Quote fixed-bid AI-assisted work without bleeding margin on the back end. Plug in deliverable length, prompt context size, and revision rounds — get an honest per-engagement and per-month cost across GPT-5, Claude 4.6, Gemini 3, DeepSeek, and 25+ more models.
An independent consultant's cost structure used to be simple: hours billed minus a few SaaS subscriptions. Then 2025 happened, the work shifted from manual synthesis to AI-augmented synthesis, and a new line item showed up that nobody had seen before — variable token spend that scales with deliverable size, prompt context, and how many revision rounds the client requests. Most consultants are still pricing as if that line doesn't exist.
The math gets ugly fast. A 12-page market scan that pulls 30,000 tokens of research context, drafts 6,000 tokens of output, and goes through three rounds of partner review can cost $2 on the cheap end and $40 on the frontier-tier end. If you quoted that engagement at a flat $4,500 without modeling the tooling layer, the difference between provider choices is the difference between a 1% and a 9% cost-of-goods-sold hit. Across a year of engagements that's the size of a junior associate's salary.
This calculator exists to put the number on the page before you sign the SOW. Enter the realistic shape of your deliverable — context volume, output length, and revision count — and it cross-multiplies against published rates for every major hosted model. Output: a sortable cost table you can paste straight into a pricing memo, a fixed-bid worksheet, or a pass-through line in a client invoice.
Before you quote a flat fee, model the worst-case revision scenario. A research memo that goes through five client rounds instead of two can quintuple your AI cost while the headline price stays flat. The calculator's revision-pass slider exposes that risk in dollars, so you can either price it in or write the SOW to limit rounds.
If you bill API usage as a transparent pass-through, you need a number that's defensible to a procurement reviewer who knows the public rates. The calculator gives you the at-cost figure plus your margin in a clean breakdown. Drop it into the engagement letter and the procurement conversation gets much shorter.
Anthropic raises a tier? OpenAI ships a smaller, cheaper variant? Move your standard engagement template through the comparison view and see whether re-tuning prompts is worth the lift. Most consulting firms find that 70-85% of their AI cost lives in one polish call that has no business being on the frontier tier.
For monthly advisory retainers, the variable-cost line is the surprise. If you commit to "up to four memos a month" and the client routinely requests deeper research dumps, your token spend can drift 3-4x. The calculator's monthly view lets you size the retainer ceiling before you offer it.
Solo consultants and small firms often debate whether to keep AI synthesis in-house or subcontract it. Plug the same workload into both columns — your hosted model bill versus a sub's flat fee — and the answer is usually obvious in under a minute. It also tells you when a sub's quote is too cheap to be sustainable.
Three failure modes show up over and over in production AI-augmented advisory work:
The calculator surfaces all three by design — it asks for context size, output length, and revision count separately, then recomputes the bill against every supported provider. See OpenAI's pricing page for the canonical input/output split, Anthropic's Claude pricing for the latest Sonnet and Opus tier numbers, or the Consultancy.org coverage of how boutique firms are restructuring billing models around AI tooling.
To make the numbers concrete, here's how a typical "boutique strategy advisory engagement" lands when run through the calculator:
| Model | Cost / deliverable | Monthly bill |
|---|---|---|
| GPT-5 | $1.92 | $15.36 |
| Claude Sonnet 4.6 | $1.40 | $11.20 |
| Gemini 3 Flash | $0.18 | $1.44 |
| DeepSeek V3.1 | $0.11 | $0.88 |
| Mixed (Flash draft + Sonnet polish) | $0.46 | $3.68 |
Numbers above are illustrative. Plug your real engagement shape into the live tool to get a current comparison with the latest published rates. The "mixed" row is the pattern most successful boutique firms settle into — a cheap workhorse for first-pass synthesis, a smarter model for the final partner-quality pass.
An AI bill is one line on the engagement P&L. The other lines that matter — and where the TinyTools suite already covers most of them:
The pattern is the same across all of them: free, single-purpose, no signup. For a wider view of how independent consultants are restructuring fees around AI tooling in 2026, Harvard Business Review publishes consistent coverage of advisory pricing trends, and the Deloitte Insights archive tracks how the larger firms are passing AI cost through on engagements.
Yes — the comparison table is plain HTML, so you can copy it into a Notion proposal, a Google Doc SOW, or a Linear spec. Many independent consultants paste the per-deliverable number directly into the engagement letter as a transparent pass-through estimate.
Yes. GPT-5 mini, Claude Haiku 4.5, Gemini Flash, and DeepSeek's full lineup are all in the comparison. Mini tiers are usually 5-20x cheaper than the flagship and good enough for outlining, citation formatting, and bullet-conversion work.
The calculator reads from a price table that we update whenever a major provider publishes a change. Expect 1-3 day lag on smaller providers, near-real-time on the top five.
Self-hosted GPU pricing is too workload-dependent to model precisely, but we cover the major hosted serverless rates (Together, Fireworks, Groq, Bedrock) for Llama, Mistral, Qwen, and DeepSeek — those are a reasonable upper bound for what private deployment saves once you factor in ops overhead.