{ "site": "https://getculpa.com", "entity": "Culpa", "category": "a local-first LLM cost, margin, and forecast ledger", "generated": "2026-08-01", "count": 60, "provenance": { "confidenceLevels": { "provider-reported": "Taken from a provider invoice or usage API. This corpus contains none.", "calculated": "Derived from token counts times a published price-book rate. The arithmetic is shown on the page.", "estimated": "Modelled. Always published as a range and labelled as such." }, "priceBook": { "note": "Published provider list prices, verified against the official pricing pages on 2026-07-02.", "verifiedOn": "2026-07-02" }, "limitations": [ "No first-party customer aggregates appear anywhere in this corpus.", "Worked examples marked illustrative use modelled token volumes priced at real published rates.", "Model pricing changes frequently. Check the effectiveDate on any page carrying rates." ] }, "endpoints": { "catalogue": "https://getculpa.com/api/pages", "pageText": "https://getculpa.com/{slug}/llms.txt", "hub": "https://getculpa.com/guides", "sitemap": "https://getculpa.com/sitemap.xml", "siteText": "https://app.getculpa.com/llms.txt" }, "pages": [ { "slug": "/what-is-cost-per-conversation", "url": "https://getculpa.com/what-is-cost-per-conversation", "textUrl": "https://getculpa.com/what-is-cost-per-conversation/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per conversation", "title": "What Is Cost per Conversation? Definition and Worked Math", "answer": "Cost per conversation is the total model spend for one complete exchange, every call, retry, and tool step included. It's a sum, not an average. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call from a versioned price book and rolls it up to the conversation that spent it, so you can name the exchange behind a spike.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0416", "claim": "cost of a five-turn support exchange with history resent each turn", "confidence": "calculated", "source": "Sum of five turns at Claude Sonnet 5 introductory rates ($0.002 per 1k input, $0.010 per 1k output, price book effective 2026-07-02). Full arithmetic shown in the teardown." }, { "value": "4.4x", "claim": "growth in input tokens between turn one and turn five of that exchange", "confidence": "calculated", "source": "4,400 input tokens at turn five divided by 1,000 at turn one, from the modelled token profile above." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-cost-per-feature", "url": "https://getculpa.com/what-is-cost-per-feature", "textUrl": "https://getculpa.com/what-is-cost-per-feature/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per feature", "title": "What Is Cost per Feature? Tagging LLM Spend to Product Surfaces", "answer": "Cost per feature is model spend grouped by the product surface that caused it, rather than by model or by day. It answers which part of your product burns the budget. Culpa, a local-first LLM cost, margin, and forecast ledger, tags every call to a feature at request time and rolls the spend up, so you can rank surfaces by what they actually cost.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$169", "claim": "modelled monthly spend across three surfaces sharing two models", "confidence": "calculated", "source": "Sum of three feature totals using real GPT-5.4 and GPT-5.4 mini rates from the price book (effective 2026-07-02). Token volumes are modelled, arithmetic shown in the teardown." }, { "value": "$50", "claim": "modelled monthly cost of a low-traffic report-drafting surface", "confidence": "calculated", "source": "(2,000 x $0.0025) + (3,000 x $0.015), GPT-5.4 rates per 1k tokens from the price book." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/what-is-effective-cost", "url": "https://getculpa.com/what-is-effective-cost", "textUrl": "https://getculpa.com/what-is-effective-cost/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "effective cost", "title": "What Is Effective Cost? The Rate You Actually Pay per Token", "answer": "Effective cost is what you really pay per useful token once retries, failed calls, and cache behaviour are counted, rather than the list rate on a pricing page. It's almost always higher. Culpa, a local-first LLM cost, margin, and forecast ledger, meters every call including the ones that failed, so your effective cost reflects spend rather than intent.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0031 per 1k", "claim": "modelled effective input rate against a $0.0025 list rate", "confidence": "calculated", "source": "$31.00 total billed divided by 10,000 thousand-token units of useful input. List rate from the GPT-5.4 price book row, effective 2026-07-02. Retry and discard percentages are modelled." }, { "value": "24%", "claim": "gap between list rate and effective rate in the modelled month", "confidence": "calculated", "source": "($0.0031 - $0.0025) / $0.0025, from the arithmetic in the teardown." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/what-is-token-accounting", "url": "https://getculpa.com/what-is-token-accounting", "textUrl": "https://getculpa.com/what-is-token-accounting/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "token accounting", "title": "What Is Token Accounting? Counting LLM Spend Without Floats", "answer": "Token accounting is recording input, output, and cached tokens for every call, then pricing each category at its own rate in exact decimal arithmetic. The three categories bill differently, so one combined count can't produce a correct figure. Culpa, a local-first LLM cost, margin, and forecast ledger, meters all three per call and prices them from a versioned book.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0037", "claim": "correct cost of a single cached Claude Haiku 4.5 call, priced by category", "confidence": "calculated", "source": "(1 x $0.001) + (2 x $0.0001) + (0.5 x $0.005), using Claude Haiku 4.5 rates per 1k tokens from the price book, effective 2026-07-02." }, { "value": "5%", "claim": "understatement produced by collapsing the three token categories into one count", "confidence": "calculated", "source": "($0.0037 - $0.0035) / $0.0037, from the two calculations shown in the teardown." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-blended-rate", "url": "https://getculpa.com/what-is-blended-rate", "textUrl": "https://getculpa.com/what-is-blended-rate/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "blended rate", "title": "What Is a Blended Rate? Averaging LLM Cost Across Models", "answer": "A blended rate is your average cost per thousand tokens across every model you run, weighted by how much traffic each one takes. It's one number for a mixed fleet. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call at its own model rate first and blends afterwards, so the average never hides which model moved.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0011 to $0.0015 per 1k", "claim": "modelled blended input rate before and after a mix shift at identical token volume", "confidence": "calculated", "source": "Weighted averages of real Claude Haiku 4.5 and Claude Sonnet 5 input rates from the price book, effective 2026-07-02. Token splits are modelled, arithmetic shown in the teardown." }, { "value": "36%", "claim": "bill increase caused by mix shift alone, with volume and list prices unchanged", "confidence": "calculated", "source": "($15 - $11) / $11, from the two monthly totals in the teardown." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-gross-margin-per-customer", "url": "https://getculpa.com/what-is-gross-margin-per-customer", "textUrl": "https://getculpa.com/what-is-gross-margin-per-customer/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "gross margin per customer", "title": "What Is Gross Margin per Customer for AI Products?", "answer": "Gross margin per customer is what an account pays you minus what serving it costs in model spend, per account rather than averaged. On usage-heavy AI products the spread between accounts is enormous. Culpa, a local-first LLM cost, margin, and forecast ledger, tags spend to a customer and reconciles it against revenue you type in or import.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$260", "claim": "modelled monthly model spend for a heavy account paying $200", "confidence": "calculated", "source": "(60,000 x $0.002) + (14,000 x $0.010) using real Claude Sonnet 5 introductory rates per 1k tokens from the price book, effective 2026-07-02. Usage volumes are modelled." }, { "value": "-30% to 96%", "claim": "modelled gross margin spread between two accounts on an identical plan", "confidence": "estimated", "source": "Range across the two modelled usage profiles above, both priced at real Claude Sonnet 5 rates. Shown as a range because the endpoints depend entirely on your own usage distribution." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-llm-burn-forecast", "url": "https://getculpa.com/what-is-llm-burn-forecast", "textUrl": "https://getculpa.com/what-is-llm-burn-forecast/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "llm burn forecast", "title": "What Is an LLM Burn Forecast? Predicting Next Month's Bill", "answer": "An LLM burn forecast projects your model spend forward from your own measured usage rather than from a rate card. A credible one carries a range and states its assumptions. Culpa, a local-first LLM cost, margin, and forecast ledger, forecasts deterministically from your ledger and stores every projection so its accuracy can be scored later.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$700 to $950", "claim": "modelled forecast range for a launch month at 2,000 active users", "confidence": "estimated", "source": "Central figure $800, from driver-level arithmetic at real GPT-5.4 rates (price book, effective 2026-07-02) with a 24% effective-rate uplift. Shown as a range because launch-week traffic is the dominant uncertainty." }, { "value": "$0.22", "claim": "modelled steady-state model cost per active user per month", "confidence": "calculated", "source": "(40 x $0.0025) + (8 x $0.015), GPT-5.4 rates per 1k tokens from the price book. Per-user token volumes are modelled." