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 18 graded outputs by 9 models. 67% 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. Avoids unsupported claims26% pass
    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.
    Haiku 5.5 · API · The checklist isn't the moment
  2. Identifies material uncertainty61% pass
    Does not name specific unknowns that could change the decision, only suggests tracking metrics before expanding.
    GPT-6 Luna · API · Analytics tool losing users at setup
  3. Uses the supplied evidence correctly64% 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

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 · v1.1

The outputs

9 setups answered this brief.

Needs more than a quick edit8 of 11 checks passed

The output lacks an explicit correlation-causation caveat and a proposed test, which a PM would need to add before it could be acted upon confidently.

Output

To: Omar (Head of Product) From: PM, Onboarding Subject: Onboarding Review: Redefining Activation and Q4 Strategy

Executive Summary

Our current activation metric—completing the setup checklist (61%)—is a vanity metric. Analysis of August’s 4,000 sign-ups shows that users who complete the checklist retain at Day 60 at roughly the same rate as the overall average (30.4% vs. 28.0%). Worse, the checklist sits before our core value proposition, causing 39% of users to drop off before we even ask them to connect a bank.

We need to redefine activation around real product value, scrap the current Q4 checklist goal, and redesign onboarding to get users straight to their financial data.

---

1. The Evidence: What Actually Drives Retention?

Activation should measure the moment a user experiences Tidepool’s core value ("see where your money goes") in a way that predicts long-term habit formation. Looking at Day 60 retention from August’s cohort (4,000 total sign-ups), the data is clear:

  • All sign-ups: 28.0% D60 retention (1,120 / 4,000)
  • Completed setup checklist (61% of users): 30.4% retention (742 / 2,438)
  • Created a budget (31% of users): 33.3% retention (410 / 1,230)
  • Opened the app 4+ days (55% of users): 39.9% retention (870 / 2,180)
  • Connected a bank account (50% of users): 48.2% retention (964 / 2,000)
  • Categorised 10+ transactions (34% of users): 64.6% retention (891 / 1,380)
  • Connected a bank AND categorised 10+ transactions (27% of users): 78.0% retention (842 / 1,080)

Key Takeaways from the Data: 1. The setup checklist does not predict retention. Doing administrative chores (naming an account, picking a color theme) does not deliver financial clarity. Users who skip or abandon the checklist aren't failing to activate; they are likely just bored. 2. Core value is delivered through bank connection and transaction categorisation. When a user connects their bank and categorises 10+ transactions in week one, their Day 60 retention jumps to 78.0%. Notably, this 27% of users accounts for 75% of all retained users at Day 60 (842 of 1,120). 3. Crucial caveat on Bank vs. Manual: Notice that 1,380 users categorised 10+ transactions, but only 1,080 did so and connected a bank. For the 300 users who categorised 10+ transactions without connecting a bank (presumably via manual entry or CSV), D60 retention was only 16.3% (49 / 300). Categorising alone isn’t enough; automated bank syncing is essential.

---

2. Redefining Activation & Next Quarter’s Goal

  • New Activation Definition: "Connected a bank account AND categorised 10+ transactions within 7 days of sign-up."
  • Verdict on Q4 Goal: We must cancel the goal of raising checklist completion from 61% to 75%. Even if we hit 75%, D60 retention wouldn't budge.
  • New Q4 Goal: Increase true activation (Bank + 10 Categorised Transactions) from 27.0% to 38.0%. Because this cohort retains at 78% (though we should monitor for slight dilution as we push less-motivated users through the funnel), moving this needle is the highest-leverage way to lift overall D60 retention above 28%.

---

3. Assessment of the Design Team's Proposal

The design team proposed adding a progress bar and a celebration screen to the setup checklist. We should not build this for the current checklist.

Optimising a step that adds friction without adding value is wasted effort. However, we should repurpose the design team’s UI patterns for the Categorisation step. A progress bar ("4 of 10 transactions categorised to unlock your spending breakdown") and a celebration screen that immediately reveals their visualised spending insights will directly drive our new activation metric.

