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.
IMPLAUSIBLEClaimed COGS is far below the vendor-exact floor; the workload or model story likely differs from what was described.
Deal-memo findingFounder claims $0.02/question COGS on claude-sonnet-4-6. At 8K input / 0.9K output tokens and vendor-exact 2026-07-20 pricing, the floor is $0.0375/question — a 1.9x gap. Their 83.3% gross margin is really 68.7% at list prices — above the ~52% 2026 AI median. Holding price and applying a ~50%/yr inference-cost decline, the real margin moves to 84.4% in 12mo and 92.2% in 24mo (AI median reached 12 months). To reach $0.02 they would need a sustained ~81% cache-hit rate (verify in production, not as a target), or contracted committed-use discounts or self-host economics. Ask which — and for evidence. At 0.8M questions/mo the gap is ~$168,000/yr of understated COGS.
Claimed COGS/question
$0.02
Vendor-exact floor
$0.0375
Gap
1.88×
Understated COGS/yr
$168,000
Real gross margin
68.7%

Benchmark: Real margin 68.7% is above the 2026 AI norm (50-60%); strong for the category. (2026 AI median ~52%)

Margin trajectory — investors price the path, not the level
HorizonCOGSGross margin
Today$0.037568.7%
+12 mo$0.018884.4%
+24 mo$0.009492.2%
AI median reached: 12 months

Routing (top COGS lever): Route routine questions to voxstral-mini ($0.0004) and reserve claude-sonnet-4-6 for genuinely hard tasks — a 105.3x per-call spread. Model routing is the single biggest COGS lever.

Diligence questions to ask (6)
  1. Are your token counts (8K in / 0.9K out per question) measured or estimated? Show a traced sample.
  2. What is your actual cache-hit rate in production, and is it durable as prompts change?
  3. Are you on batch pricing, and what share of volume can tolerate batch latency?
  4. What committed-use / enterprise discount are you assuming, and is it contracted?
  5. You could hit this on voxstral-mini — are you actually on that model, or a pricier one?
  6. Does this COGS include retries, guardrails, embeddings/retrieval, and orchestration — or only the primary call?
📄 Generate the PDF deal-memo →
📖 Data sources & methodology 158 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-22

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-22.
  • 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
2026-07-22
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 625 days captured
Anthropic Docs
2026-07-22
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 625 days captured
OpenAI
2026-07-22
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 626 days captured
Google AI
2026-07-22
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 601 days captured
Google Vertex
2026-07-22
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 601 days captured
DeepSeek
2026-07-22
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 540 days captured
xAI
2026-07-22
https://x.ai/api
Daily snapshot since Nov 2024 · 458 days captured
Mistral
2026-07-22
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 599 days captured
Cohere
2026-07-22
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 625 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.
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 →