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/what-is-cost-attribution", "url": "https://getculpa.com/what-is-cost-attribution", "textUrl": "https://getculpa.com/what-is-cost-attribution/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost attribution", "title": "What Is LLM Cost Attribution? Linking Spend to Its Cause", "answer": "LLM cost attribution links every dollar of model spend to the call, conversation, feature, and customer that caused it. It's a capture-time decision, because the identifiers can't be recovered afterwards. Culpa, a local-first LLM cost, margin, and forecast ledger, attaches those identifiers as calls leave and maps the chain from dollar through to customer.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$300 to $380", "claim": "modelled monthly spend increase traced through four attribution levels", "confidence": "estimated", "source": "Modelled scenario used to contrast grouping levels, priced against real GPT-5.4 rates from the price book, effective 2026-07-02. Published as a range because no customer data backs the point figure." }, { "value": "85% to 95%", "claim": "share of the modelled increase attributable to a single product surface", "confidence": "estimated", "source": "Modelled distribution across surfaces in the teardown above. A range, because it illustrates the shape rather than a measured result." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/what-is-prompt-cache-hit-rate", "url": "https://getculpa.com/what-is-prompt-cache-hit-rate", "textUrl": "https://getculpa.com/what-is-prompt-cache-hit-rate/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "prompt cache hit rate", "title": "What Is Prompt Cache Hit Rate? And When Caching Costs More", "answer": "Prompt cache hit rate is the share of your input tokens that billed at the cached rate rather than the standard one. On some providers a cache write bills above the input rate, so a badly tuned cache costs more than none. Culpa, a local-first LLM cost, margin, and forecast ledger, meters cached, uncached, and cache-write tokens separately per call.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "1 read", "claim": "break-even point before a five-minute cache write pays for itself on Claude Haiku 4.5", "confidence": "calculated", "source": "Incremental write cost $0.001 divided by the $0.0036 per-read saving = 0.28, so the first read clears it. Claude Haiku 4.5 rates from the price book (effective 2026-07-02) with the published 1.25x write multiplier. Matches Anthropic's own statement that a five-minute cache pays off after one read." }, { "value": "25%", "claim": "cost penalty of rebuilding the cache on every call versus not caching at all", "confidence": "calculated", "source": "($0.005 - $0.004) / $0.004, from the per-call arithmetic shown in the teardown." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-cost-per-user", "url": "https://getculpa.com/what-is-cost-per-user", "textUrl": "https://getculpa.com/what-is-cost-per-user/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per user", "title": "What Is Cost per User for an AI Product? Median vs P90", "answer": "Cost per user is model spend attributed to individual users, read as a distribution rather than an average. On AI products the top decile routinely costs many times the median. Culpa, a local-first LLM cost, margin, and forecast ledger, tags each call to a user and reports the spread, so your pricing floor comes from the heavy tail rather than the mean.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.51 mean, $0.135 median", "claim": "modelled cost per active user across a long-tailed distribution of 1,000 users", "confidence": "calculated", "source": "Totals computed at real Gemini 2.5 Flash rates per 1k tokens from the price book, effective 2026-07-02. The user distribution is modelled, arithmetic shown in the teardown." }, { "value": "53%", "claim": "share of modelled total spend generated by the heaviest 1% of users", "confidence": "calculated", "source": "$270 from ten heavy users divided by the $513 modelled total, from the teardown arithmetic." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/why-did-my-openai-bill-go-up", "url": "https://getculpa.com/why-did-my-openai-bill-go-up", "textUrl": "https://getculpa.com/why-did-my-openai-bill-go-up/llms.txt", "pageType": "problem", "intent": "AUDIT", "primaryKeyword": "why did my openai bill go up", "title": "Why Did My OpenAI Bill Go Up? The Four Causes, Ranked", "answer": "Your OpenAI bill went up for one of four reasons: more calls, longer calls, a shift toward a pricier model, or retries you paid for twice. Culpa, a local-first LLM cost, margin, and forecast ledger, prices every call from a versioned price book and names the conversation, feature, and customer behind the increase.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$400 to $600", "claim": "modelled monthly spend before and after, priced at real GPT-5.4 rates", "confidence": "estimated", "source": "Baseline (40,000 x $0.0025) + (20,000 x $0.015) using GPT-5.4 rates per 1k tokens from the price book, effective 2026-07-02. Published as a range because the token volumes are modelled." }, { "value": "$0.0025 / $0.015 per 1k", "claim": "GPT-5.4 input and output rates used throughout the arithmetic", "confidence": "calculated", "source": "Culpa price book row for openai/gpt-5.4, effective 2026-07-02, verified against OpenAI API pricing." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/llm-retry-loop-cost", "url": "https://getculpa.com/llm-retry-loop-cost", "textUrl": "https://getculpa.com/llm-retry-loop-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "llm retry loop cost", "title": "LLM Retry Loop Cost: Why Failed Calls Still Bill You", "answer": "A retry loop costs you the full rate on every attempt, because a provider bills tokens it generated even when your code discarded the response. Culpa, a local-first LLM cost, margin, and forecast ledger, meters failed and retried calls alongside successful ones, so your retry waste shows up as a number instead of an unexplained gap.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.042", "claim": "billed cost of one usable result after two rejected attempts", "confidence": "calculated", "source": "Three attempts at (3 x $0.002) + (0.8 x $0.010), using Claude Sonnet 5 introductory rates per 1k tokens from the price book, effective 2026-07-02." }, { "value": "3x", "claim": "multiple of intended cost paid for a single usable result at a three-attempt retry", "confidence": "calculated", "source": "$0.042 billed divided by the $0.014 single-attempt cost, from the teardown arithmetic." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/agent-fan-out-cost", "url": "https://getculpa.com/agent-fan-out-cost", "textUrl": "https://getculpa.com/agent-fan-out-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "agent fan out cost", "title": "Agent Fan Out Cost: Why One Request Becomes Thirty Calls", "answer": "Agent fan out is the gap between one user request and the many model calls an agent makes to satisfy it. Cost scales with steps, not requests, so a modest request count can carry a large bill. Culpa, a local-first LLM cost, margin, and forecast ledger, traces every step in a run back to the conversation that started it.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.39", "claim": "modelled cost of one twelve-step agent run on a frontier model", "confidence": "calculated", "source": "Sum of twelve steps with input growing 2,000 to 8,600 tokens, priced at Claude Opus 4.8 rates per 1k tokens from the price book, effective 2026-07-02. Step profile is modelled." }, { "value": "$0.14 to $0.39", "claim": "cost range for the same run capped at six steps versus twelve", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic above, at real Claude Opus 4.8 rates. A range because real step counts vary per run." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/prompt-cache-miss-cost", "url": "https://getculpa.com/prompt-cache-miss-cost", "textUrl": "https://getculpa.com/prompt-cache-miss-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "prompt cache miss cost", "title": "Prompt Cache Miss Cost: What a Broken Cache Really Costs", "answer": "A prompt cache miss bills your repeated prompt at the full input rate instead of the cached rate, which on some models is ten times more. Culpa, a local-first LLM cost, margin, and forecast ledger, meters cached, uncached and cache-write tokens separately per call, so a cache that stopped working shows up the day it breaks.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "10x", "claim": "cost of a cache miss against a cache hit on Claude Haiku 4.5", "confidence": "calculated", "source": "$0.001 input rate divided by the $0.0001 cached rate, both per 1k tokens from the Claude Haiku 4.5 price book row, effective 2026-07-02." }, { "value": "$400 to $4,000", "claim": "modelled monthly cost of a 4,000-token system prompt at one million calls, fully cached versus fully missed", "confidence": "estimated", "source": "Endpoints from the teardown arithmetic at real Claude Haiku 4.5 rates. A range because it brackets the two extremes of hit rate." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/context-creep-cost", "url": "https://getculpa.com/context-creep-cost", "textUrl": "https://getculpa.com/context-creep-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "context creep cost", "title": "Context Creep Cost: When Your Prompt Grows and Nobody Notices", "answer": "Context creep is the slow growth of your prompt across releases, as instructions, examples and history get added and nothing is removed. It raises cost per call with no traffic change at all. Culpa, a local-first LLM cost, margin, and forecast ledger, tracks tokens per call over time and maps a rise to the release that caused it.