Tasks / Metrics & Experimentation

Pick a North Star metric

Can the model pick the one metric that measures the value a product creates, and break it into inputs teams can move?

Task type v1.1 · 2 tasksLast changed 5 Oct 2026 · ChangelogDifficulty

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

From 16 graded outputs by 8 models. 75% were usable with at most a quick edit.

Reliably right

  1. Says how it could be gamed100% pass
    Identifies gaming vectors (tapping through, notification overuse, short-term spikes, revenue manipulation, MAU drift) and sets specific guardrails (quality completion, push caps, blocking metrics, cohort retention checks, subscriber churn tracking).
    Sonnet 5.5 · API · Revenue, actives, or something else?
  2. Sees the growth is narrow100% pass
    It shows the split (chains up, independents' filled shifts and fill rate down, more leaving) and tracks independent venues via segment reporting and guardrails.
    GPT-6 Luna · API · Growing, on paper
  3. Checks the North Star can be measured100% pass
    It flags the 64% confirmation gap and proposes instrumenting check-in/out, reconciliation, and auditing to fix it before the board readout.
    GPT-6 Luna · API · Growing, on paper

Where it slips

  1. Identifies material uncertainty55% pass
    It identifies the confirmation gap as an unknown but does not say how resolving it could change the North Star decision.
    GPT-6.1 Sol · API · Growing, on paper
  2. Addresses the actual decision58% pass
    The output commits to a North Star but does not state what result or condition would change that recommendation.
    GPT-6 Luna · API · Growing, on paper
  3. Avoids unsupported claims67% pass
    It presents 'easy to tap through' and 'we don't track today' as facts without support, and does not label them as assumptions.
    Opus 5.5 · Claude · Revenue, actives, or something else?

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. Measures value created, not captured

    Does the chosen North Star measure the value customers get from the product, rather than revenue or a count of people who merely showed up?

    Passes when Picks a metric of value delivered to customers (a core action that predicts retention), and keeps revenue as a separate business measure.

  2. Breaks it into inputs teams can move

    Does the output break the North Star into a few input metrics, each one a team could own and move week to week, and pair it with a separate business measure of value captured?

    Passes when Gives a small tree of input metrics, each with an owner and a link to the North Star, plus the revenue or business measure it's paired with.

  3. Says how it could be gamed

    Does the output pressure-test the North Star for how teams could raise it without delivering more value, and set guardrail metrics against that?

    Passes when Names a realistic way the metric could be gamed in this product and the guardrails that would catch it.

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 4 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 a Staff PM at Crewmate. Every team has been steering by its own number, and the exec team wants to pick one North Star at next week's offsite, so the CEO can report it to the board three weeks later. Write the memo they'll read beforehand, in no more than 1,200 words: the North Star you recommend and why, its metric tree with an owner for each input, the business measure it's paired with, how it could be gamed and the guardrails against that, and what each team's current metric becomes. The numbers below are as the data team sent them. Not all of it matters equally.

What the model was given9 items: About Crewmate, What each exec wants, Shifts by quarter (monthly averages), GMV (monthly average), Venues, Workers, Same-day shifts, Completion data, From the COO
About CrewmateA marketplace for hospitality shifts: venues post shifts, and vetted workers pick them up. Crewmate takes 18% of each shift's value. Two kinds of venue: four enterprise hotel and restaurant chains (310 venues between them, on annual contracts) and 2,790 independent venues (bars, restaurants, cafes) who pay as they go.
What each exec wantsCEO: GMV, 'because it's what the board looks at'. Head of Demand: shifts posted. Head of Supply: active workers. COO: fill rate.
Shifts by quarter (monthly averages)Enterprise chains: Q2 posted 21,000, filled 19,500. Q3 posted 34,000, filled 31,600. Independent venues: Q2 posted 56,000, filled 44,200. Q3 posted 58,000, filled 39,400.
GMV (monthly average)Q2 $8.5M, Q3 $9.8M. Enterprise chains were 34% of GMV in Q2 and 46% in Q3. The chains raised hourly rates by 11% for the holiday season from August.
VenuesVenues whose shifts fill 90% of the time or more: 84% still posting six months later. Under 70%: 41%. In Q3, 9% of independent venues stopped posting altogether (5% in Q2). No chain venue stopped.
WorkersRegistrations: 6,100 in Q2, 7,900 in Q3, after Supply introduced a $25 sign-up bonus in July. 2,370 of the Q3 registrants have worked a shift. Workers who work three or more shifts in their first month: 66% still working after three months; everyone else: 12%.
Same-day shiftsShifts posted less than 24 hours before they start fill 58% of the time. They are 31% of independents' posts and 8% of the chains'.
Completion dataVenues confirm in the app that a shift was worked for 64% of filled shifts; the rest are assumed worked unless the venue raises a dispute. Of confirmed shifts, 6% were no-shows or disputed.
From the COO"Since September, Ops pays workers a $15 rescue bonus to pick up same-day shifts that would otherwise go unfilled. 6,200 rescue fills last month, $93,000. Rescue-filled shifts have a 13% no-show rate, against 4% for the rest. It's kept our fill rate from falling further."
What a strong answer doesThe answer key the graders mark against

