Tasks / Strategy

Price and package a product

Can the model price on the value each kind of customer gets, using real usage and willingness-to-pay data, and get existing customers there?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 16 graded outputs by 8 models. 56% were usable with at most a quick edit.

Reliably right

  1. Doesn't trust the price test100% pass
    Names the 59/41 split mismatch, holiday timing, and small sample; does not rest the decision on the test.
    Opus 5.5 · Claude · Unlimited pages, falling margins
  2. Plans the renewals100% pass
    Addresses each enterprise renewal with its date and note, including Copperfield in January, Northwind's tripled volume, and Arcadia's competitor evaluation.
    Opus 5.5 · Claude · Unlimited pages, falling margins
  3. Each tier for a different buyer94% pass
    Each tier names its buyer (startups, mid-market, enterprise) and is priced on distinct value: startups protected, mid-market overage on heavy users, enterprise per-page with unlimited seats.
    Opus 5.5 · Claude · Unlimited pages, falling margins

Where it slips

  1. Uses the supplied evidence correctly34% pass
    Total MRR is stated as $11,670 (actual $11,820), and median 'getting expensive' for small agencies is given as about $37 (actual $33) and for agencies as about $55 (actual $51).
    Haiku 5.5 · API · Fifteen dollars for everyone
  2. Gets the base of every number right41% pass
    MRR total and two segment‑level median getting‑expensive figures are incorrectly computed from the supplied data.
    Haiku 5.5 · API · Fifteen dollars for everyone
  3. Avoids unsupported claims52% pass
    Median getting‑expensive values and total MRR are presented as fact but are not supported by the data; these are facts about the current situation, not labelled as assumptions.
    Haiku 5.5 · API · Fifteen dollars for everyone

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Each tier for a different buyer

    Is each plan built for a distinct kind of buyer, with what that buyer values, rather than more-of-the-same at a higher price?

    Passes when Every tier names its buyer and the value it's priced on, and includes what that buyer needs and the lower tier's buyer doesn't.

  2. Priced on willingness to pay

    Are the proposed prices grounded in evidence of what each kind of customer would pay and the value they get, rather than in cost-plus, competitors alone or a round-number rise?

    Passes when Uses the willingness-to-pay and value evidence in the material to set each price, and says where the evidence is thin.

  3. Moves existing customers fairly

    Does the proposal say how existing customers move to the new pricing (who changes first, with what notice, and the value that justifies it)?

    Passes when A transition plan: new customers first or a set notice period for existing ones, tied to value delivered, with how churn will be watched.

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Gets the base of every number right · and 3 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the Head of Product at Parsewell. Our CFO, Tom Reyes, wants every price up 30% in January, seats and enterprise contracts alike, citing the price test below. Our CEO has asked you for the alternative: a new pricing model, what it does to each kind of customer and to gross margin, how we get there (including the enterprise renewals), and what you make of the price test. No more than 1,300 words, with a table of the model and its effect by segment. All of our accounts are in the usage export. The files are attached.

What the model was given7 items: About Parsewell, current_pricing.md, usage_last_30_days.csv (every account), price_test_readout.md, enterprise_contracts.md, competitor_pricing.md, cfo_note.md
About ParsewellAn API that extracts structured data from documents (invoices, claims, bills of lading). Developers integrate it; customers are charged as described in the pricing file.
current_pricing.md5 lines · Download
# Current pricing