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$7.50 per token per month", "claim": "cost of one extra system-prompt token at three million calls a month", "confidence": "calculated", "source": "3,000,000 calls x $0.0025 per 1k input tokens / 1,000, using the GPT-5.4 rate from the price book, effective 2026-07-02." }, { "value": "$8,250 to $15,000", "claim": "modelled monthly input spend before and after a 900-token prompt increase", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at the real GPT-5.4 input rate. A range because the call volume is modelled." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/llm-cost-spike-after-launch", "url": "https://getculpa.com/llm-cost-spike-after-launch", "textUrl": "https://getculpa.com/llm-cost-spike-after-launch/llms.txt", "pageType": "problem", "intent": "AUDIT", "primaryKeyword": "llm cost spike after launch", "title": "LLM Cost Spike After Launch: What Actually Drives It", "answer": "A launch spike is rarely proportional to traffic. New users explore more per session than retained ones, they arrive on your cheapest tier, and any unbounded loop meets its worst input at once. Culpa, a local-first LLM cost, margin, and forecast ledger, forecasts a launch from your own usage curve and prices it before you ship.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$440 to $1,730", "claim": "modelled launch-month spend, naive forecast versus cohort-based forecast", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at real GPT-5.4 rates from the price book, effective 2026-07-02. A range because the first-week usage multiple is modelled, not measured." }, { "value": "$0.22", "claim": "modelled steady-state model cost per active user per month", "confidence": "calculated", "source": "(40 x $0.0025) + (8 x $0.015) using GPT-5.4 rates per 1k tokens from the price book. Per-user token volumes are modelled." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/llm-model-mix-drift", "url": "https://getculpa.com/llm-model-mix-drift", "textUrl": "https://getculpa.com/llm-model-mix-drift/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "llm model mix drift", "title": "LLM Model Mix Drift: The Bill Rises With No Code Change", "answer": "Model mix drift is traffic shifting toward pricier models without anyone changing the code, through fallbacks, routing rules or a default that moved. Volume holds and the bill climbs. Culpa, a local-first LLM cost, margin, and forecast ledger, records the model that served every call, so a mix shift separates cleanly from a price change.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "30.6%", "claim": "modelled bill increase from a tenth of traffic moving to a pricier model at identical volume", "confidence": "calculated", "source": "($10.45 - $8.00) / $8.00, using real Gemini 2.5 Flash and Gemini 2.5 Pro rates per 1k tokens from the price book, effective 2026-07-02. Token volumes are modelled." }, { "value": "$8.00 to $10.45", "claim": "modelled monthly spend before and after the mix shift, same token volume", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at real Gemini rates. A range because the traffic split is modelled." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/llm-eval-job-cost", "url": "https://getculpa.com/llm-eval-job-cost", "textUrl": "https://getculpa.com/llm-eval-job-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "llm eval job cost", "title": "LLM Eval Job Cost: The Line Item Nobody Watches", "answer": "Evaluation runs bill at production rates while serving no customer, and they scale with test cases times prompt variants times a judge call. That product grows fast. Culpa, a local-first LLM cost, margin, and forecast ledger, tags eval traffic as its own feature, so it stops hiding inside your unattributed spend.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$14.55", "claim": "modelled cost of one full eval run across 200 cases and 3 variants with a model judge", "confidence": "calculated", "source": "600 test calls at $0.00975 plus 600 judge calls at $0.0145, using real GPT-5.4 and GPT-5.5 rates per 1k tokens from the price book, effective 2026-07-02. Case counts and token sizes are modelled." }, { "value": "60%", "claim": "share of the modelled eval run cost taken by the judge rather than the model under test", "confidence": "calculated", "source": "$8.70 judge subtotal divided by the $14.55 run total, from the teardown arithmetic." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/tool-definition-token-cost", "url": "https://getculpa.com/tool-definition-token-cost", "textUrl": "https://getculpa.com/tool-definition-token-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "tool definition token cost", "title": "Tool Definition Token Cost: You Pay for Every Unused Tool", "answer": "Tool definitions are input tokens, so every schema you attach bills on every call whether the model uses it or not. A large tool block is a fixed tax on all traffic. Culpa, a local-first LLM cost, margin, and forecast ledger, meters input tokens per call, so a growing tool block shows up as a rising floor rather than a mystery.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0064 per call", "claim": "cost of a 3,200-token tool definition block at real Claude Sonnet 5 input rates", "confidence": "calculated", "source": "3.2 x $0.002 per 1k input tokens, using the Claude Sonnet 5 introductory rate from the price book, effective 2026-07-02." }, { "value": "$9,000 to $32,000", "claim": "modelled monthly tool-block spend at five million calls, trimmed versus untrimmed", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at the real Claude Sonnet 5 input rate. A range because call volume and block size are modelled." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/unattributed-llm-spend", "url": "https://getculpa.com/unattributed-llm-spend", "textUrl": "https://getculpa.com/unattributed-llm-spend/llms.txt", "pageType": "problem", "intent": "AUDIT", "primaryKeyword": "unattributed llm spend", "title": "Unattributed LLM Spend: The Bucket That Quietly Grows", "answer": "Unattributed spend is every dollar your breakdown can't assign to a feature, user or customer. It grows because new call sites ship without tags, and it hides your worst waste. Culpa, a local-first LLM cost, margin, and forecast ledger, tracks the untagged share as its own number, so the gap in your attribution stays visible.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "30% to 35%", "claim": "modelled share of a monthly invoice sitting outside the feature breakdown", "confidence": "estimated", "source": "$3,000 unattributed against a $9,000 modelled invoice, from the teardown arithmetic. Published as a scenario because no customer data backs the split." }, { "value": "$1,900 to $3,000", "claim": "modelled unattributed spend, background jobs alone versus the full gap", "confidence": "estimated", "source": "Both endpoints from the modelled breakdown above. A range because it brackets one known contributor against the total." } ], "sources": [ { "label": "Groq pricing", "url": "https://groq.com/pricing" } ] }, { "slug": "/gpt-5-4-pricing", "url": "https://getculpa.com/gpt-5-4-pricing", "textUrl": "https://getculpa.com/gpt-5-4-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gpt-5.4 pricing", "title": "GPT-5.4 Pricing: $2.50 In, $15.00 Out per Million Tokens", "answer": "GPT-5.4 pricing is $2.50 per million input tokens and $15.00 per million output tokens, on rates verified 2026-07-02. Output bills six times input, so answer length drives your bill harder than prompt length. Culpa, a local-first LLM cost, margin, and forecast ledger, prices every call from that rate and names the conversation that spent it.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$2.50 / $15.00 per million", "claim": "GPT-5.4 input and output rates", "confidence": "calculated", "source": "Culpa price book row for openai/gpt-5.4, $0.0025 and $0.015 per 1k, effective 2026-07-02, verified against OpenAI API pricing. Shown here per million." }, { "value": "6x", "claim": "output rate as a multiple of the input rate on GPT-5.4", "confidence": "calculated", "source": "$15.00 divided by $2.50, from the same price-book row." }, { "value": "90%", "claim": "discount the cached input rate carries against standard input", "confidence": "calculated", "source": "1 - ($0.25 / $2.50), using the cached and input rates from the same row." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/gpt-5-5-pricing", "url": "https://getculpa.com/gpt-5-5-pricing", "textUrl": "https://getculpa.com/gpt-5-5-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gpt-5.5 pricing", "title": "GPT-5.5 Pricing: $5.00 In, $30.00 Out, Same as GPT-5.6 Sol", "answer": "GPT-5.5 pricing is $5.00 per million input tokens and $30.00 per million output tokens, exactly double GPT-5.4 on both sides. The premium earns itself only when a wrong answer costs more than the call. Culpa, a local-first LLM cost, margin, and forecast ledger, meters both models side by side so the comparison is measured, not argued.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$5.00 / $30.00 per million", "claim": "GPT-5.5 input and output rates", "confidence": "calculated", "source": "Culpa price book row for openai/gpt-5.5, $0.005 and $0.030 per 1k, effective 2026-07-02, verified against OpenAI API pricing. Shown here per million." }, { "value": "2.0x", "claim": "GPT-5.5 cost relative to GPT-5.4 on both input and output", "confidence": "calculated", "source": "$5.00 / $2.50 and $30.00 / $15.00, from the two price-book rows." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/gpt-5-4-mini-pricing", "url": "https://getculpa.com/gpt-5-4-mini-pricing", "textUrl": "https://getculpa.com/gpt-5-4-mini-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gpt-5.4 mini pricing", "title": "GPT-5.4 mini Pricing: $0.75 In, $4.50 Out per Million", "answer": "GPT-5.4 mini pricing is $0.75 per million input tokens and $4.50 per million output tokens, roughly a third of GPT-5.4 on both sides. Cheap enough that teams stop counting calls, which is how volume eats the saving. Culpa, a local-first LLM cost, margin, and forecast ledger, meters every call so a cheap model can't hide behind its rate.