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AI Cost-Claim Diligence

Stress-test a startup's claimed AI unit economics against vendor-exact pricing — verdict, real margin vs the 2026 AI median, trajectory, and a deal-memo.

Enter the target's claimed numbers
Prices resolve live from the aicost pricing SSOT. Nothing is stored.
OPTIMISTICThe claim is 2.35x below the vendor-exact floor, but it is reachable: it needs a sustained ~83.3% cache-hit rate. That is achievable and also easy to assume - ask for production cache metrics, not a target.
Deal-memo findingAt vendor-exact list pricing for the stated workload (18,000 input / 900 output tokens per query on claude-sonnet-5), inference COGS is $0.05 per query against a claimed $0.02 — a 2.35x gap, or roughly $648,000 of understated COGS per year at 2,000,000 querys/month. Real gross margin at vendor-exact cost is 60.8% against a claimed 83.3% and a 2026 AI median near 52%. Verdict: OPTIMISTIC.
Claimed COGS/query
$0.02
Vendor-exact floor
$0.047
Gap
2.35×
Understated COGS/yr
$648,000
Real gross margin
60.8%

Benchmark: At vendor-exact cost this sits above the 2026 AI range - strong, and worth confirming the token volumes are production figures rather than a best case. (2026 AI median ~52%)

Margin trajectory — investors price the path, not the level
HorizonCOGSGross margin
Today$0.04760.8%
+12 mo$0.010191.6%
+24 mo$0.002298.2%
AI median reached: already there

Routing (top COGS lever): Routing routine work to gpt-oss-20b and reserving claude-sonnet-5 for hard tasks is a 45.71x per-call spread - normally the single largest COGS lever, and the one a founder should already be able to describe.

Diligence questions to ask (7)
  1. What is the production cache-hit rate over the last 90 days, and how is it measured? (A target is not evidence.)
  2. Are the stated tokens per query the median or the mean? Show the distribution - retries and long-context outliers dominate COGS.
  3. Which model actually serves production traffic today, and what share is routed to cheaper models?
  4. Is any inference subsidised - vendor credits, cloud committed-use discounts, or a research programme? When does it end and what is the list-price cost after?
  5. Do the COGS in the deck match the data-room P&L line by line, and does COGS include embeddings, vector search, retries and egress?
  6. What happens to gross margin if token volumes per query grow with product depth, as they usually do?
  7. Have you modelled routing routine traffic to gpt-oss-20b? It is a 45.71x per-call spread and the fastest COGS lever available.
📄 Generate the PDF deal-memo →
📖 Data sources & methodology 150 text models · 9 embeddings · 40 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-09-07

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-09-07.
  • 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
2026-09-07
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 672 days captured
Anthropic Docs
2026-09-07
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 672 days captured
OpenAI
2026-09-07
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 673 days captured
Google AI
2026-09-07
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 648 days captured
Google Vertex
2026-09-07
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 648 days captured
DeepSeek
2026-09-07
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 587 days captured
xAI
2026-09-07
https://x.ai/api
Daily snapshot since Nov 2024 · 505 days captured
Mistral
2026-09-07
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 646 days captured
Cohere
2026-09-07
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 672 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 →