- Self-serve: $99 per seat per month. A seat is a developer or admin with access to the dashboard. Unlimited pages.
- Enterprise: annual contracts, priced per seat at a negotiated discount. Unlimited pages.
- Cost to serve: about $0.0012 per page (model inference and storage), and falling about 10% a quarter.
usage_last_30_days.csv (every account)account,segment,seats,pages_last_30d,revenue_last_30d_usd,cost_to_serve_last_30d_usd,gross_margin_usd Northwind Logistics,Enterprise (contract),12,4820000,3200,5784,-2584 Halden Insurance,Enterprise (contract),9,3960000,2600,4752,-2152 Arcadia Health,Enterprise (contract),15,3310000,4500,3972,528 Brightwater Bank,Enterprise (contract),10,2740000,3000,3288,-288 Copperfield Freight,Enterprise (contract),8,2380000,2400,2856,-456 Meridian Claims,Enterprise (contract),11,2150000,3300,2580,720 Mid-market A,Mid-market (self-serve),5,166000,495,199,296 Mid-market B,Mid-market (self-serve),8,291000,792,349,443 Mid-market C,Mid-market (self-serve),7,301000,693,361,332 Mid-market D,Mid-market (self-serve),6,288000,594,346,248 Mid-market E,Mid-market (self-serve),5,254000,495,305,190 Mid-market F,Mid-market (self-serve),4,235000,396,282,114 Mid-market G,Mid-market (self-serve),4,159000,396,191,205 Mid-market H,Mid-market (self-serve),8,330000,792,396,396 Mid-market I,Mid-market (self-serve),5,366000,495,439,56 Mid-market J,Mid-market (self-serve),5,234000,495,281,214 Mid-market K,Mid-market (self-serve),7,433000,693,520,173 Mid-market L,Mid-market (self-serve),7,134000,693,161,532 Mid-market M,Mid-market (self-serve),6,222000,594,266,328 Mid-market N,Mid-market (self-serve),6,196000,594,235,359 Mid-market O,Mid-market (self-serve),8,428000,792,514,278 Mid-market P,Mid-market (self-serve),3,251000,297,301,-4 Mid-market Q,Mid-market (self-serve),6,544000,594,653,-59 Mid-market R,Mid-market (self-serve),6,392000,594,470,124 Mid-market S,Mid-market (self-serve),8,350000,792,420,372 Mid-market T,Mid-market (self-serve),8,553000,792,664,128 Mid-market U,Mid-market (self-serve),8,192000,792,230,562 Mid-market V,Mid-market (self-serve),3,346000,297,415,-118 Mid-market W,Mid-market (self-serve),4,157000,396,188,208 Mid-market X,Mid-market (self-serve),5,145000,495,174,321 Startup 1,Startup (self-serve),3,33000,297,40,257 Startup 2,Startup (self-serve),3,40000,297,48,249 Startup 3,Startup (self-serve),1,2000,99,2,97 Startup 4,Startup (self-serve),3,21000,297,25,272 Startup 5,Startup (self-serve),2,25000,198,30,168 Startup 6,Startup (self-serve),2,10000,198,12,186 Startup 7,Startup (self-serve),1,19000,99,23,76 Startup 8,Startup (self-serve),2,29000,198,35,163 Startup 9,Startup (self-serve),1,11000,99,13,86 Startup 10,Startup (self-serve),2,14000,198,17,181 Startup 11,Startup (self-serve),1,9000,99,11,88 Startup 12,Startup (self-serve),2,16000,198,19,179 Startup 13,Startup (self-serve),3,32000,297,38,259 Startup 14,Startup (self-serve),3,15000,297,18,279 Startup 15,Startup (self-serve),1,35000,99,42,57 Startup 16,Startup (self-serve),1,35000,99,42,57 Startup 17,Startup (self-serve),3,27000,297,32,265 Startup 18,Startup (self-serve),1,26000,99,31,68 Startup 19,Startup (self-serve),3,26000,297,31,266 Startup 20,Startup (self-serve),2,14000,198,17,181 Startup 21,Startup (self-serve),3,20000,297,24,273 Startup 22,Startup (self-serve),3,16000,297,19,278 Startup 23,Startup (self-serve),3,39000,297,47,250 Startup 24,Startup (self-serve),1,9000,99,11,88 Startup 25,Startup (self-serve),1,16000,99,19,80 Startup 26,Startup (self-serve),1,10000,99,12,87 Startup 27,Startup (self-serve),3,27000,297,32,265 Startup 28,Startup (self-serve),2,39000,198,47,151 Startup 29,Startup (self-serve),2,18000,198,22,176 Startup 30,Startup (self-serve),1,15000,99,18,81 Startup 31,Startup (self-serve),3,11000,297,13,284 Startup 32,Startup (self-serve),1,40000,99,48,51 Startup 33,Startup (self-serve),2,32000,198,38,160 Startup 34,Startup (self-serve),2,39000,198,47,151 Startup 35,Startup (self-serve),1,15000,99,18,81 Startup 36,Startup (self-serve),3,12000,297,14,283 Startup 37,Startup (self-serve),1,21000,99,25,74 Startup 38,Startup (self-serve),3,19000,297,23,274 Startup 39,Startup (self-serve),2,38000,198,46,152 Startup 40,Startup (self-serve),2,32000,198,38,160 Startup 41,Startup (self-serve),2,5000,198,6,192 Startup 42,Startup (self-serve),2,18000,198,22,176 Startup 43,Startup (self-serve),2,35000,198,42,156 Startup 44,Startup (self-serve),2,31000,198,37,161 Startup 45,Startup (self-serve),3,21000,297,25,272 Startup 46,Startup (self-serve),2,7000,198,8,190 Startup 47,Startup (self-serve),2,30000,198,36,162 Startup 48,Startup (self-serve),3,3000,297,4,293 Startup 49,Startup (self-serve),2,28000,198,34,164 Startup 50,Startup (self-serve),1,8000,99,10,89
price_test_readout.md10 lines · Download
# Price test readout (Growth team, January)