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$0.75 / $4.50 per million", "claim": "GPT-5.4 mini input and output rates", "confidence": "calculated", "source": "Culpa price book row for openai/gpt-5.4-mini, $0.00075 and $0.0045 per 1k, effective 2026-07-02, verified against OpenAI API pricing. Shown here per million." }, { "value": "$4,560 to $7,560", "claim": "modelled monthly cost of a mini routing step at eight million calls, trimmed prompt versus untrimmed", "confidence": "estimated", "source": "From the teardown arithmetic at real GPT-5.4 mini rates. Published as a modelled scenario because the call volume and token sizes are assumptions." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/gpt-5-4-pro-pricing", "url": "https://getculpa.com/gpt-5-4-pro-pricing", "textUrl": "https://getculpa.com/gpt-5-4-pro-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gpt-5.4 pro pricing", "title": "GPT-5.4 Pro Pricing: $30 In, $180 Out, and No Cached Rate", "answer": "GPT-5.4 Pro pricing is $30.00 per million input tokens and $180.00 per million output tokens, and GPT-5.5 Pro bills identically. Neither carries a cached rate, so a repeated system prompt pays full price every call. Culpa, a local-first LLM cost, margin, and forecast ledger, meters the repeated share of your input so that cost stops being invisible.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$30.00 / $180.00 per million", "claim": "GPT-5.4 Pro and GPT-5.5 Pro input and output rates, identical on both models", "confidence": "calculated", "source": "Culpa price book rows for openai/gpt-5.4-pro and openai/gpt-5.5-pro, both $0.030 and $0.180 per 1k, effective 2026-07-02, verified against OpenAI API pricing." }, { "value": "12x", "claim": "Pro tier cost as a multiple of GPT-5.4 on both input and output", "confidence": "calculated", "source": "$30.00 / $2.50 and $180.00 / $15.00, from the price-book rows." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/claude-sonnet-5-pricing", "url": "https://getculpa.com/claude-sonnet-5-pricing", "textUrl": "https://getculpa.com/claude-sonnet-5-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "claude sonnet 5 pricing", "title": "Claude Sonnet 5 Pricing: $2 In, $10 Out Until 2026-08-31", "answer": "Claude Sonnet 5 pricing is $2.00 per million input tokens and $10.00 per million output tokens on an introductory rate. From 2026-09-01 it rises to $3.00 and $15.00, a 50% increase on both sides. Culpa, a local-first LLM cost, margin, and forecast ledger, holds dated rates, so a forecast built today prices September correctly.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$2.00 / $10.00 per million", "claim": "Claude Sonnet 5 introductory input and output rates, effective 2026-07-02", "confidence": "calculated", "source": "Culpa price book row for anthropic/claude-sonnet-5, $0.002 and $0.010 per 1k, effective 2026-07-02, verified against Anthropic pricing." }, { "value": "$3.00 / $15.00 per million", "claim": "Claude Sonnet 5 standard rates, effective 2026-09-01", "confidence": "calculated", "source": "Second price book row for anthropic/claude-sonnet-5, $0.003 and $0.015 per 1k, effective 2026-09-01." }, { "value": "50%", "claim": "rate increase on both input and output at the 2026-09-01 boundary", "confidence": "calculated", "source": "($3.00 - $2.00) / $2.00, and ($15.00 - $10.00) / $10.00, from the two dated rows above." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/claude-opus-4-8-pricing", "url": "https://getculpa.com/claude-opus-4-8-pricing", "textUrl": "https://getculpa.com/claude-opus-4-8-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "claude opus 4.8 pricing", "title": "Claude Opus 4.8 Pricing: $5 In, $25 Out, Same as 4.5 to Opus 5", "answer": "Claude Opus 4.8 pricing is $5.00 per million input tokens and $25.00 per million output tokens, and Opus 4.5, 4.6, 4.7 and the newer Opus 5 all bill identically. So an Opus version pin saves nothing at all. Culpa, a local-first LLM cost, margin, and forecast ledger, records the model version on every call, so a stale pin shows up as a fact.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$5.00 / $25.00 per million", "claim": "Claude Opus 4.5, 4.6, 4.7, 4.8 and Opus 5 input and output rates, identical across all five", "confidence": "calculated", "source": "Culpa price book rows for anthropic/claude-opus-4-5 through 4-8 and claude-opus-5, all $0.005 and $0.025 per 1k, verified against Anthropic pricing on 2026-08-01." }, { "value": "$6.25 per million", "claim": "Opus five-minute cache write rate, above the input rate", "confidence": "calculated", "source": "$0.005 per 1k input x 1.25, the published Anthropic cache-write multiplier, mirrored from the ledger's own derivation in migration 0020." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/claude-haiku-4-5-pricing", "url": "https://getculpa.com/claude-haiku-4-5-pricing", "textUrl": "https://getculpa.com/claude-haiku-4-5-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "claude haiku 4.5 pricing", "title": "Claude Haiku 4.5 Pricing: $1 In, $5 Out per Million Tokens", "answer": "Claude Haiku 4.5 pricing is $1.00 per million input tokens and $5.00 per million output tokens, with cached reads at $0.10 per million. It runs at call volumes where a small per-call regression becomes a large monthly number. Culpa, a local-first LLM cost, margin, and forecast ledger, meters every call so a high-volume tier can't drift unnoticed.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$1.00 / $5.00 per million", "claim": "Claude Haiku 4.5 input and output rates", "confidence": "calculated", "source": "Culpa price book row for anthropic/claude-haiku-4-5, $0.001 and $0.005 per 1k, effective 2026-07-02, verified against Anthropic pricing." }, { "value": "10x", "claim": "cost of uncached input against the cached rate on Haiku 4.5", "confidence": "calculated", "source": "$1.00 input divided by the $0.10 cached rate, from the same price-book row." }, { "value": "$1,520 to $4,400", "claim": "modelled monthly cost of a one-million-call classification step, cached versus uncached", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at real Haiku 4.5 rates. A range because call volume and prompt size are modelled." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/gemini-2-5-flash-pricing", "url": "https://getculpa.com/gemini-2-5-flash-pricing", "textUrl": "https://getculpa.com/gemini-2-5-flash-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gemini 2.5 flash pricing", "title": "Gemini 2.5 Flash Pricing: $0.30 In, $2.50 Out per Million", "answer": "Gemini 2.5 Flash pricing is $0.30 per million input tokens and $2.50 per million output tokens, a spread of roughly eight to one. That makes answer length, not prompt length, the lever that moves your bill. Culpa, a local-first LLM cost, margin, and forecast ledger, splits input and output per call so you can see which side is growing.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$0.30 / $2.50 per million", "claim": "Gemini 2.5 Flash input and output rates", "confidence": "calculated", "source": "Culpa price book row for gemini/gemini-2.5-flash, $0.0003 and $0.0025 per 1k, effective 2026-07-02, verified against Gemini API pricing." }, { "value": "8.3x", "claim": "output rate as a multiple of the input rate on Gemini 2.5 Flash", "confidence": "calculated", "source": "$2.50 divided by $0.30, from the same price-book row." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/gemini-2-5-pro-pricing", "url": "https://getculpa.com/gemini-2-5-pro-pricing", "textUrl": "https://getculpa.com/gemini-2-5-pro-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gemini 2.5 pro pricing", "title": "Gemini 2.5 Pro Pricing: $1.25 In, $10.00 Out per Million", "answer": "Gemini 2.5 Pro pricing is $1.25 per million input tokens and $10.00 per million output tokens, an eight-to-one spread. Cheap input makes long-context analysis viable, and expensive output punishes narration. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call from that rate and attributes it to the document run that spent it.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$1.25 / $10.00 per million", "claim": "Gemini 2.5 Pro input and output rates", "confidence": "calculated", "source": "Culpa price book row for gemini/gemini-2.5-pro, $0.00125 and $0.010 per 1k, effective 2026-07-02, verified against Gemini API pricing." }, { "value": "$260 to $460", "claim": "modelled cost of one 4,000-document batch run, at halved versus full context", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at real Gemini 2.5 Pro rates. A range because document count and context size are modelled." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/openai-api-cost", "url": "https://getculpa.com/openai-api-cost", "textUrl": "https://getculpa.com/openai-api-cost/llms.txt", "pageType": "provider_cost", "intent": "EVALUATE", "primaryKeyword": "openai api cost", "title": "OpenAI API Cost: A 150x Spread From Nano to Pro", "answer": "OpenAI API cost runs from $0.20 per million input tokens on nano to $30.00 on the Pro tier, a 150-fold spread, while every model bills output at roughly six times input. Culpa, a local-first LLM cost, margin, and forecast ledger, records which model served each call, so a drift up that ladder shows as a number rather than a surprise.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "150x", "claim": "spread between the cheapest and dearest OpenAI input rates", "confidence": "calculated", "source": "$30.00 per million on gpt-5.4-pro divided by $0.20 on gpt-5.4-nano, from the price book, effective 2026-07-02." }, { "value": "$0.45 to $66.00", "claim": "cost of one million input and 200,000 output tokens across the OpenAI line", "confidence": "calculated", "source": "Five tier calculations shown in the teardown, all from price-book rows effective 2026-07-02." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/anthropic-api-cost", "url": "https://getculpa.com/anthropic-api-cost", "textUrl": "https://getculpa.com/anthropic-api-cost/llms.txt", "pageType": "provider_cost", "intent": "EVALUATE", "primaryKeyword": "anthropic api cost", "title": "Anthropic API Cost: Every Model Bills Output at Exactly 5x", "answer": "Anthropic API cost runs from $1.00 per million input tokens on Haiku to $10.00 on Fable, and every single model bills output at exactly five times input. Anthropic is also the only provider here charging to write a cache. Culpa, a local-first LLM cost, margin, and forecast ledger, meters cached, uncached and cache-write tokens separately per call.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "5.0x", "claim": "output-to-input ratio, identical on all ten Anthropic models", "confidence": "calculated", "source": "Output divided by input on every anthropic row in the price book, effective 2026-07-02. All ten return exactly 5." }, { "value": "1 read", "claim": "break-even point before a five-minute cache write pays for itself on Sonnet 5", "confidence": "calculated", "source": "Incremental write cost $0.0025 divided by the $0.009 per-read saving = 0.28, so the first read clears it. Real Sonnet 5 introductory rates. Matches Anthropic's published statement that a five-minute cache pays off after one read and a one-hour cache after two." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/gemini-api-cost", "url": "https://getculpa.com/gemini-api-cost", "textUrl": "https://getculpa.com/gemini-api-cost/llms.txt", "pageType": "provider_cost", "intent": "EVALUATE", "primaryKeyword": "gemini api cost", "title": "Gemini API Cost: The Widest Output Spreads, Up to 8.3x", "answer": "Gemini API cost runs from $0.25 per million input tokens on Flash Lite to $2.00 on the Pro preview, with output spreads reaching 8.3 times input. That makes answer length the dominant lever here. Culpa, a local-first LLM cost, margin, and forecast ledger, splits input and output per call, so you can see which side is actually growing.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "8.3x", "claim": "widest output-to-input ratio across the Gemini line, on 2.5 Flash", "confidence": "calculated", "source": "$2.50 output divided by $0.30 input per million on gemini-2.5-flash, from the price book, effective 2026-07-02." }, { "value": "$0.55 to $4.40", "claim": "cost of one million input and 200,000 output tokens across the five Gemini models", "confidence": "calculated", "source": "Five calculations shown in the teardown, all from price-book rows effective 2026-07-02." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/groq-api-cost", "url": "https://getculpa.com/groq-api-cost", "textUrl": "https://getculpa.com/groq-api-cost/llms.txt", "pageType": "provider_cost", "intent": "EVALUATE", "primaryKeyword": "groq api cost", "title": "Groq API Cost: Where Trimming Your Output Barely Helps", "answer": "Groq API cost runs from $0.05 per million input tokens to $1.00, and output ratios go as flat as 1.34 times input on Llama 3.3 70B. That flatness breaks the usual advice to trim the answer first. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call from the model's own rates rather than a rule of thumb.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "1.34x", "claim": "flattest output-to-input ratio in this price book, on Llama 3.3 70B via Groq", "confidence": "calculated", "source": "$0.79 output divided by $0.59 input per million, from the price book, effective 2026-07-02." }, { "value": "20x", "claim": "spread between the cheapest and dearest Groq input rates", "confidence": "calculated", "source": "$1.00 per million on kimi-k2 divided by $0.05 on llama-3.1-8b-instant, from the price book." } ], "sources": [ { "label": "Groq pricing", "url": "https://groq.com/pricing" } ] }, { "slug": "/openrouter-api-cost", "url": "https://getculpa.com/openrouter-api-cost", "textUrl": "https://getculpa.com/openrouter-api-cost/llms.txt", "pageType": "provider_cost", "intent": "EVALUATE", "primaryKeyword": "openrouter api cost", "title": "OpenRouter API Cost: Same Headline Rate, No Cached Discount", "answer": "OpenRouter re-lists Anthropic models at identical headline rates, $5.00 and $25.00 per million on Opus 4.7, but records no cached rate where the direct route offers one at $0.50. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call by the route that served it, so the difference stops being invisible.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$5.00 / $25.00 per million", "claim": "OpenRouter Opus 4.7 rates, identical to the direct Anthropic headline", "confidence": "calculated", "source": "Price book row for openrouter/anthropic/claude-opus-4.7, $0.005 and $0.025 per 1k, effective 2026-07-02, matching the anthropic/claude-opus-4-7 row." }, { "value": "$2,100 to $7,500", "claim": "modelled monthly cost of a repeated-prompt workload, direct with caching versus routed without", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic using real Opus 4.7 rates on both routes. A range because call volume and hit rate are modelled." } ], "sources": [ { "label": "OpenRouter models", "url": "https://openrouter.ai/models" }, { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/gpt-5-6-terra-pricing", "url": "https://getculpa.com/gpt-5-6-terra-pricing", "textUrl": "https://getculpa.com/gpt-5-6-terra-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gpt-5.6 terra pricing", "title": "GPT-5.6 Terra Pricing: $2.00 In, $12.00 Out per Million", "answer": "GPT-5.6 Terra pricing is $2.00 per million input tokens and $12.00 per million output tokens, which undercuts GPT-5.4 on both sides at $2.50 and $15.00. Output bills six times input, the usual OpenAI shape. Culpa, a local-first LLM cost, margin, and forecast ledger, records the model on every call, so a migration lands as a measured delta.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-08-01", "costFigures": [ { "value": "$2.00 / $12.00 per million", "claim": "GPT-5.6 Terra input and output rates", "confidence": "calculated", "source": "Culpa price book row for openai/gpt-5.6-terra, $0.002 and $0.012 per 1k, read off the OpenAI pricing page on 2026-08-01. Pending seed into the ledger migrations." }, { "value": "20%", "claim": "Terra discount against GPT-5.4 on both input and output", "confidence": "calculated", "source": "($2.50 - $2.00) / $2.50 and ($15.00 - $12.00) / $15.00, from the two price-book rows." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/gpt-5-6-luna-pricing", "url": "https://getculpa.com/gpt-5-6-luna-pricing", "textUrl": "https://getculpa.com/gpt-5-6-luna-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "gpt-5.6 luna pricing", "title": "GPT-5.6 Luna Pricing: $0.20 In, $1.20 Out per Million", "answer": "GPT-5.6 Luna pricing is $0.20 per million input tokens and $1.20 per million output tokens, the cheapest OpenAI tier and fractionally under GPT-5.4 nano on output. At this rate call volume decides your bill rather than the rate. Culpa, a local-first LLM cost, margin, and forecast ledger, meters every call so a cheap tier can't hide behind its price.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-08-01", "costFigures": [ { "value": "$0.20 / $1.20 per million", "claim": "GPT-5.6 Luna input and output rates", "confidence": "calculated", "source": "Culpa price book row for openai/gpt-5.6-luna, $0.0002 and $0.0012 per 1k, read off the OpenAI pricing page on 2026-08-01. Pending seed into the ledger migrations." }, { "value": "$600 to $1,400", "claim": "modelled monthly input cost at ten million calls, trimmed prompt versus untrimmed", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at the real Luna input rate. A range because call volume and prompt size are modelled." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/claude-fable-5-pricing", "url": "https://getculpa.com/claude-fable-5-pricing", "textUrl": "https://getculpa.com/claude-fable-5-pricing/llms.txt", "pageType": "model_pricing", "intent": "PRICE", "primaryKeyword": "claude fable 5 pricing", "title": "Claude Fable 5 Pricing: $10 In, $50 Out, Same as Mythos 5", "answer": "Claude Fable 5 pricing is $10.00 per million input tokens and $50.00 per million output tokens, and Claude Mythos 5 bills identically at the same rate point. Cache writes cost $12.50, above the input rate. Culpa, a local-first LLM cost, margin, and forecast ledger, meters cached, uncached and cache-write tokens separately per call.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$10.00 / $50.00 per million", "claim": "Claude Fable 5 and Claude Mythos 5 input and output rates, identical on both", "confidence": "calculated", "source": "Culpa price book rows for anthropic/claude-fable-5 and anthropic/claude-mythos-5, both $0.010 and $0.050 per 1k, verified against Anthropic pricing on 2026-08-01." }, { "value": "$12.50 per million", "claim": "Fable 5 five-minute cache write rate, above the input rate", "confidence": "calculated", "source": "$0.010 per 1k input x 1.25, the published Anthropic cache-write multiplier, shown on the pricing page as $12.50 per MTok." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/how-to-price-an-ai-saas-product", "url": "https://getculpa.com/how-to-price-an-ai-saas-product", "textUrl": "https://getculpa.com/how-to-price-an-ai-saas-product/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "how to price an ai saas product", "title": "How to Price an AI SaaS Product Without Losing Money", "answer": "Price an AI SaaS product from your 90th-percentile customer cost rather than the average, then cap the tail beyond it. Usage is long-tailed, so an average-priced plan loses money on the heaviest accounts. Culpa, a local-first LLM cost, margin, and forecast ledger, tags spend to a customer and reconciles it against revenue you type in or import.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$12.00 to $260.00", "claim": "modelled monthly model cost from median to heaviest account on identical usage-based pricing", "confidence": "estimated", "source": "Both endpoints computed at real Claude Sonnet 5 introductory rates per 1k tokens from the price book, effective 2026-07-02. A range because the usage distribution is modelled." }, { "value": "$58.00", "claim": "modelled 90th-percentile monthly customer cost, the figure a plan price should clear", "confidence": "calculated", "source": "(14 x $2.00) + (3 x $10.00) per million, using real Claude Sonnet 5 introductory rates. Token volumes are modelled." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/ai-cogs-calculator", "url": "https://getculpa.com/ai-cogs-calculator", "textUrl": "https://getculpa.com/ai-cogs-calculator/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "ai cogs calculator", "title": "AI COGS Calculator: What Serving One Customer Really Costs", "answer": "AI cost of goods sold is the model spend required to serve a paying customer, including retries and discarded output, excluding research and evaluation runs. Most teams under-count it by leaving waste out. Culpa, a local-first LLM cost, margin, and forecast ledger, meters every call including failures, so your COGS reflects spend rather than intent.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$700 to $880", "claim": "modelled customer COGS range, naive count versus corrected, against a $1,000 month", "confidence": "estimated", "source": "Modelled split of a $1,000 month priced at real GPT-5.4 rates from the price book, effective 2026-07-02. Published as a scenario because no customer data backs it." }, { "value": "$140 to $320", "claim": "modelled misattribution range hidden by two offsetting errors, one leg alone versus both", "confidence": "estimated", "source": "The $180 excluded retries plus the $140 wrongly included eval spend, from the teardown arithmetic." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/ai-usage-cap-calculator", "url": "https://getculpa.com/ai-usage-cap-calculator", "textUrl": "https://getculpa.com/ai-usage-cap-calculator/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "ai usage cap calculator", "title": "AI Usage Cap Calculator: Where to Set the Ceiling", "answer": "Set a usage cap from your 90th-percentile account so it binds the tail and almost nobody else. A cap set at the average binds half your base and protects against nothing. Culpa, a local-first LLM cost, margin, and forecast ledger, reports the spend distribution per customer, so the ceiling comes from measurement rather than instinct.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$3.20 to $160.00", "claim": "modelled monthly spend from median to heaviest account across 500 accounts", "confidence": "estimated", "source": "Both endpoints at real Gemini 2.5 Flash rates per 1k tokens from the price book, effective 2026-07-02. A range because the distribution is modelled." }, { "value": "$21.50", "claim": "modelled 90th-percentile monthly account spend, the level a cap should sit at", "confidence": "calculated", "source": "(30 x $0.30) + (5 x $2.50) per million, using real Gemini 2.5 Flash rates. Token volumes are modelled." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/price-an-unlimited-ai-plan", "url": "https://getculpa.com/price-an-unlimited-ai-plan", "textUrl": "https://getculpa.com/price-an-unlimited-ai-plan/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "price an unlimited ai plan", "title": "How to Price an Unlimited AI Plan Without Going Broke", "answer": "An unlimited AI plan prices a promise against a maximum nobody has computed, and on a long-tailed usage curve that maximum keeps moving. A generous included allowance with a stated overage reads nearly as well and bounds the risk. Culpa, a local-first LLM cost, margin, and forecast ledger, reports the spend distribution so the tail is a number rather than a fear.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$40 to $850", "claim": "modelled monthly cost range on a single $200 unlimited tier", "confidence": "estimated", "source": "Both endpoints at real Claude Haiku 4.5 rates per 1k tokens from the price book, effective 2026-07-02. A range because the usage profiles are modelled." }, { "value": "-325% to 80%", "claim": "modelled gross margin spread from typical to extreme account on a $200 unlimited tier", "confidence": "estimated", "source": "($200 - $850) / $200, from the teardown arithmetic at real Claude Haiku 4.5 rates. Modelled usage, so published as a scenario." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/ai-saas-gross-margin-calculator", "url": "https://getculpa.com/ai-saas-gross-margin-calculator", "textUrl": "https://getculpa.com/ai-saas-gross-margin-calculator/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "ai saas gross margin calculator", "title": "AI SaaS Gross Margin Calculator: The Four Inputs It Needs", "answer": "An AI SaaS gross margin calculator needs four inputs: revenue for the period, model spend, the share of that spend serving customers, and infrastructure that scales with usage. Most teams have the first and guess the rest. Culpa, a local-first LLM cost, margin, and forecast ledger, reconciles metered spend against revenue you import, so the middle two stop being estimates.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "27.9% to 79.3%", "claim": "modelled gross margin spread across three plan tiers in the same month", "confidence": "estimated", "source": "Each tier computed at real GPT-5.4 rates per million from the price book, effective 2026-07-02. A range because customer counts and token volumes are modelled." }, { "value": "$10,905", "claim": "modelled total monthly model spend across all three tiers", "confidence": "calculated", "source": "$2,400 + $4,185 + $4,320 from the teardown arithmetic at real GPT-5.4 rates." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/price-a-credit-based-ai-plan", "url": "https://getculpa.com/price-a-credit-based-ai-plan", "textUrl": "https://getculpa.com/price-a-credit-based-ai-plan/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "price a credit based ai plan", "title": "How to Price a Credit-Based AI Plan So a Credit Covers Cost", "answer": "A credit-based AI plan works only when one credit maps to a bounded amount of model spend. Teams usually set the credit price from what feels saleable, then let expensive actions cost the same credit as cheap ones. Culpa, a local-first LLM cost, margin, and forecast ledger, meters the real spend behind each action, so a credit gets priced from measurement.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.008 to $0.522", "claim": "modelled model spend behind a single credit, cheapest action against dearest", "confidence": "estimated", "source": "Both endpoints at real Claude Sonnet 5 introductory rates per 1k tokens from the price book, effective 2026-07-02. A range because the action token volumes are modelled." }, { "value": "$0.049", "claim": "revenue per credit on a modelled $49 plan carrying 1,000 credits", "confidence": "calculated", "source": "$49 divided by 1,000 credits. The plan shape is modelled, the rates behind each action are real." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/price-a-per-seat-ai-product", "url": "https://getculpa.com/price-a-per-seat-ai-product", "textUrl": "https://getculpa.com/price-a-per-seat-ai-product/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "price a per seat ai product", "title": "How to Price a Per-Seat AI Product When Seats Differ", "answer": "Per-seat pricing decouples revenue from usage, which is comfortable until seats stop looking alike. On AI products an active seat can cost hundreds of times a dormant one, so the seats you bill and the seats you serve drift apart. Culpa, a local-first LLM cost, margin, and forecast ledger, attributes spend to each seat, so the drift becomes a number instead of a surprise.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.162 to $63.60", "claim": "modelled monthly model spend for one seat, dormant against power user, on a $40 seat price", "confidence": "estimated", "source": "Both endpoints at real Gemini 3.5 Flash rates per 1k tokens from the price book, effective 2026-07-02. A range because the seat mix and token volumes are modelled." }, { "value": "86.3%", "claim": "modelled blended gross margin across 1,000 seats, of which 380 are dormant", "confidence": "calculated", "source": "($40,000 minus $5,464.56) divided by $40,000, from the teardown arithmetic at real Gemini 3.5 Flash rates." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/price-a-usage-based-ai-product", "url": "https://getculpa.com/price-a-usage-based-ai-product", "textUrl": "https://getculpa.com/price-a-usage-based-ai-product/llms.txt", "pageType": "margin", "intent": "EVALUATE", "primaryKeyword": "price a usage based ai product", "title": "How to Price a Usage-Based AI Product on a Real Markup", "answer": "Usage-based pricing passes model cost through to the customer, so the only real decision is the markup and what it covers. A markup set on accepted spend under-recovers, because retries, evaluation traffic and calls that produced nothing you kept all bill too. Culpa, a local-first LLM cost, margin, and forecast ledger, counts every billed call, so the markup sits on real spend.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$57.00 to $71.18", "claim": "modelled monthly provider spend for one account, accepted work alone against everything billed", "confidence": "estimated", "source": "Both endpoints at real GPT-5.4 mini rates per million from the price book, effective 2026-07-02. A range because the waste and evaluation shares are modelled." }, { "value": "37.6%", "claim": "modelled gross margin when a 2x markup is applied to accepted spend rather than billed spend", "confidence": "calculated", "source": "($114.00 minus $71.18) divided by $114.00, from the teardown arithmetic at real GPT-5.4 mini rates." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/what-is-cost-per-customer", "url": "https://getculpa.com/what-is-cost-per-customer", "textUrl": "https://getculpa.com/what-is-cost-per-customer/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per customer", "title": "What Is Cost per Customer? Why It Isn't Cost per User", "answer": "Cost per customer is model spend attributed to a paying account rather than to one person inside it. In B2B a single customer can hold dozens of users, so cost per user hides the number your contract actually has to cover. Culpa, a local-first LLM cost, margin, and forecast ledger, tags each call to an account and rolls seat activity up to the customer paying the invoice.