Works out from the raw numbers what the pack doesn't say: all of the growth is the four chains (filled shifts up from 19,500 to 31,600 a month, plus an 11% rate rise), while independent venues filled fewer shifts (44,200 to 39,400, so their fill rate fell from 79% to 68%) and are leaving faster (9% stopped posting, against 5%); GMV rose mostly on the chains' volume and prices. Picks a North Star of value delivered to both sides, such as shifts worked, but designs it so the decline can't hide: measured for independents separately, or as reliably staffed venues, or with an explicit independent-venue guardrail. Notices that 'worked' is only confirmed for 64% of shifts, so the North Star can't be trusted yet, and makes fixing confirmation an early input or uses a measurable proxy until then. Treats the rescue bonus as a way the fill rate (and any fills-based North Star) is being bought: 13% no-shows against 4%, so it needs guardrails such as cost per worked shift and no-show rate. Shows how shifts posted, registrations (2,370 of 7,900 worked) and GMV each rose without more value. Builds a metric tree with owners, pairs it with revenue or GMV as the business measure, and redefines each team's metric as an input.

Critical failures (cap the score at 40)
  • Recommends GMV or shifts posted as the North Star
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit13 of 14 checks passed

The memo is well-structured, uses evidence correctly, and covers all required elements. A PM could use it with minor wording edits, without re-checking data or reasoning.

Output

To: Executive team Subject: Choose successful completed shifts as Crewmate’s North Star

Recommendation

Adopt monthly successful completed shifts as Crewmate’s North Star: the number of unique posted shifts that are filled, actually worked, and have no unresolved dispute. Count a shift once, after its scheduled end; exclude cancellations, no-shows, and disputed shifts until resolved.

This measures the outcome venues hire us for and workers show up to deliver—not activity on either side of the marketplace. It also directs attention to the reliability that supports repeat use. Venues with fill rates of at least 90% are much more likely to still be posting six months later than venues below 70% (84% vs. 41%).

The latest figures show why the distinction matters. Monthly posts rose from 77,000 in Q2 to 92,000 in Q3, while fills rose from 63,700 to 71,000. But overall fill rate fell from 82.7% to 77.2%; independent-venue fill rate fell from 78.9% to 67.9%, and independent venues stopping posts rose from 5% to 9%. Meanwhile, GMV grew 15%, but chains’ share of GMV rose from 34% to 46% and they raised rates 11% for the holiday season. GMV is important, but this mix and price change make it a poor standalone signal of marketplace health.

Nor should we equate registrations with supply: only 2,370 of 7,900 Q3 registrants worked a shift, after the sign-up bonus began. Workers who complete three or more shifts in their first month are far more likely to remain active. We should reward dependable activity, not sign-ups.

Metric tree and accountability

The core identity is:

Successful completed shifts = valid posted shifts × fill rate × successful-completion rate

InputDefinitionAccountable owner
Valid posted shiftsUnique, legitimate shifts with a real venue, role, time, and pay; split by enterprise/independent and lead timeHead of Demand
Fill rateFilled shifts ÷ valid posted shiftsCOO
Successful-completion rateShifts actually worked without a no-show or unresolved dispute ÷ filled shiftsCOO; Product/Data owns reliable status capture
Qualified, available worker coverageRepeat, activated workers available for the shifts and locations demanded; leading driver of fill and completionHead of Supply
Repeat venue demandActive venues returning to post, with retention and posting frequency by venue segmentHead of Demand

The first three inputs calculate the North Star; worker coverage and repeat venue demand are leading drivers and diagnostic cuts, not extra terms to multiply into it. Review all results by enterprise vs. independent, and scheduled vs. same-day.

