Tasks / Growth

Activation & onboarding review

Can the model find the friction that matters most and prioritise the fixes?

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. 69% were usable with at most a quick edit.

Reliably right

  1. Respects explicit constraints100% pass
    The output is a review addressed to Omar, well under 800 words, and respects the requested form.
    GPT-6 Luna · API · The checklist isn't the moment
  2. Produces the required deliverable100% pass
    The review covers activation definition, evidence, changes, design proposal, and goal, is addressed to Omar, within length, and is actionable.
    GPT-6 Luna · API · The checklist isn't the moment
  3. Activation defined by what predicts retention100% pass
    Activation is defined by the behaviour that predicts retention (bank+cat 10+), and fixes are judged by their effect on that event.
    GPT-6 Luna · API · The checklist isn't the moment

Where it slips

  1. Uses the supplied evidence correctly44% pass
    The output states 'we have no step-level data for screens 2–8', 'a blank builder is hard for non-analysts', and 'people invite colleagues to something useful, not to a setup screen' as facts, none of which appear in the brief or follow from arithmetic.
    Opus 5.5 · Claude · Analytics tool losing users at setup
  2. Avoids unsupported claims55% pass
    It presents 'a blank builder is hard for non-analysts' and 'people invite colleagues to something useful, not to a setup screen' as established facts without labelling them as hypotheses.
    Opus 5.5 · Claude · Analytics tool losing users at setup
  3. Addresses the actual decision56% pass
    The output commits to a ranked order but does not state what result or condition would change that order, as required.
    GPT-6 Astra · ChatGPT · Analytics tool losing users at setup

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. Activation defined by what predicts retention

    Does the review define (or question) the activation event by whether it predicts retention, and judge fixes by their effect on that event rather than on step completion?

    Passes when Names the activation behaviour and its link to retention (from the supplied data, or as an explicit assumption to verify), and ranks fixes by their effect on it.

  2. Gets people to value sooner

    Do the recommended fixes shorten the path to the first moment of value, by removing or deferring steps before it or starting people with templates or sample data, rather than adding tours, tooltips or extra steps?

    Passes when The main fixes cut or defer steps before first value, or give a warm start, and say which steps go.

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 2 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 PM for onboarding at Tidepool. Omar, our Head of Product, has asked for a review of onboarding (his note is below). Write it in no more than 800 words: what activation should mean for us, the evidence, what to change in onboarding, and what to make of the design team's proposal and next quarter's goal. The files are attached.

What the model was given4 items: About Tidepool, omar_note.md, onboarding_today.md, first_week_behaviour.csv (August sign-ups: how many who did each thing in their first week were still active at day 60)
About TidepoolA personal finance app: connect your bank, see where your money goes, and budget.
omar_note.md1 lines · Download
From Omar (Head of Product): "Activation is flat at 61%. Review our onboarding and tell me what to fix. The design team has proposed a celebration screen and a progress bar for the checklist."
onboarding_today.md8 lines · Download
# Onboarding today (for 4,000 sign-ups in August)

1. Sign up (100%)
2. Setup checklist: name your account, pick a theme, set a savings goal, enable notifications (61% complete it)
3. Connect a bank (50%)
4. Categorise transactions (34% reach 10)

…
first_week_behaviour.csv (August sign-ups: how many who did each thing in their first week were still active at day 60)first_week_behaviour,retained_at_day_60,churned_by_day_60 All sign-ups,1120,2880 Completed the setup checklist,742,1696 Connected a bank account,964,1036 Categorised 10+ transactions,891,489 Connected a bank AND categorised 10+ transactions,842,238 Created a budget,410,820 Opened the app 4+ days,870,1310
What a strong answer doesThe answer key the graders mark against

Works out retention for each first-week behaviour from the file: all sign-ups 28% (1,120 of 4,000); completed the checklist about 30% (barely better than average); connected a bank about 48%; categorised 10+ transactions about 65%; connected a bank and categorised 10+ about 78%, and that group is about three-quarters of everyone retained (842 of 1,120). So the checklist isn't activation and raising its completion to 75% would do little; activation should be connecting a bank and categorising ten transactions in week one. Recommends cutting or deferring checklist steps that sit before that (theme, notifications, savings goal) and getting people to connect a bank and categorise sooner (for example suggested categories to confirm), and resets the goal to the new activation rate (about 27% today, 1,080 of 4,000). Treats the celebration screen and progress bar as polishing the wrong step. Notes that the data shows correlation and proposes a test to confirm that driving the behaviour improves retention.