Test: new self-serve sign-ups saw $129 per seat (B) or $99 (A), 8 to 21 December. Intended split 50/50.

| Arm | Pricing page visitors | Started paying | Conversion |
|---|---|---|---|
| A ($99) | 1,240 | 62 | 5.0% |
…
enterprise_contracts.md10 lines · Download
# Enterprise contracts

| Account | Annual value | Renews | Note |
|---|---|---|---|
| Northwind Logistics | $38,400 | Feb 2027 | Pages up 3x in a year; wants a 2-year renewal "at the same rate" |
| Halden Insurance | $31,200 | Jun 2027 | |
| Arcadia Health | $54,000 | Mar 2027 | Evaluating a competitor |
…
competitor_pricing.md5 lines · Download
# Competitor pricing (public pricing pages, September)

- DocuLens: $0.012 per page up to 1M pages a month, $0.008 above that; $500 a month platform fee.
- ExtractIQ: $0.015 per page, volume discounts from 500k pages; no seat charges.
- Formwise: per seat ($79), page limits per seat (50k), overage $0.01 per page.
cfo_note.md1 lines · Download
From Tom (CFO): "Gross margin was 41% a year ago and it's 24% now. Let's put every price up 30% in January, seats and contracts, and use the price test as cover. Customers expect increases."
What a strong answer doesThe answer key the graders mark against

Shows that the problem is the value metric, not the price level. Six enterprise accounts process about 71% of all pages (19.4M of 27.4M) on seat-based contracts worth $19,000 a month, costing about $23,200 to serve: they lose about $4,200 a month, while startups pay for seats they barely use (margin about 87%). Overall gross margin is about 24% ($43,100 revenue against $32,900 cost). A 30% rise on seats barely touches the loss-making volume (enterprise contracts would rise only at renewal) and lands hardest on small self-serve accounts. Proposes a hybrid: a platform fee plus a per-page price with volume tiers, priced against competitors (about $0.008 to $0.015 a page) and well above cost ($0.0012), and works out its effect by segment. Handles the enterprise renewals one by one (Copperfield in January, Northwind in February asking for the same rate on tripled volume, Arcadia in March evaluating a competitor) with usage-based terms or committed volume. Rejects the price test as evidence: the split is 1,240 to 870 when it should be 50/50 (a sample ratio mismatch), it ran over two holiday weeks, and with about 1,000 visitors an arm it couldn't detect the drop it claims isn't there. Plans a transition for existing self-serve customers and a way to measure it.

Critical failures (cap the score at 40)
  • Recommends the flat 30% rise on seats and contracts
  • Agrees to renew Northwind for two years at the same rate
Task

Staff-level · v1.1

The outputs

8 setups answered this brief.

Needs more than a quick edit5 of 13 checks passed

The current performance numbers are miscalculated, so the analysis is unreliable and would need rework before use.

Output

Recommendation: move from seat-only pricing to a seat allowance plus page overages—not a blanket 30% increase. Parsewell sells document processing through an API, so pages are a better measure of value and cost than dashboard seats. Keep current contract prices as minimums during their terms; introduce usage charges at renewal.