Paired business measure

Pair the North Star with monthly contribution margin dollars: Crewmate’s 18% take-rate revenue on completed-shift GMV, less variable fulfillment costs, including rescue bonuses and other worker incentives. This checks that completed shifts create sustainable value rather than being purchased at any cost. Continue reporting GMV to the board as a scale measure, but not as our North Star.

Measurement, gaming, and guardrails

Completion data is not yet strong enough for an unqualified claim: venues confirm only 64% of filled shifts, and the rest are assumed worked unless disputed. Among confirmed shifts, 6% were no-shows or disputed; rescue-filled shifts have a 13% no-show rate versus 4% for other fills. For the first board readout, label the metric provisional, disclose the confirmation rate, and show the calculation and coverage. Then instrument worker and venue check-in/out, reconcile mismatches, and audit a sample. Do not treat missing confirmation as proof of success indefinitely.

Key gaming risks and controls:

  • Post inflated, duplicate, or low-quality shifts: count only valid shifts; monitor cancellations, disputes, and fill rate.
  • Favor easy or high-priced chain shifts over independent or same-day demand: report North Star and fill/completion rates by segment; track independent-venue retention and same-day performance as guardrails.
  • Buy apparent success with rescue bonuses: monitor rescue share, no-show rate, and contribution margin alongside completed shifts. Rescue fills are not successes unless the shift is actually worked.
  • Misreport completion or suppress disputes: require auditable status capture and monitor confirmation coverage and dispute rates.

What current metrics become

  • CEO — GMV: remains a board/business measure, paired with contribution margin; it is not the North Star.
  • Demand — shifts posted: becomes a leading input, with valid posts and repeat independent-venue demand emphasized over raw volume.
  • Supply — active workers: shifts to qualified, repeat workers and first-month activation (especially the three-shift threshold); registrations are a funnel diagnostic.
  • COO — fill rate: remains a critical driver and guardrail, but success is completed shifts—not fills that fail to show up.

At the offsite, agree on the definition, owners, and guardrails. Before the board update, establish the provisional baseline and be explicit about its measurement limits.

Grades and run details

Decision model 100 · LLM judge 93
Decision model checks
  • passUses the supplied evidence correctly60%
  • passAddresses the actual decision75%
  • passRespects explicit constraints56%
  • passIdentifies material uncertainty85%
  • passAvoids unsupported claims24%
  • passProduces the required deliverable82%
  • passSees the growth is narrow98%
  • passChecks the North Star can be measured100%
  • passSees the fill rate being bought98%
  • passOne North Star for both sides99%
  • passMeasures value created, not captured100%
  • passGets the base of every number right45%
  • passBreaks it into inputs teams can move100%
  • passSays how it could be gamed100%
Run
Run
#1
API response time
37 s
Submitted
5 Oct 2026

Usable with a quick edit11 of 14 checks passed

The memo is well-structured, evidence-based, and covers all required elements; a PM could use it with minor edits, such as adding a condition that would change the recommendation.

Output

Memo: One North Star for Crewmate

To: Exec team | From: Staff PM | For: Offsite decision, ahead of the board report

Recommendation

North Star: Shifts worked as booked, per month. This counts shifts that were filled, worked, and not disputed. It is always reported split by chains and independents.

Paired business measure: Net revenue. This is our 18% take on worked shifts, minus worker incentives (sign-up and rescue bonuses).

A shift that gets filled and actually worked is the one event that keeps both sides of the marketplace. Venues at 90%+ fill keep posting (84% are still posting at six months, against 41% for venues under 70%). Workers who get three or more shifts in their first month stay (66% against 12%). Every team's work feeds this one number.

Why not the four metrics on the table

Q3 looks good on each team's number. The data underneath says otherwise.