Critical failures (cap the score at 40)
  • Recommends raising checklist completion as the main goal
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit8 of 11 checks passed

The memo is structured, evidence-based, and a PM could act on it with a quick edit to soften the unsupported causation claim about app opens.

Output

To: Omar From: PM, Onboarding Re: Onboarding review

Summary

Our activation metric is measuring the wrong thing. Completing the setup checklist is 61% of sign-ups, but it barely separates users who stay from those who leave: 30% of completers are retained at day 60, against 24% of non-completers. The behaviours that matter are connecting a bank account and categorising transactions. Users who do both are retained at 78%. Users who do neither are retained at 6%. I recommend redefining activation, putting bank connection at the centre of onboarding, and reshaping the design proposal around it.

What activation should mean

Activation = connected a bank account and categorised 10+ transactions in the first 7 days.

Baseline: 27% of August sign-ups (1,080 of 4,000). This definition reflects the core value (seeing where money goes), is the only combination with a large retention gap, and is measurable from event data.

Evidence

First-week behaviourShare of sign-upsRetained at day 60
Completed checklist61%30%
Did not complete checklist39%24%
Connected a bank50%48%
Did not connect a bank50%8%
Categorised 10+35%65%
Did not categorise 10+65%9%
Bank and categorised27%78%
Bank only23%13%
Categorised only8%16%
Neither43%6%
Opened app on 4+ days55%40%
Created a budget31%33%

Four things stand out:

  • Bank connection and categorising are complementary. Each alone yields 13–16% retention; together they yield 78%. The value appears only when both happen.
  • The biggest fixable leak is after bank connection. 46% of users who connect a bank never categorise 10 transactions.
  • Budget creation is weak in week one. Its retention (33%) is barely above the 28% baseline, so it shouldn't define activation yet.
  • App-open frequency is an outcome, not a cause. It's a useful engagement metric, but users who are already activated open the app more.

These are correlations. Users who do more are probably more motivated to begin with, so the 78% overstates the causal effect. We need experiments to confirm the lift, but the gap is large enough to act on now.

What to change in onboarding

  1. Make bank connection the first real step. Only half of sign-ups connect, and the cosmetic steps (name, theme) come first. Move bank linking up, explain the payoff ("see where last month went"), and reduce friction: clear read-only reassurance, and recovery paths for failed connections.
  2. Help users categorise 10 transactions quickly. Pre-suggest categories, allow one-tap confirmation, and show progress ("3 of 10: your spending is taking shape"). Target the 46% who stop short.
  3. Demote checklist items that don't carry the value. Theme, name, and notifications should be optional. The savings goal is untested in our data, so test it before cutting it.
  4. Move budget creation later. Prompt it after the user has categorised 10 transactions, and measure whether that lifts budget creation and retention in weeks 2–4.

The design team's proposal

The celebration screen and progress bar are good craft aimed at the wrong target. A progress bar on the current checklist will push users through theme and notifications faster. That raises the 61% number without touching the steps that predict retention, and may do nothing for bank connection.

The celebration is valuable, but only at a milestone that matters. I'd ask the design team to rebuild the progress bar around the two activation steps (bank connected, 10 transactions categorised), with the celebration triggered when both are done. We test it against the current flow and ship only if activation moves.

Next quarter's goal

Replace "checklist completion 61% → 75%" with "activation (bank connected and 10+ categorised in week one) 27% → 35%."

Sizing: in a 4,000-user cohort, 27% → 35% is about 320 more activated users. At 78% retention, that's roughly 250 more retained at day 60. A checklist point, by contrast, is worth about 10 retained users at current completer retention. Marginal users will retain worse than average, so treat 250 as an upper bound.

35% is ambitious but reachable: it requires bank connection to rise from 50% to about 60%, and categorising conversion from 54% to about 58%. The checklist stays as a leading indicator, not the goal.

