval runs / month: How often you run the full golden set.
How to choose: Per PR plus per release plus nightly drift checks; 20-60 a month is common for active teams.
▸ Agent input tokens / item: Prompt plus context the agent consumes per eval item.
How to choose: Match your production prompt size.
▸ Agent output tokens / item: Tokens the agent generates per item.
How to choose: Use a typical completion length.
▸ Judge rubric tokens / item: Extra rubric and few-shot context the judge reads on top of the item and response.
How to choose: A concise rubric is 200-600 tokens; longer rubrics raise judge cost.
▸ Judge verdict tokens / item: Judge output per item: a score plus a short reason.
How to choose: Keep it small; binary pass/fail with a one-line reason is often 30-80 tokens.
📋 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: Golden-set anchor: ~246 items per scenario gives ±5% at 95% confidence.

Input Default Typical ballparks
goldenSetSize moves the needle 200 Quick smoke set · 80 = 80 · Pilot set = 150 · Statistical slice (±5% @95%) = 250 · Production CI gate = 500 · Multi-slice suite = 1,000
evalRunsPerMonth moves the needle 30 Weekly regression = 4 · Nightly = 30 · Per-merge CI (~5/day) = 100
avgInputTokensPerItem moves the needle 2,000 Short prompt task = 500 · Typical single-turn = 2,000 · RAG item · k=3 chunks = 3,000 · RAG item · k=10 chunks = 10,000
avgOutputTokensPerItem 500 Classification / label = 50 · Short answer = 300 · Typical response = 500 · Long-form answer = 1,500
judgeRubricTokens 400 Minimal rubric * = 150 · Typical rubric * = 400 · Detailed multi-criteria rubric * = 1,000 (rough estimates)
judgeVerdictTokens 60 Score only * = 20 · Score + short rationale * = 60 · Structured multi-criteria verdict * = 300 (rough estimates)
samplingPct 2 Light · 1% = 1 · 2026 standard · 2% = 2 · High-stakes · 5% = 5
liveTrafficCallsPerMonth 0 Growing product · 100K/mo = 100,000 · Production · 500K/mo = 500,000 · High-volume · 5M/mo = 5,000,000
agentModelSlug claude-haiku-4-5 —
judgeModelSlug claude-haiku-4-5 —

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-hallucination-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.

🚀 Open the full calculator →

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 →