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 18 graded outputs by 9 models. 50% were usable with at most a quick edit.

Reliably right

  1. Doesn't trust the price test100% pass
    The output identifies the sample ratio mismatch (1240 vs 870 against a 50/50 design), the two-week holiday timing, and the inability to detect a meaningful drop, and refuses to rely on the test.
    Haiku 5.5 · API · Unlimited pages, falling margins
  2. Plans the renewals100% pass
    It handles each near-term renewal: Copperfield in January with a per-page rate, Northwind in February with a two-year escalating rate and walk-away floor, and Arcadia in March with a competitive benchmark and concession limit.
    Haiku 5.5 · API · Unlimited pages, falling margins
  3. Answers the CEO's flat rise94% pass
    Explains that $19 would exceed freelancers' price comfort zone (mid-teens) and still be far below agencies' willingness to pay, using trial and survey data.
    GPT-6 Luna · API · Fifteen dollars for everyone

Where it slips

  1. Gets the base of every number right36% 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
  2. Uses the supplied evidence correctly44% 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
  3. Sees who's underpriced56% pass
    Does not use signed value per seat from the account sample to quantify how much more value agencies get; relies on features and activity mentions but omits the explicit value metric.
    GPT-6 Luna · 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 · v1.2

The outputs

9 setups answered this brief.

Needs more than a quick edit8 of 13 checks passed

The current-state numbers in the table are miscalculated, so a PM would need to redo the sums and correct the analysis before using it.

Output

To: CEO From: Head of Product Date: January 2027 Subject: Alternative to the 30% Across-the-Board Price Increase: Shifting to Usage-Based Pricing

1. Diagnosis: We Have a Metric Problem, Not a Price-Level Problem

Tom is right to sound the alarm on our gross margin falling from 41% to 24.4% ($10,775 margin on $44,127 monthly revenue). However, a flat 30% price hike across seats and enterprise contracts will not fix our unit economics, and it risks triggering churn among our highest-margin customers.

Our fundamental issue is a mismatch between our value metric (seats) and our cost driver (pages processed): * Startups (50 accounts): Generate $9,999/mo in revenue and cost $1,297 to serve (87.0% gross margin). They average 21.6k pages/month. * Mid-Market (24 accounts): Generate $15,147/mo and cost $8,454 to serve (44.2% gross margin). Three high-volume accounts (P, Q, V) are already gross-margin negative. * Enterprise (6 accounts): Generate $19,000/mo and cost $23,232 to serve (-22.3% gross margin). Four of our six Enterprise customers are underwater. Northwind Logistics alone processes 4.82M pages, costing us $5,784 while paying $3,200.

Because our API is integrated by a few developers and runs headless in production, customers scale page volume massively without adding seats. Even if we could magically apply Tom’s 30% hike to all Enterprise contracts tomorrow (ignoring that four don’t renew until Mar–Nov), Enterprise revenue would rise to $24,700 against $23,232 in costs—a dismal 5.9% gross margin. Meanwhile, we would penalize our 87%-margin Startups, giving them a reason to look at Formwise ($79/seat).

---

2. Critique of the December Price Test

We cannot use the Growth team’s price test to justify a broad 30% increase for four reasons: 1. Severe Sample Ratio Mismatch (SRM): The test intended a 50/50 split across 2,110 visitors, but routed 1,240 (58.8%) to Control and 870 (41.2%) to Variant ($\chi^2 = 64.9, p < 0.0001$). Our randomization or tracking broke—likely due to caching, bot traffic, or self-selection before the event fired. The readout is invalid. 2. Significantly Underpowered: With only 104 total conversions, the 95% confidence interval for the difference is roughly $\pm 1.9\%$. The test could not detect anything smaller than a ~38% relative drop in conversion. "Not statistically significant" here means "insufficient data," not "no effect." 3. Wrong Cohort & Seasonality: A two-week window in mid-December captures holiday anomalies and tests initial sign-up conversion of small self-serve accounts, telling us nothing about 90-day retention, seat expansion, or Enterprise contract elasticity. 4. Ignoring Usage Economics: Raising the seat price to $129 still leaves unlimited pages uncapped, inviting adverse selection from heavy API users.

---

3. Proposed Pricing Model: Platform + Usage (Hybrid)

Competitors price on pages ($0.008–$0.015/page) or cap pages per seat (Formwise at 50k/seat). Our cost to serve is $0.0012/page (falling 10%/quarter). We must align revenue with page volume while retiring the "seat" tax for API products.