  • GMV (CEO): up 15%, from $8.5M to $9.8M a month. All of that growth is chains. Chain GMV rose from $2.9M to $4.5M, helped by an 11% holiday rate rise that will unwind. Independent GMV fell from $5.6M to $5.3M, down 6%. Four customers now produce 46% of GMV. GMV hid our biggest problem, and next quarter it will flatter or punish us on chain pricing we don't control.
  • Shifts posted (Demand): up 19%. About 13,000 of the 15,000 extra shifts were chains. The metric also counts a shift that never fills as a win.
  • Active workers (Supply): registrations up 30% after the $25 bonus. Only 2,370 of the 7,900 Q3 registrants (30%) have worked a shift.
  • Fill rate (COO): this is the closest to right, but it fell from 83% to 77%. Chains held at 93%, while independents dropped from 79% to 68%. It has three flaws:
  • It assumes the 36% of unconfirmed shifts were worked.
  • It is propped up by rescue fills, which have a 13% no-show rate.
  • It improves if venues simply stop posting hard shifts.

The real story: the average independent venue is now below the 70% fill line where retention collapses. Independent churn nearly doubled, from 5% to 9% a quarter. Chains, at 93%, lost no venues.

Estimated baseline. This applies the 6% no-show/dispute rate seen on confirmed shifts to all filled shifts.

Q2Q3Change
Chains~18,300~29,700+62%
Independents~41,500~37,000−11%
Total~59,900~66,700+11%

This is the honest version of Q3: real growth from chains, with the independent base eroding.

Metric tree

Shifts worked as booked = Shifts posted × Fill rate × Worked-as-booked rate

1. Shifts posted by active venues. Owner: Head of Demand - 1a. Independent venues still posting (quarterly churn, now 9%) - 1b. Share of independent posts made 24+ hours ahead (now 69%). Same-day shifts fill only 58% of the time and are 31% of independent posts, against 8% for chains. - 1c. Chain shift volume (account management) 2. Fill rate, by segment - 2a. Fill rate on shifts posted 24+ hours ahead. Owner: Head of Supply. The driver is new workers reaching three shifts within 30 days, because that is the pool that becomes reliable supply. - 2b. Same-day fill rate (58%). Owner: COO 3. Worked-as-booked rate. Owner: COO - 3a. No-show rate (4% standard, 13% on rescue fills) - 3b. Venue confirmation rate (64%). Product builds the confirmation flow; the COO owns the rate.

Fill rate by segment, and the share of independents at 90%+ fill, remain diagnostics the COO reports weekly.

The business pairing

Our gross take is about $1.76M a month. Net revenue subtracts what we pay to manufacture activity, which has become material.

Rescue bonuses. An independent shift averages about $134 GMV, so we earn about $24 on it. A rescue bonus costs $15. After the 13% no-show rate, that is about $17 per rescue shift actually worked, or roughly 70% of our take. Last month that was $93,000.

Sign-up bonuses. At $25 per sign-up, 7,900 registrations cost up to about $200,000, if the bonus was paid on sign-up. Fewer than a third of those registrants have worked.

Net revenue makes these trade-offs visible. GMV hides them.

We should still show the board GMV as context, with the chain price effect disclosed. It should not be what we steer by.

How this could be gamed, and the guardrails

Gaming riskGuardrail
Counting unverified shifts as workedA shift counts only if confirmed, or unconfirmed after a 7-day dispute window. Confirmation rate is a tracked guardrail, raised from 64% toward a target we set at the offsite.
Buying fills with bonusesPairing with net revenue. Incentive cost per worked shift is capped. Rescue and sign-up bonuses are paid only on completion.
Growing chains while independents erodeThe North Star is always shown split by segment. Guardrails: independent churn back to ≤5% a quarter, and the share of independents at 90%+ fill rising.
Venues posting fewer hard shifts to lift fillThe North Star counts volume, so fewer posts lowers it. Independent shifts posted is tracked.
Loosening vetting to add workers fastNo-show and dispute rate guardrail; a rise triggers review.
Price changes inflating the headlineThe North Star is a count of shifts, so it is immune to rate changes.