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$2.85", "claim": "modelled monthly model cost for one active seat on GPT-5.4 mini", "confidence": "calculated", "source": "(2 x $0.75) + (0.3 x $4.50) per million, using real GPT-5.4 mini rates from the price book, effective 2026-07-02. Per-seat token volumes are modelled." }, { "value": "$11.40 to $131.10", "claim": "modelled monthly cost of one account as it grew from 4 active seats to 46 on an unchanged contract", "confidence": "estimated", "source": "Both endpoints from the teardown arithmetic at real GPT-5.4 mini rates. A range because the seat expansion curve is modelled." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/what-is-cost-per-agent", "url": "https://getculpa.com/what-is-cost-per-agent", "textUrl": "https://getculpa.com/what-is-cost-per-agent/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per agent", "title": "What Is Cost per Agent? The Whole Run, Not the First Call", "answer": "Cost per agent is the total model spend for one complete autonomous run, counting every sub-call, tool step and retry the agent made before it answered. One run isn't one call, it's a tree. Culpa, a local-first LLM cost, margin, and forecast ledger, traces each call back to the run that spawned it and prices the whole tree as a single unit.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$1.19", "claim": "modelled cost of one complete agent run across 18 calls on Claude Opus 4.8", "confidence": "calculated", "source": "Sum of the teardown arithmetic at real Claude Opus 4.8 rates per 1k tokens from the price book, effective 2026-07-02. The call tree and token volumes are modelled." }, { "value": "3%", "claim": "share of a modelled run's total cost carried by the opening planner call", "confidence": "calculated", "source": "$0.040 divided by $1.19, from the teardown arithmetic." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-cost-per-agent-step", "url": "https://getculpa.com/what-is-cost-per-agent-step", "textUrl": "https://getculpa.com/what-is-cost-per-agent-step/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per agent step", "title": "What Is Cost per Agent Step? Why Step 20 Costs More", "answer": "Cost per agent step is the model spend for one call inside an agent run, and it isn't constant across the run. Each step carries the transcript of the steps before it, so input grows as the agent works. Culpa, a local-first LLM cost, margin, and forecast ledger, prices every step individually and shows where in a run the money actually goes.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0035 to $0.0206", "claim": "modelled cost of a single agent step, first step against twentieth, on Claude Haiku 4.5", "confidence": "estimated", "source": "Both endpoints at real Claude Haiku 4.5 rates per 1k tokens from the price book, effective 2026-07-02. A range because the per-step token volumes are modelled." }, { "value": "$0.241", "claim": "modelled total cost of a 20-step run before any step-budget increase", "confidence": "calculated", "source": "211k input tokens x $0.001 plus 6k output tokens x $0.005 per 1k, from the teardown arithmetic." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/what-is-cost-per-prompt-version", "url": "https://getculpa.com/what-is-cost-per-prompt-version", "textUrl": "https://getculpa.com/what-is-cost-per-prompt-version/llms.txt", "pageType": "metric", "intent": "LEARN", "primaryKeyword": "cost per prompt version", "title": "What Is Cost per Prompt Version? Pricing Every Edit", "answer": "Cost per prompt version is model spend split by which revision of a prompt served each call. Without a version stamped on the call at request time, an edit and the cost change it caused can't be connected afterwards. Culpa, a local-first LLM cost, margin, and forecast ledger, names the prompt version behind every call, so an edit reads as a priced change rather than a mystery.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.00465 to $0.009075", "claim": "modelled cost per call across two versions of one prompt on Gemini 2.5 Pro", "confidence": "estimated", "source": "Both endpoints at real Gemini 2.5 Pro rates per 1k tokens from the price book, effective 2026-07-02. A range because the token counts on each version are modelled." }, { "value": "$3,983", "claim": "modelled monthly increase produced by a single prompt edit at 900,000 calls", "confidence": "calculated", "source": "(900,000 x $0.009075) minus (900,000 x $0.00465), using real Gemini 2.5 Pro rates per 1k tokens. Call volume and token counts are modelled." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/anthropic-bill-spike", "url": "https://getculpa.com/anthropic-bill-spike", "textUrl": "https://getculpa.com/anthropic-bill-spike/llms.txt", "pageType": "problem", "intent": "AUDIT", "primaryKeyword": "anthropic bill spike", "title": "Anthropic Bill Spike: Cache Writes Are Usually the Cause", "answer": "An Anthropic bill spike on flat traffic usually traces to prompt caching, because Anthropic is the only provider that charges to write a cache. A five-minute write bills at 1.25 times the input rate, so a prefix you rebuild on every call costs 25% more than never caching. Culpa, a local-first LLM cost, margin, and forecast ledger, prices cache writes, cache reads and plain input as three separate things.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.4875 to $3.9375", "claim": "modelled cost of 21 calls over one 30k prefix, cached well against cached badly on Claude Opus 4.8", "confidence": "estimated", "source": "Both endpoints at real Claude Opus 4.8 input, cached-read and five-minute cache-write rates from the price book, effective 2026-07-02. A range because the traffic pattern is modelled." }, { "value": "25%", "claim": "premium paid over not caching at all when every call rewrites the cache", "confidence": "calculated", "source": "The five-minute cache-write rate of $0.00625 per 1k divided by the $0.005 input rate, which is the published 1.25x multiplier." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/gemini-bill-spike", "url": "https://getculpa.com/gemini-bill-spike", "textUrl": "https://getculpa.com/gemini-bill-spike/llms.txt", "pageType": "problem", "intent": "AUDIT", "primaryKeyword": "gemini bill spike", "title": "Gemini Bill Spike: The 200k Context Tier Nobody Priced", "answer": "A Gemini bill spike with no traffic change often means prompts crossed 200,000 tokens, where Google charges a long-context rate at roughly double. Crossing that line changes your rate as well as your volume, so a modest prompt increase lands as a large bill increase. Culpa, a local-first LLM cost, margin, and forecast ledger, prices every call from a dated price book and names the feature behind the change.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.245", "claim": "modelled cost of one 180k-token call on Gemini 2.5 Pro below the long-context boundary", "confidence": "calculated", "source": "(180 x $0.00125) + (2 x $0.010) per 1k, using real Gemini 2.5 Pro rates from the price book, effective 2026-07-02. Token volumes are modelled." }, { "value": "$0.245 to $0.630", "claim": "modelled cost per call either side of the 200,000-token tier boundary", "confidence": "estimated", "source": "The upper endpoint uses the above-200k tier listed on the Gemini pricing page, fetched 2026-08-01, which the price book deliberately doesn't hold. A range because token volumes are modelled." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/openrouter-bill-spike", "url": "https://getculpa.com/openrouter-bill-spike", "textUrl": "https://getculpa.com/openrouter-bill-spike/llms.txt", "pageType": "problem", "intent": "AUDIT", "primaryKeyword": "openrouter bill spike", "title": "OpenRouter Bill Spike: The Cached Rate You Stopped Getting", "answer": "An OpenRouter bill spike on a repeated-prompt workload usually means you lost the cached input rate. No OpenRouter row in Culpa's price book carries a cached rate, so a prefix that re-read at a tenth of the price on a direct route now bills in full. Culpa, a local-first LLM cost, margin, and forecast ledger, prices cached and uncached input separately, so the routing decision shows its real cost.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.027 to $0.135", "claim": "modelled cost per call for the same repeated-prompt workload, direct route against routed", "confidence": "estimated", "source": "Both endpoints from real Claude Opus 4.7 rates per 1k tokens from the price book, effective 2026-07-02. The OpenRouter row carries no cached rate. A range because the prefix size and call volume are modelled." }, { "value": "5.0x", "claim": "modelled cost multiple from losing the cached input rate at an identical headline rate", "confidence": "calculated", "source": "$0.135 divided by $0.027, from the teardown arithmetic." } ], "sources": [ { "label": "OpenRouter models", "url": "https://openrouter.ai/models" }, { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/failed-call-cost", "url": "https://getculpa.com/failed-call-cost", "textUrl": "https://getculpa.com/failed-call-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "failed call cost", "title": "Failed Call Cost: You Pay for Output You Never Read", "answer": "A failed call still bills for every token the model generated before your code gave up. Timeouts, cancelled streams, truncated structured output and guardrail rejections all produce tokens you pay for and never read. Culpa, a local-first LLM cost, margin, and forecast ledger, meters failed calls alongside successful ones, so the cost of failure stops hiding inside the gap between your numbers and the invoice.