Next steps

  1. Adopt the new activation definition this week and add it to the onboarding dashboard.
  2. Run an A/B test of bank-first onboarding against the current flow.
  3. Ask design to rebuild the progress bar and celebration around the activation steps, and test before shipping.
  4. Confirm the retention lift with a holdout or experiment, since current figures are correlational.

Grades and run details

Decision model 82 · LLM judge 92
Decision model checks
  • failUses the supplied evidence correctly19%
  • passAddresses the actual decision94%
  • passRespects explicit constraints58%
  • passIdentifies material uncertainty70%
  • passAvoids unsupported claims34%
  • passProduces the required deliverable92%
  • passComputes retention by behaviour67%
  • passTests before trusting the correlation100%
  • passActivation defined by what predicts retention100%
  • failGets the base of every number right19%
  • passGets people to value sooner82%
Run
Run
#1
API response time
29 s
Submitted
8 Oct 2026

Usable with a quick edit9 of 11 checks passed

The output is a complete, data-driven review with clear recommendations and next steps, requiring no re-checking of evidence or reasoning.

Output

To: Omar | Re: Onboarding review

Recommendation

Activation should mean connecting a bank and categorising 10+ transactions in the first week. Our current definition, completing the setup checklist, barely predicts retention, so the 61% is flat for a reason: we're measuring the wrong thing. I'd fix the path to the real activation event, use the design team's ideas only on that path, and replace next quarter's goal.

Evidence (4,000 August sign-ups, day-60 retention)

First-week behaviourUsersRetained at day 60
All sign-ups4,00028%
Completed checklist2,43830%
Did not complete checklist1,56224%
Connected bank2,00048%
Did not connect bank2,0008%
Categorised 10+1,38065%
Budget created1,23033%
Opened app 4+ days2,18040%
Bank AND 10+ categorised1,08078%

Three things stand out:

  • The checklist is a weak signal. Checklist completers retain at 30%, against 28% for everyone. The checklist is theme, notifications, account name and goal, so it's mostly cosmetic setup.
  • The combination matters, not either step alone. Backing out the overlaps from the CSV:
  • Bank only (no categorising): about 920 users, 13% retained.
  • Categorised only (no bank): about 300 users, 16% retained.
  • Neither: about 1,700 users, 6% retained.
  • Both: 78% retained.

Users who connect but never see their spending sorted do worse than average. The value moment is seeing where the money goes, which needs both steps. - Budgets and app opens aren't the answer. Budget creators retain at 33%, close to baseline. Opening the app 4+ days (40%) is engagement, not a lever we can pull in onboarding.

Caveat: this is observational. Motivated users probably do everything, so these numbers show who retains, not what causes it. The recommendations below are therefore tests, not guarantees.

What to change

The funnel is 100% → 50% connect → 27% connect and categorise. There are two leaks:

  1. Half of users never connect a bank, and they retain at 8%. Put bank connection first, straight after sign-up. Move theme, notifications and account name out of the critical path (defer or drop them). Look at where the connect flow fails and whether trust copy (read-only access, security) is needed. I can't tell from this data whether the checklist is causing bank drop-off, so I'd A/B test bank-first against the current order.
  2. 46% of those who connect (920 of 2,000) never reach 10 categorised transactions. This is the biggest fixable gap, since 78% of those who finish retain. Auto-categorise on connect, pre-fill best guesses, and walk users through their first 10 with one-tap confirmations rather than leaving them to find the feature.

I'd also instrument the funnel by step and by time, so we can see where in the first week people stall.

The design team's proposal

A celebration screen and a progress bar for the checklist would probably raise checklist completion, but that metric barely relates to retention, so we'd likely see a "win" with no change in retained users. I wouldn't ship them as proposed. Both ideas are cheap and sound, though, so I'd redirect them:

  • Make the progress bar track the real path (connect bank → categorise 10).
  • Put the celebration at the moment of value, such as the 10th categorised transaction, ideally with a first insight ("here's where your money went").

This is a good way to involve design in the work above rather than rejecting their proposal.

Next quarter's goal

Drop "checklist completion 61% → 75%". Pushing it up could even pull effort toward low-value steps.