What each team's metric becomes

TeamTodayBecomesPractical change
CEOGMVBoard context, reported alongside net revenueDisclose chain concentration and the holiday price effect
DemandShifts postedIndependent venues retained; share of independent posts made 24+ hours ahead; chain volumePush independents to post earlier, where we can actually fill
SupplyActive workersNew workers reaching 3 shifts in their first 30 daysMove the $25 bonus from sign-up to third shift; prioritise first-shift matching for new workers
COOFill rateSame-day fill rate and worked-as-booked ratePay the rescue bonus on completion only; fill rate becomes a diagnostic

Nobody loses their number. Each team's number becomes an input, and no team's metric can go up while the North Star goes down.

Decisions for the offsite

  1. Adopt the North Star and its definition, including the 7-day dispute window.
  2. Adopt net revenue as the paired measure, with GMV as board context.
  3. Confirm the owners for each input above.
  4. Approve the incentive changes: both bonuses paid on completion.
  5. Agree the board narrative: real chain growth, an independent problem we have identified, and a plan to fix it.

Before the board meeting

Data will backfill the North Star by segment for Q2 and Q3, using actual confirmed and disputed records instead of my estimate. We do not yet know the dispute rate on unconfirmed shifts. That is the main uncertainty in the baseline.

Data will also separate how much of the Q3 fill rate came from rescue fills, so we report the underlying independent rate. I expect it to be below 68%.

Grades and run details

Decision model 89 · LLM judge 87
Decision model checks
  • passUses the supplied evidence correctly10%
  • passAddresses the actual decision89%
  • passRespects explicit constraints35%
  • passIdentifies material uncertainty86%
  • partialAvoids unsupported claims52%
  • passProduces the required deliverable52%
  • passSees the growth is narrow100%
  • passChecks the North Star can be measured100%
  • passSees the fill rate being bought100%
  • passOne North Star for both sides100%
  • passMeasures value created, not captured95%
  • failGets the base of every number right30%
  • passBreaks it into inputs teams can move100%
  • passSays how it could be gamed100%
Run
Run
#1
Time to output
82 s
Submitted
5 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 2

Identifies material uncertaintyRightMixed
GPT-6 Luna · API

It names the confirmation gap as a key unknown, bounds it with the 64% figure, and says how to resolve it (instrument check-in/out, reconcile, audit).

Opus 5.5 · Claude

It names the unknown dispute rate on unconfirmed shifts but does not say how that would change the decision or what result would alter the call.

Gets the base of every number rightRightMixed
GPT-6 Luna · API

All derived percentages and rates are computed correctly from the supplied data, and bases are stated where needed.

Opus 5.5 · Claude

All derived percentages and rates are computed correctly from the supplied data, and bases are stated where needed.

All mixed 1

Addresses the actual decisionMixedMixed
GPT-6 Luna · API

The output commits to a North Star but does not state what result or condition would change that recommendation.

Opus 5.5 · Claude

The output commits to a North Star but does not state what result or condition would change that recommendation, as required.

All got right 11

Uses the supplied evidence correctlyRightRight
GPT-6 Luna · API

All claims about the current situation are taken directly from the brief or derived by correct arithmetic.

Opus 5.5 · Claude

All factual claims about the current situation are directly from the supplied context or correctly derived by arithmetic.

Respects explicit constraintsRightRight
GPT-6 Luna · API

The output is a memo under 1,200 words, addressed to the exec team, and includes all requested elements.

Opus 5.5 · Claude

The memo is under 1,200 words, addressed to the exec team, and includes all requested elements.

Avoids unsupported claimsRightRight
GPT-6 Luna · API

Interpretations are presented as conclusions from the data, not as established facts, and no unsupported causal claims are made.

Opus 5.5 · Claude

Interpretations and forecasts are clearly labelled as such, and no unsupported claims are presented as established fact.

Produces the required deliverableRightRight
GPT-6 Luna · API

The memo is complete, within word count, and usable by the exec team with all required components.

Opus 5.5 · Claude

The memo is complete, in the right form, within length, and usable by the exec team with light edits.

Sees the growth is narrowRightRight
GPT-6 Luna · API

It shows the split (chains up, independents' filled shifts and fill rate down, more leaving) and tracks independent venues via segment reporting and guardrails.

Opus 5.5 · Claude

It clearly shows chains up, independents down in filled shifts and fill rate, with higher churn, and tracks independents via segment split and guardrails.