The New Model: * Starter (Self-Serve): $199/month platform fee. Includes 50,000 pages/mo and unlimited seats. Overage: $0.006 per page. * Growth (Self-Serve): $499/month platform fee. Includes 150,000 pages/mo and unlimited seats. Overage: $0.004 per page. * Enterprise (Annual Contract): $1,500/month ($18k/yr) platform fee. Includes 500,000 pages/mo and unlimited seats, plus SLA/SSO. Tiered overage: * 500k – 2M pages: $0.0035 per page * 2M+ pages: $0.0025 per page

Why this works: Even our lowest Enterprise tier ($0.0025/page) yields a 52% gross margin on marginal pages today, expanding to 67% over the next year as inference costs drop 10% per quarter. Meanwhile, we remain 50% to 70% cheaper per page than DocuLens ($0.008–$0.012) and ExtractIQ ($0.015).

---

4. Segment & Financial Impact

The table below compares our current state, Tom’s 30% hike (assuming 100% immediate realization and zero churn), and the proposed model (assuming customers optimize for the lowest-cost tier at current volumes):

Segment (Accounts)Current Rev / MoCurrent Cost / MoCurrent GM ($ / %)CFO +30% Rev / MoCFO +30% GM ($ / %)Proposed Rev / MoProposed GM ($ / %)
:---:---:---:---:---:---:---:---
Startups (50)$9,999$1,297$8,702 (87.0%)$12,999$11,702 (90.0%)$9,950$8,653 (87.0%)
Mid-Market (24)$15,147$8,454$6,693 (44.2%)$19,691$11,237 (57.1%)$26,332$17,878 (67.9%)
Enterprise (6)$19,000$23,232-$4,232 (-22.3%)$24,700$1,468 (5.9%)$64,650$41,418 (64.1%)
Total (80)$44,146$32,983$11,163 (25.3%)$57,390$24,407 (42.5%)$100,932$67,949 (67.3%)

(Note: Table sums exact per-account figures from the export; CSV rounding accounts for minor differences from the $44,127 / 24.4% total).

Customer Impact by Segment: * Startups (Neutral/Positive): Every single Startup processes $\le 40,000$ pages. On Starter ($199/mo), all 50 accounts are 100% covered by the 50k included allowance. The 17 accounts with 3 seats see a 33% price drop (from $297 to $199); the 19 accounts with 2 seats pay the exact same ($198 vs. $199); the 14 single-seat accounts pay $100 more (which we will grandfather for 6 months). Removing seat limits encourages whole-team adoption. * Mid-Market (Rebalanced to Value): All 24 accounts optimize onto the Growth plan ($499 base + $0.004 overage). Low-volume, high-seat accounts (e.g., Mid-market L at 7 seats / 134k pages) actually see a $194/mo price reduction, protecting them from churn. High-volume, unprofitable accounts (e.g., Mid-market Q at 544k pages, currently -$59 GM) move from $594/mo to $2,075/mo (68.5% GM). * Enterprise (Profitability Restored): Average effective price becomes $0.0033/page—still ~65% below DocuLens, giving us massive pricing power while turning a -$4.2k monthly loss into a +$41.4k gross profit.

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5. Rollout & Enterprise Renewal Playbook

Self-Serve Rollout (February 1): 1. New Sign-ups: Launch Starter and Growth tiers immediately on the pricing page. 2. Existing Self-Serve: Migrate accounts whose bill decreases or stays flat (37 of 74 accounts) immediately in February—this builds goodwill and unlocks unlimited seats. For accounts whose bill increases (14 single-seat Startups and 23 high-volume Mid-Market accounts), grant a 90-day transition window at legacy rates, paired with new dashboard usage alerts and an annual-commit discount option.

Enterprise Renewals (Account-by-Account): We cannot unilaterally hike annual contracts in January, as 5 of 6 renew later in the year. However, we have two immediate renewals and strong competitive leverage: * Copperfield Freight (Renews Jan 2027 | 2.38M pages | Currently $2,400/mo, -$456 GM): Immediate priority. Under the new card, their usage prices at $7,700/mo ($92.4k/yr). Playbook: Offer a 1-year transition bridge at $5,500/mo ($66k/yr)—a 48% gross margin for us, and still less than a third of what DocuLens ($19.5k/mo) would charge them. * Northwind Logistics (Renews Feb 2027 | 4.82M pages | Currently $3,200/mo, -$2,584 GM): Their pages tripled in a year and they want a 2-year lock at the same rate. Locking in -$31k/yr in losses for two years is a non-starter. Standard new pricing is $13,800/mo. Playbook: Show them their 3x volume growth and competitor benchmarks ($39k/mo on DocuLens). Offer the 2-year lock they want in exchange for a stepped commitment: $8,500/mo in Year 1 (47% GM today, rising to >60% as costs fall) and $11,500/mo in Year 2. * Arcadia Health (Renews Mar 2027 | 3.31M pages | Currently $4,500/mo, +$528 GM): Evaluating a competitor. Paying $0.00136/page today. Playbook: Any competitor they are evaluating will quote them $25k–$40k/mo for 3.3M pages. Position our new model aggressively: waive the $1,500 platform fee for Year 1 and lock in a flat $0.0028/page ($9,268/mo) on a 2-year deal. We double our revenue, hit a 57% gross margin, and remain 70% cheaper than the competitor they are evaluating. * Halden (Jun), Meridian (Sep), Brightwater (Nov): Begin QBRs 120 days prior to renewal, sharing usage telemetry that highlights their cost-per-page savings vs. market benchmarks before introducing the new rate card.