Replace it with: percentage of sign-ups who connect a bank and categorise 10+ transactions within 7 days. Baseline is 27%. I'd propose a target around 35%, but that is a judgment call until we've sized the effect of the first experiments, so let's set it after a few weeks of data. Guardrail: day-60 retention among those users should hold near its current 78% (if we widen the funnel with less-motivated users, it may dip, and we should watch that). Track the real outcome, overall day-60 retention (28% today), alongside it.

Next steps

  • Run the bank-first vs current-order A/B.
  • Prototype auto-categorisation and a guided first-10 flow.
  • Work with design on the redirected progress bar and celebration.
  • Agree the new activation definition and dashboard before the quarter starts.

I'm happy to walk through the numbers if useful.

Grades and run details

Decision model 82 · LLM judge 100
Decision model checks
  • failUses the supplied evidence correctly77%
  • passAddresses the actual decision89%
  • passRespects explicit constraints51%
  • passIdentifies material uncertainty91%
  • passAvoids unsupported claims35%
  • passProduces the required deliverable65%
  • passComputes retention by behaviour46%
  • passTests before trusting the correlation95%
  • passActivation defined by what predicts retention100%
  • failGets the base of every number right56%
  • passGets people to value sooner71%
Run
Run
#1
API response time
26 s
Submitted
6 Oct 2026

Needs more than a quick edit6 of 11 checks passed

The output lacks a causation caveat and test proposal, and makes unsupported causal claims; a PM would need to add those before using it.

Output

To: Omar, Head of Product From: PM, Onboarding Subject: Onboarding Review & Activation Strategy

---

Executive Summary

Our current activation rate (61%) is flat because we are measuring and optimizing the wrong milestone. Completing our setup checklist barely moves the needle on long-term retention.

To drive retention, we must redefine activation around core product value—connecting a bank and categorising transactions—and reorient our onboarding funnel and quarterly goals to support it.

---

1. What Activation Should Mean (and the Evidence)

Activation should represent the “Aha!” moment: the point where a user experiences Tidepool’s core value proposition and forms a lasting habit.

Currently, we define activation as completing the setup checklist (name account, pick theme, set savings goal, enable notifications). The data shows this is a vanity metric:

  • Baseline Day 60 Retention: 28.0% (1,120 / 4,000 sign-ups)
  • Checklist Completion: 30.4% Day 60 retention (742 / 2,438) — a negligible +2.4 percentage point lift over baseline.

By contrast, actions tied to our core utility (seeing where money goes) dramatically predict retention: * Connected a bank account: 48.2% Day 60 retention (964 / 2,000) * Categorised 10+ transactions: 64.6% Day 60 retention (891 / 1,380) * Connected bank AND categorised 10+ transactions: 78.0% Day 60 retention (842 / 1,080)

Users who complete both core actions retain at nearly triple the baseline rate. In fact, this group accounts for 75.2% of all retained users at Day 60 (842 of 1,120), despite representing only 27% of initial sign-ups.

New Activation Metric: > A user connects a bank account and categorises at least 10 transactions within their first 7 days.

---

2. Design Proposal & Next Quarter’s Goal

#### The Design Proposal (Celebration Screen & Progress Bar) The design team’s proposal is solving the wrong problem. Adding progress bars and celebration screens to cosmetic tasks (picking themes, naming accounts) creates artificial friction before users experience any real value. We are gamifying administrative overhead instead of utility.

Recommendation: Repurpose the design team’s work, but attach it to the real value milestone (e.g., celebrating when their first 10 transactions are imported and categorized).

#### Next Quarter’s Goal (Raise Checklist Completion from 61% to 75%) We should retire this goal immediately.

Even if we hit 75% checklist completion, model projections indicate Day 60 retention would increase by less than 1%. Optimizing an arbitrary checklist delivers zero business impact.

Proposed New Goal for Next Quarter: > Increase the core Activation Rate (Bank Connected + 10 Categorised Transactions) from 27% to 40% of sign-ups.

---

3. What to Change in Onboarding

Our onboarding funnel currently bleeds users before they experience core value: 50% drop off before connecting a bank, and only 34% categorize 10 transactions. We must clear the runway to value:

1. Eliminate the Cosmetic Checklist from First-Run Flow * Move “pick a theme” and “name your account” to Settings/Profile. * Defer “enable notifications” and “set a savings goal” until after the user sees their financial data.