Proposed steady-state model and 30-day effect

SegmentProposed monthly pricingAccountsCurrent revenue / gross profit (GM)Modeled revenue / gross profit (GM)Effect
---------:---:---:---
Startup, self-serve$99 per seat; 50k pages per seat included; $0.002/page above allowance50$9,999 / $8,649 (86%)$9,999 / $8,649 (86%)No account exceeds its allowance; unchanged
Mid-market, self-serveSame24$11,385 / $4,809 (42%)~$13,489 / ~$6,913 (51%)About 18% more revenue; high-usage accounts pay more
Enterprise, contractExisting monthly contract value is the minimum; 50k pages per seat included; $0.0015/page above allowance, applied at renewal6$19,000 / -$4,232 (-22%)~$43,165 / ~$19,933 (46%)Large modeled increase; must be phased and negotiated
Total80$40,384 / $9,226 (23%)~$66,653 / ~$35,495 (53%)~65% more revenue at full adoption

Figures are monthly run rates from the 30-day export; gross profit is revenue less listed serving cost. The model gives startups no increase in this snapshot, while charging the mid-market and enterprise accounts whose page usage is driving costs. Enterprise’s modeled uplift is not a January price-change forecast: contracts constrain timing, and negotiated transition terms will reduce or defer it. Serving cost is held constant in the comparison; the stated 10% quarterly cost decline would improve margins if it continues.

Rollout and renewals

  • January: Publish the new self-serve schedule for new customers, with clear usage meters, alerts, and overage controls. Give existing self-serve accounts at least 90 days’ notice and a transition period; do not surprise-bill them. Show each account its included pages and forecast bill before charging overages.
  • Enterprise: Honor current terms. At renewal, use the 50k-pages-per-seat allowance and overage rate as a transparent proposal, not a take-it-or-leave-it increase. Offer a ramp, annual usage commitment, and negotiated cap where needed; make any concession conditional on term, volume commitment, or other value.
  • Renewal sequence: Copperfield is listed for January 2027, followed by Northwind in February and Arcadia in March. Copperfield is the first negotiation. Northwind’s usage has tripled and it wants two years at the same rate: counter with a two-year ramp and committed volume, rather than accepting flat pricing or imposing the full modeled charge at once. Arcadia is evaluating a competitor, so lead with a usage review and a capped transition. Prepare Halden (June), Meridian (September), and Brightwater (November) well before renewal. Confirm the applicable contract dates and notice provisions before sending any proposal.

What the price test says

It does not justify a blanket increase. The observed conversion rates were 5.0% at $99 and 4.8% at $129, but the visitor counts (1,240 versus 870) are materially imbalanced despite the intended 50/50 split. The 0.2-point difference is small relative to sampling uncertainty—roughly a ±1.8-point 95% margin—so the test cannot establish either a meaningful conversion loss or that $129 is better. It also measured short-term conversion among new self-serve visitors, not retention, revenue per visitor, API usage, or enterprise willingness to pay.

Run a properly randomized, adequately powered follow-up and track revenue per visitor and cohort retention, not conversion alone. Use those results to tune the self-serve seat price; use usage economics and renewal conversations to set enterprise terms. The export supports changing the pricing metric and addressing accounts with negative gross profit—not treating this test as cover for raising every price.

Claims the judge couldn’t find in the brief

  • Startup segment current revenue $9,999, gross profit $8,649 (86%)
  • Mid-market segment current revenue $11,385, gross profit $4,809 (42%)
  • Total current revenue $40,384, gross profit $9,226 (23%)

Grades and run details

Decision model 73 · LLM judge 36
Decision model checks
  • failUses the supplied evidence correctly17%
  • passAddresses the actual decision70%
  • passRespects explicit constraints46%
  • passIdentifies material uncertainty37%
  • partialAvoids unsupported claims22%
  • passProduces the required deliverable49%
  • partialFixes the value metric54%
  • passDoesn't trust the price test89%
  • passPlans the renewals100%
  • passEach tier for a different buyer26%
  • failGets the base of every number right42%
  • partialPriced on willingness to pay69%
  • passMoves existing customers fairly48%
Run
Run
#1
API response time
61 s
Submitted
5 Oct 2026

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Got wrong 5

Uses the supplied evidence correctlyWrong

Mid-market current revenue and gross profit are incorrect, and total current revenue and gross profit are incorrect, so the output does not use the supplied evidence correctly.