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.072", "claim": "modelled cost of one abandoned call on GPT-5.5 that delivered nothing", "confidence": "calculated", "source": "(6 x $0.005) + (1.4 x $0.030) per 1k, using real GPT-5.5 rates from the price book, effective 2026-07-02. Token counts and the abandonment point are modelled." }, { "value": "86%", "claim": "share of a successful call's cost still paid when the call is abandoned near the end", "confidence": "calculated", "source": "$0.072 divided by $0.084, from the teardown arithmetic." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/output-token-creep", "url": "https://getculpa.com/output-token-creep", "textUrl": "https://getculpa.com/output-token-creep/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "output token creep", "title": "Output Token Creep: The Rise Input Monitoring Can't See", "answer": "Output token creep is a rise in tokens generated per call while input stays flat, so any monitoring built around prompt size sees nothing at all. It matters because output bills five to eight times input on most models here. Culpa, a local-first LLM cost, margin, and forecast ledger, tracks input and output separately per feature, so the two curves stop being reported as one number.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.001525 to $0.00265", "claim": "modelled cost per call before and after an output-length increase on Gemini 2.5 Flash", "confidence": "estimated", "source": "Both endpoints at real Gemini 2.5 Flash rates per 1k tokens from the price book, effective 2026-07-02. A range because the output lengths are modelled." }, { "value": "8.33x", "claim": "output-to-input price ratio on Gemini 2.5 Flash, the widest in the price book", "confidence": "calculated", "source": "$0.0025 output divided by $0.0003 input per 1k, from the price book, effective 2026-07-02." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] }, { "slug": "/input-token-creep", "url": "https://getculpa.com/input-token-creep", "textUrl": "https://getculpa.com/input-token-creep/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "input token creep", "title": "Input Token Creep: Four Percent a Month Is 60% a Year", "answer": "Input token creep is a small monthly rise in average input tokens per call across your whole workload, with no single change large enough to notice. Four percent a month compounds to about 60% in a year. Culpa, a local-first LLM cost, margin, and forecast ledger, tracks tokens per call as a series, so drift reads as a trend rather than as a surprise.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$10,000 to $16,010", "claim": "modelled monthly input spend at the start and end of a year of 4% drift, at flat call volume", "confidence": "estimated", "source": "Both endpoints at the real Claude Haiku 4.5 input rate of $0.001 per 1k from the price book, effective 2026-07-02. A range because the drift rate and volumes are modelled." }, { "value": "60%", "claim": "annual input cost increase produced by 4% monthly drift with no traffic growth", "confidence": "calculated", "source": "1.04 to the power of 12 equals 1.601, from the teardown arithmetic." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/power-user-ai-cost", "url": "https://getculpa.com/power-user-ai-cost", "textUrl": "https://getculpa.com/power-user-ai-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "power user ai cost", "title": "Power User AI Cost: Why Your Heaviest Account Is Heavy", "answer": "Power user AI cost is what your heaviest accounts spend, and the useful question isn't how much but why. Most heavy accounts are heavy because of one workflow rather than general enthusiasm. Culpa, a local-first LLM cost, margin, and forecast ledger, attributes spend to the feature inside the account, so the answer names a workflow you can fix rather than a customer you'd rather not lose.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$240.00", "claim": "modelled monthly model spend for one heavy account across three workloads on GPT-5.4", "confidence": "calculated", "source": "$105.00 + $54.00 + $81.00 from the teardown arithmetic at real GPT-5.4 rates per 1k tokens from the price book, effective 2026-07-02. Workload volumes are modelled." }, { "value": "$86.10", "claim": "modelled share of that account's month spent re-processing documents that hadn't changed", "confidence": "calculated", "source": "82% of the $105.00 bulk re-run, from the teardown arithmetic at real GPT-5.4 rates. The unchanged share is modelled." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/free-tier-abuse", "url": "https://getculpa.com/free-tier-abuse", "textUrl": "https://getculpa.com/free-tier-abuse/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "free tier abuse", "title": "Free Tier Abuse: What One Automated Signup Really Costs", "answer": "Free tier abuse is scripted signups consuming a whole free allowance at the highest cost per call they can reach. A genuine trial user takes a fraction of what's granted, while a scripted one takes all of it with the longest prompts it can send. Culpa, a local-first LLM cost, margin, and forecast ledger, meters spend per signup, so the free tier carries a unit cost instead of a hope.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0495 to $4.95", "claim": "modelled cost of one free signup, genuine trial against scripted, on GPT-5.4 mini", "confidence": "estimated", "source": "Both endpoints at real GPT-5.4 mini rates per 1k tokens from the price book, effective 2026-07-02. A range because the usage profiles are modelled." }, { "value": "81%", "claim": "modelled share of free-tier spend produced by 4% of signups", "confidence": "calculated", "source": "$1,782.00 divided by $2,209.68, from the teardown arithmetic." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/prompt-version-cost-increase", "url": "https://getculpa.com/prompt-version-cost-increase", "textUrl": "https://getculpa.com/prompt-version-cost-increase/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "prompt version cost increase", "title": "Prompt Version Cost Increase: Finding the Edit Behind It", "answer": "A prompt version cost increase shows up as a step change in cost per call on one specific date, with call volume unchanged. Finding the edit behind it needs a prompt version recorded on each call, because an invoice can't be bisected after the fact. Culpa, a local-first LLM cost, margin, and forecast ledger, names the prompt version behind every call, so the step resolves to a diff.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0038182 to $0.0058636", "claim": "modelled cost per call either side of a single-day step change on GPT-5.4", "confidence": "estimated", "source": "Daily spend divided by daily calls, at real GPT-5.4 rates per 1k tokens from the price book, effective 2026-07-02. A range because the daily figures are modelled." }, { "value": "818 tokens", "claim": "input tokens added per call, derived from the observed per-call cost delta", "confidence": "calculated", "source": "$0.0020455 divided by the $0.0025 per 1k input rate, with output length held constant." } ], "sources": [ { "label": "OpenAI API pricing", "url": "https://developers.openai.com/api/docs/pricing" } ] }, { "slug": "/model-upgrade-cost-increase", "url": "https://getculpa.com/model-upgrade-cost-increase", "textUrl": "https://getculpa.com/model-upgrade-cost-increase/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "model upgrade cost increase", "title": "Model Upgrade Cost Increase: Same Workload, New Rate", "answer": "A model upgrade cost increase is the same workload priced at a new rate, and it happens whether or not you changed anything. Claude Sonnet 5's introductory pricing ends on 2026-09-01 and rises 50%. Culpa, a local-first LLM cost, margin, and forecast ledger, prices every call from a dated price book, so a rate change lands as a forecast rather than as an invoice.", "lastReviewed": "2026-08-01", "effectiveDate": "2026-07-02", "costFigures": [ { "value": "$3,200 to $4,800", "claim": "modelled monthly cost of one unchanged workload on Claude Sonnet 5, before and after 2026-09-01", "confidence": "estimated", "source": "Both endpoints use real Claude Sonnet 5 rows from the price book, $2.00 and $10.00 per million effective 2026-07-02 and $3.00 and $15.00 effective 2026-09-01. A range because the token volumes are modelled." }, { "value": "$1,600", "claim": "modelled monthly increase from the scheduled Sonnet 5 rate change, at unchanged volume", "confidence": "calculated", "source": "$4,800 minus $3,200, from the teardown arithmetic at real price book rates." } ], "sources": [ { "label": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/docs/about-claude/pricing" } ] }, { "slug": "/rag-context-cost", "url": "https://getculpa.com/rag-context-cost", "textUrl": "https://getculpa.com/rag-context-cost/llms.txt", "pageType": "problem", "intent": "FIX", "primaryKeyword": "rag context cost", "title": "RAG Context Cost: Top-K Is a Pricing Decision", "answer": "RAG context cost is the share of your bill created by retrieved chunks rather than by the question a user asked. Top-k multiplied by chunk size sets input tokens per call, so retrieval settings are a pricing decision made in a config file. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call and maps the input back to the feature that assembled it.", "lastReviewed": "2026-08-01", "effectiveDate": null, "costFigures": [ { "value": "$0.0102 to $0.0186", "claim": "modelled cost per call at top-k 5 against top-k 12 on Gemini 3.5 Flash", "confidence": "estimated", "source": "Both endpoints at real Gemini 3.5 Flash rates per 1k tokens from the price book, effective 2026-07-02. A range because chunk size and answer length are modelled." }, { "value": "93%", "claim": "share of input tokens that are retrieved context at top-k 12 with 800-token chunks", "confidence": "calculated", "source": "9,600 retrieved tokens divided by 10,300 total input tokens, from the teardown arithmetic." } ], "sources": [ { "label": "Gemini API pricing", "url": "https://ai.google.dev/gemini-api/docs/pricing" } ] } ] }