2. Lead with Bank Connection * Prompt bank linking immediately post-registration. * Pre-frame the security and the payoff clearly: “Connect your account to see your last 30 days of spending instantly.”

3. Accelerate Time-to-Value (TTV) on Categorisation * Upon successful bank connection, immediately import transactions and guide the user through a rapid, swipeable 10-transaction categorisation flow (or auto-categorise and prompt a one-tap review). * Conclude with an instant visual breakdown: “Here’s where your money went this month.” This is Tidepool’s true “Aha!” moment.

4. Contextual Budgeting * Only prompt users to create a budget after categorisation. Budgeting retention is low in week one (33.3%) because users cannot budget accurately without understanding their baseline spend.

Next Steps

  1. Align leadership on the new Activation definition.
  2. Re-scope the design sprint from checklist gamification to a streamlined "Connect & Review 10 Transactions" flow.
  3. Update the team OKR dashboard for next quarter.

Grades and run details

Decision model 68 · LLM judge 58
Decision model checks
  • failUses the supplied evidence correctly24%
  • passAddresses the actual decision56%
  • passRespects explicit constraints49%
  • failIdentifies material uncertainty71%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable68%
  • passComputes retention by behaviour71%
  • failTests before trusting the correlation95%
  • passActivation defined by what predicts retention100%
  • passGets the base of every number right19%
  • passGets people to value sooner84%
Run
Run
#1
API response time
20 s
Submitted
6 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 5

Addresses the actual decisionRightRightMixed
Haiku 5.5 · API

Commits to a clear activation definition, specific onboarding changes, and a verdict on the design proposal and next quarter's goal, with conditions for change.

Sonnet 5.5 · API

The output commits early to a clear activation definition (connect bank and categorise 10+), gives a ranked set of changes, and says what would change the call (A/B test results).

Gemini 3.8 Flash · API

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

Identifies material uncertaintyRightRightWrong
Haiku 5.5 · API

Explicitly notes that retention figures are correlations and proposes A/B tests and a holdout to resolve the causal uncertainty.

Sonnet 5.5 · API

It explicitly names the correlation vs. causation risk, notes the data is observational, and proposes specific tests (bank-first A/B, auto-categorisation) with retention as the measure.

Gemini 3.8 Flash · API

The output does not name any unknowns that could change the decision or say how they would be resolved.

Avoids unsupported claimsMixedRightWrong
Haiku 5.5 · API

States 'App-open frequency is an outcome, not a cause' as an established fact, but only correlation is shown in the data; this causal direction is an unsupported interpretation.

Sonnet 5.5 · API

Interpretations like 'the value moment is seeing where the money goes' are presented as conclusions from the data, not as established fact, and the overall review is cautious about causation.

Gemini 3.8 Flash · API

It presents causal claims (e.g., checklist steps create artificial friction, low budgeting retention is because users lack baseline spend) as fact without labelling them as hypotheses.

Tests before trusting the correlationRightRightWrong
Haiku 5.5 · API

Notes correlation risk and proposes a specific A/B test of bank-first onboarding and a holdout experiment with retention as the measure.

Sonnet 5.5 · API

It notes the correlation risk and proposes a specific A/B test (bank-first vs. current order) with retention as the outcome, plus tracking overall retention.

Gemini 3.8 Flash · API

The output does not note that the observed relationships are correlations and does not propose a test to drive the behaviour and measure retention.

Gets the base of every number rightMixedMixedRight
Haiku 5.5 · API

All derived percentages (24%, 30%, 78%, 6%, 48%, 13%, 16%, etc.) use the correct denominators, with no ambiguous bases.

Sonnet 5.5 · API

All derived percentages and step-to-step changes use the correct denominators (e.g., 27% from 1,080/4,000, 78% from 842/1,080), and ambiguous bases are stated.

Gemini 3.8 Flash · API

All percentages and rates use the correct denominators from the data, and the bases are clear.

All mixed 1

Uses the supplied evidence correctlyMixedMixedMixed
Haiku 5.5 · API

Every factual statement about the current situation is taken from the supplied context or follows from correct arithmetic.

Sonnet 5.5 · API

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

Gemini 3.8 Flash · API

All statements about the current situation are directly from the supplied context or correctly derived from it.