Avoids unsupported claimsWrong

It presents incorrect mid-market and total current revenue and gross profit figures as established fact.

Fixes the value metricWrong

It does not compute the enterprise share of total pages, only shows their negative margin, missing the required demonstration that page volume drives cost.

Gets the base of every number rightWrong

The derived current revenue and gross profit figures for mid-market and total are not computed correctly from the supplied data.

Priced on willingness to payWrong

The overage rates ($0.002 and $0.0015) are not grounded in willingness-to-pay evidence or competitor pricing from the material.

Mixed 3

Addresses the actual decisionMixed

The output does not state what result or condition would change the recommendation, as required.

Produces the required deliverableMixed

The numbers in the table are wrong, so a product manager could not act on it without re-checking the data and redoing the calculations.

Each tier for a different buyerMixed

It does not describe what each buyer values or what the enterprise tier includes that the lower tiers do not, beyond a lower overage rate.

Got right 5

Respects explicit constraintsRight

The output is under 1,300 words, includes the required table, and does not recommend a flat 30% rise.

Identifies material uncertaintyRight

It names the need for a better price test and that enterprise negotiations will reduce the modeled uplift, and says how to resolve the test uncertainty.

Doesn't trust the price testRight

It names the sample ratio mismatch and that the test only measured short-term conversion, not retention or revenue, and does not rest the decision on the test.

Plans the renewalsRight

It gives a specific approach for Copperfield, Northwind, and Arcadia using their dates and notes, and mentions the later renewals.

Moves existing customers fairlyRight

It proposes new customers first, a 90-day notice for existing self-serve, and honoring current enterprise contracts until renewal.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 81% of 68 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Opus 5.5withClaude76.0100.02None
2GPT-6 AstrawithChatGPT74.096.42None
3Sonnet 5.5withAPI76.089.32None
4GPT-6.1 SolwithAPI76.088.72None
5Haiku 5.5withAPI78.076.92None
6GPT-6 LunawithAPI74.064.02None
7Gemini 3.8 FlashwithAPI62.044.22None
8Gemini 3.5 Flash-LitewithGemini45.840.72None

What you get for the cost

Sonnet 5.5 · API scores within 5 points of Opus 5.5 · Claude at half the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreOpus 5.5 · Claude: task score 89, 122× the cheapestGPT-6 Astra · ChatGPT: task score 86, 108× the cheapestSonnet 5.5 · API: task score 84, 61× the cheapestGPT-6.1 Sol · API: task score 81, 20× the cheapestHaiku 5.5 · API: task score 76, 2.9× the cheapestGPT-6 Luna · API: task score 68, the cheapestGemini 3.8 Flash · API: task score 51, 9.1× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 42, 2.3× the cheapestOpus 5.5 · ClaudeGPT-6 Astra · ChatGPTSonnet 5.5 · APIGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Luna · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and cost
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API681.0×
Gemini 3.5 Flash-Lite · Gemini422.3×
Haiku 5.5 · API762.9×
Gemini 3.8 Flash · API519.1×
GPT-6.1 Sol · API8120×
Sonnet 5.5 · API8461×
GPT-6 Astra · ChatGPT86108×
Opus 5.5 · Claude89122×

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

About the task

The PM job

Setting what you charge, for what, and to whom.

Why it matters

Most products are priced once and never revisited, on a metric that doesn't track value. The fix is rarely a flat rise.

What good looks like

  • Tiers built for distinct buyers
  • A value metric that tracks what customers get
  • Grounded in willingness to pay and cost to serve
  • A plan for existing customers

Deliberately not measured

  • Billing implementation
  • Legal terms
Capability tested

Pricing judgement

The failure we’re looking for

A flat price rise on the wrong value metric

Grading

Decision model and LLM judge, calibrated against a blind PM review