Claims the judge couldn’t find in the brief

  • Gross margin fell from 41% to 24.4% ($10,775 margin on $44,127 monthly revenue).
  • Mid-market generates $15,147/mo revenue and costs $8,454 to serve.
  • Startups cost $1,297 to serve.

Grades and run details

Decision model 58 · LLM judge 57
Decision model checks
  • failUses the supplied evidence correctly20%
  • partialAddresses the actual decision80%
  • partialRespects explicit constraints35%
  • partialIdentifies material uncertainty23%
  • partialAvoids unsupported claims27%
  • passProduces the required deliverable19%
  • passFixes the value metric95%
  • passDoesn't trust the price test100%
  • passPlans the renewals100%
  • partialEach tier for a different buyer38%
  • failGets the base of every number right70%
  • partialPriced on willingness to pay46%
  • partialMoves existing customers fairly95%
Run
Run
#1
API response time
28 s
Submitted
13 Oct 2026

Check by check

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

Got wrong 4

Uses the supplied evidence correctlyWrong

The current-state revenue and cost numbers for mid-market and startups in the table and diagnosis do not match the CSV totals, making several factual claims unsupported.

Identifies material uncertaintyWrong

The output does not name specific unknowns that could change the pricing decision (e.g., true customer WTP, churn risks) or state what would resolve them.

Gets the base of every number rightWrong

The current-month revenue and cost sums in the table are wrong, so derived percentages and margins are unreliable.

Priced on willingness to payWrong

Prices are set relative to competitors and cost rather than evidence of what each segment would pay; no willingness-to-pay data is used.

Mixed 1

Fixes the value metricMixed

The output shows enterprise accounts are loss-making but does not compute the enterprise share of total pages (e.g., 19.4M of 27.4M) as the strong answer requires.

Got right 8

Addresses the actual decisionRight

The output commits clearly to an alternative usage-based pricing model and addresses the CEO with a call.

Respects explicit constraintsRight

The response is under 1,300 words, includes the required table, price-test assessment, and enterprise renewal plan.

Avoids unsupported claimsRight

The main claims about causes and forecasts are presented as analysis, not as unsupported fact; the factual errors are covered under evidence.

Produces the required deliverableRight

A complete memo to the CEO with the requested table, segment impact, rollout, and price-test critique is provided.

Doesn't trust the price testRight

It identifies the sample ratio mismatch and underpowered/holiday flaws, and does not rest the pricing decision on the test.

Plans the renewalsRight

Copperfield (Jan), Northwind (Feb, tripled volume), and Arcadia (Mar, evaluating competitor) are each given a specific renewal approach using their dates and notes.

Each tier for a different buyerRight

Tiers (Starter, Growth, Enterprise) map to startups, mid-market, and enterprises, each with a different page allowance and buyer value.

Moves existing customers fairlyRight

It proposes new-signups first, a 90-day transition for affected self-serve customers, and a tailored playbook for enterprise renewals.

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 85% of 68 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Opus 5.5withClaude68.1100.02None
2GPT-6 AstrawithChatGPT70.296.42None
3Sonnet 5.5withAPI69.989.32None
4GPT-6.1 SolwithAPI74.088.72None
5Haiku 5.5withAPI73.776.92None
6GPT-6 LunawithAPI78.464.02None
7Gemini 4 ArgonwithAPI47.659.32None
8Gemini 3.8 FlashwithAPI55.644.22None
9Gemini 3.5 Flash-LitewithGemini41.840.72None

What you get for the cost

GPT-6.1 Sol · API scores within 5 points of Opus 5.5 · Claude at a sixth of 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 85, 122× the cheapestGPT-6 Astra · ChatGPT: task score 84, 108× the cheapestSonnet 5.5 · API: task score 81, 61× the cheapestGPT-6.1 Sol · API: task score 80, 20× the cheapestHaiku 5.5 · API: task score 74, 2.9× the cheapestGPT-6 Luna · API: task score 70, the cheapestGemini 4 Argon · API: task score 52, 15× the cheapestGemini 3.8 Flash · API: task score 48, 9.1× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 40, 2.3× the cheapestOpus 5.5 · ClaudeGPT-6 Astra · ChatGPTSonnet 5.5 · APIGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Luna · APIGemini 4 Argon · 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 · API701.0×
Gemini 3.5 Flash-Lite · Gemini402.3×
Haiku 5.5 · API742.9×
Gemini 3.8 Flash · API489.1×
Gemini 4 Argon · API5215×
GPT-6.1 Sol · API8020×
Sonnet 5.5 · API8161×
GPT-6 Astra · ChatGPT84108×
Opus 5.5 · Claude85122×

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