Checks the North Star can be measuredRightRight
GPT-6 Luna · API

It flags the 64% confirmation gap and proposes instrumenting check-in/out, reconciliation, and auditing to fix it before the board readout.

Opus 5.5 · Claude

It flags the 64% confirmation gap and proposes a 7-day dispute window and confirmation rate guardrail to make the North Star measurable.

Sees the fill rate being boughtRightRight
GPT-6 Luna · API

It uses the 13% no-show rate and cost to show rescue fills inflate the number, and sets guardrails like no-show rate and contribution margin.

Opus 5.5 · Claude

It uses the 13% no-show rate and cost to show rescue fills inflate the number, and sets guardrails like incentive cost per worked shift and paying bonuses on completion.

One North Star for both sidesRightRight
GPT-6 Luna · API

The North Star (successful completed shifts) only rises when a venue and a worker both get value, and the memo explains why that matters in a marketplace.

Opus 5.5 · Claude

The North Star 'shifts worked as booked' requires both a venue and a worker to get value, and the memo explains why that matters for retention.

Measures value created, not capturedRightRight
GPT-6 Luna · API

The North Star measures completed shifts, a core action that predicts retention, and keeps revenue as a separate business measure.

Opus 5.5 · Claude

It measures value delivered (a worked shift that predicts retention) and keeps revenue as a separate paired business measure.

Breaks it into inputs teams can moveRightRight
GPT-6 Luna · API

It breaks the North Star into valid posted shifts, fill rate, and successful-completion rate with owners, and pairs it with contribution margin.

Opus 5.5 · Claude

It breaks the North Star into inputs (shifts posted, fill rate, worked-as-booked rate) with owners and pairs it with net revenue.

Says how it could be gamedRightRight
GPT-6 Luna · API

It names realistic gaming risks (inflated posts, favoring chain shifts, buying fills, misreporting) and sets specific guardrails against each.

Opus 5.5 · Claude

It lists realistic gaming risks (unverified shifts, bonus-fueled fills, segment erosion, fewer hard shifts, looser vetting) and specific guardrails.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI95.996.72None
2GPT-6.1 SolwithAPI97.793.32None
3GPT-6 LunawithAPI95.588.32None
4GPT-6 AstrawithChatGPT94.288.32None
5Opus 5.5withClaude94.680.82None
6Haiku 5.5withAPI88.881.72None
7Gemini 3.8 FlashwithAPI78.965.82None
8Gemini 3.5 Flash-LitewithGemini54.438.321 capped

What you get for the cost

GPT-6 Luna · API scores within 4 points of Sonnet 5.5 · API at about a twentieth of the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreSonnet 5.5 · API: task score 96, 22× the cheapestGPT-6.1 Sol · API: task score 96, 9.6× the cheapestGPT-6 Luna · API: task score 92, the cheapestGPT-6 Astra · ChatGPT: task score 91, 52× the cheapestOpus 5.5 · Claude: task score 86, 36× the cheapestHaiku 5.5 · API: task score 86, the cheapestGemini 3.8 Flash · API: task score 71, 7.5× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 44, 1.8× the cheapest, 1 critical failureSonnet 5.5 · APIGPT-6.1 Sol · APIGPT-6 Luna · APIGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeHaiku 5.5 · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costAt least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API921.0×
Haiku 5.5 · API861.1×
Gemini 3.5 Flash-Lite · Gemini441.8×
Gemini 3.8 Flash · API717.5×
GPT-6.1 Sol · API969.6×
Sonnet 5.5 · API9622×
Opus 5.5 · Claude8636×
GPT-6 Astra · ChatGPT9152×

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

Choosing the metric the whole company steers by.

Why it matters

Pick revenue and you steer by the rear-view mirror. Pick a vanity metric and teams will happily grow it while the product gets worse.

What good looks like

  • Measures value delivered to customers, not value captured
  • Grounded in what predicts retention in the data
  • Broken into input metrics a team can own
  • Says how it could be gamed, with guardrails

Deliberately not measured

  • Dashboard design
  • Naming
Capability tested

Metric design

The failure we’re looking for

Revenue, or a vanity metric, as the North Star

Grading

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