All got right 5

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

Output is a memo to Omar, addresses all parts of the brief, and is well within the 800-word limit.

Sonnet 5.5 · API

The output is a review memo addressed to Omar, covers all requested topics, and is well under 800 words.

Gemini 3.8 Flash · API

The output is a memo to Omar, under 800 words, and addresses all requested elements.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

Memo format, addressed to Omar, includes activation definition, evidence, changes, design proposal view, and next quarter goal, all under the word limit.

Sonnet 5.5 · API

The output is a complete, actionable review in memo form, within the word limit, that Omar could act on without filling major gaps.

Gemini 3.8 Flash · API

The review is complete, in memo form, within the word limit, and a PM could act on it with light edits.

Computes retention by behaviourRightRightRight
Haiku 5.5 · API

Computes retention for each first-week behaviour including checklist (≈30%), bank+cat (78%), and baseline (28%), and compares them.

Sonnet 5.5 · API

It computes day-60 retention for each first-week behaviour (checklist ~30%, bank+cat ~78%, average 28%) and compares them correctly.

Gemini 3.8 Flash · API

Day-60 retention rates are correctly computed: checklist ~30%, bank+categorise ~78%, average 28%.

Activation defined by what predicts retentionRightRightRight
Haiku 5.5 · API

Defines activation by retention (bank+cat 78%), not checklist; ranks fixes by their effect on that activation behaviour.

Sonnet 5.5 · API

Activation is defined by the behaviour that predicts retention (bank + 10 categorised), and fixes are judged by their effect on that event, not on checklist completion.

Gemini 3.8 Flash · API

Activation is defined by the behaviour that predicts retention (bank connect + 10 categorised), and fixes are ranked by their effect on reaching that event.

Gets people to value soonerRightRightRight
Haiku 5.5 · API

Recommended fixes move bank connection earlier, pre-suggest categories, and demote or defer theme/name/notifications, cutting steps before value.

Sonnet 5.5 · API

The main fixes cut or defer steps before value (move bank connection first, defer theme/notifications/name) and give a warm start (auto-categorise, one-tap confirmations).

Gemini 3.8 Flash · API

The main fixes cut or defer steps before first value (cosmetic checklist, notifications, savings goal) and lead directly to bank connection and categorisation.

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 79% of 53 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT85.995.52None
2Sonnet 5.5withAPI78.4100.02None
3GPT-6.1 SolwithAPI85.990.92None
4GPT-6 LunawithAPI90.081.82None
5Haiku 5.5withAPI75.986.72None
6Opus 5.5withClaude88.478.42None
7Gemini 3.8 FlashwithAPI66.651.92None
8Gemini 3.5 Flash-LitewithGemini52.539.42None

What you get for the cost

GPT-6 Luna · API scores within 5 points of GPT-6 Astra · ChatGPT at about a hundredth 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 scoreGPT-6 Astra · ChatGPT: task score 91, 122× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 90, 32× the cheapestGPT-6.1 Sol · API: task score 89, 15× the cheapestGPT-6 Luna · API: task score 86, the cheapestHaiku 5.5 · API: task score 82, 1.8× the cheapestOpus 5.5 · Claude: task score 82, 101× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 56, 13× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 44, 3.4× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTSonnet 5.5 · APIGPT-6.1 Sol · APIGPT-6 Luna · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API861.0×
Haiku 5.5 · API821.8×
Gemini 3.5 Flash-Lite · Gemini443.4× (partly estimated)
Gemini 3.8 Flash · API5613×
GPT-6.1 Sol · API8915×
Sonnet 5.5 · API9032×
Opus 5.5 · Claude82101× (partly estimated)
GPT-6 Astra · ChatGPT91122× (partly estimated)

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

Reviewing a signup and onboarding flow that is losing users.

Why it matters

Anyone can list fifty UX nits. The job is finding the two that explain the drop-off, backed by the funnel data supplied.

What good looks like

  • Ties each issue to the funnel data
  • Prioritises by likely impact
  • Defines activation as the behaviour that predicts retention, not finishing onboarding

Deliberately not measured

  • Accessibility audit completeness
  • Visual redesign
Capability tested

Consequential critique

The failure we’re looking for

A generic UX checklist

Grading

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