---

4. What to Change in Onboarding

Right now, our funnel is backwards: we ask for customization before showing value. We should restructure the flow as follows:

  1. Kill the upfront setup checklist: Remove "name your account" and "pick a theme" from onboarding entirely (default these and move them to Settings). Delay "set a savings goal" and "enable notifications" until after the user has seen their spending data, when those prompts actually have context.
  2. Make "Connect a Bank" Step 1: Immediately after sign-up, prompt bank connection. Currently, we lose 50% of users here (many because they dropped off at the checklist first). Moving this to Step 1—and adding trust signals around security and read-only access—will immediately increase the top of our true value funnel.
  3. Guided "First 10" Categorisation Flow: Once the bank syncs, drop users into a focused, swipe/tap experience (using Design's progress bar) to categorise their 10 most recent transactions, culminating in an immediate "Aha!" dashboard showing where their money went this month.

Grades and run details

Decision model 77 · LLM judge 67
Decision model checks
  • passUses the supplied evidence correctly25%
  • passAddresses the actual decision49%
  • passRespects explicit constraints55%
  • partialIdentifies material uncertainty80%
  • failAvoids unsupported claims82%
  • passProduces the required deliverable77%
  • passComputes retention by behaviour72%
  • failTests before trusting the correlation82%
  • passActivation defined by what predicts retention100%
  • passGets the base of every number right20%
  • passGets people to value sooner91%
Run
Run
#1
API response time
12 s
Submitted
13 Oct 2026

Needs more than a quick edit7 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 73 · LLM judge 58
Decision model checks
  • passUses the supplied evidence correctly27%
  • passAddresses the actual decision31%
  • passRespects explicit constraints56%
  • failIdentifies material uncertainty69%
  • failAvoids unsupported claims85%
  • passProduces the required deliverable76%
  • passComputes retention by behaviour74%
  • failTests before trusting the correlation95%
  • passActivation defined by what predicts retention100%
  • passGets the base of every number right34%
  • passGets people to value sooner86%
Run
Run
#1
API response time
20 s
Submitted
6 Oct 2026

Usable with a quick edit9 of 11 checks passed

The review is clear, evidence-based, and covers all required points; a PM could act on it with only minor wording tweaks.

Output

Onboarding review: activate on financial value, not setup

Recommendation: Replace checklist completion as our activation metric with connecting a bank and categorising at least 10 transactions within seven days of signup. Reorient onboarding around that outcome. The celebration screen and progress bar are secondary; making the current checklist more engaging risks improving the wrong metric.

What activation should mean

For Tidepool, activation should mean a user has taken the first meaningful steps toward understanding their spending—not finished configuring the app.

Bank connection brings their financial data into Tidepool; categorisation makes that data useful. Together, these are our strongest available proxy for experiencing core value. This is a working definition, not proof that exactly 10 categorisations creates a lasting habit. We should validate the threshold and whether users actually understand something useful about their money.

Budget creation can follow that first insight. The evidence does not support making it a mandatory activation requirement today.

What the evidence says

Among August’s 4,000 signups, overall day-60 retention was 28%.

First-week behaviourUsersDay-60 retention
------:---:
Completed setup checklist2,43830.4%
Connected a bank2,00048.2%
Categorised 10+ transactions1,38064.6%
Connected a bank and categorised 10+1,08078.0%
Created a budget1,23033.3%
Opened the app on 4+ days2,18039.9%

The checklist has weak separation: completers retained at 30.4%, versus 24.2% for non-completers. Its 61% completion rate is therefore a poor headline measure of delivered value.

By contrast, only 27% of signups connected a bank and categorised 10+ transactions, but they accounted for 842 of 1,120 retained users—75% of everyone retained. Users who did not achieve both retained at 9.5%.

These are observational associations. More motivated users may both complete these actions and remain active; we cannot promise 78% retention simply by pushing people through them. Behaviour groups also overlap, so the reported steps should not be treated as a strictly sequential funnel. Before testing, reconcile the rounded onboarding figures with event counts and standardise the day-60 activity definition.

What to change

  1. Remove configuration as a gate to value. Defer theme selection and account naming; make savings goals optional. Request notification permission when there is a clear benefit, such as a relevant budget alert—not as checklist work.
  1. Bring bank connection forward. Explain why access is needed, what data is used, and how users retain control. Instrument connection starts, provider errors, unsupported banks, abandonment and successful imports. Preserve a skip/retry path rather than forcing consent.
  1. Guide users from imported transactions to spending insight. Offer a short, low-effort review of suggested categories. Let users confirm or correct them, then show an understandable spending summary. Do not require ten arbitrary manual edits merely to satisfy the metric.
  1. Offer a budget as the next useful action. Use the spending summary to suggest a realistic starting budget, rather than asking users to invent one before seeing their finances.

Test the redesigned flow against today’s onboarding. Measure seven-day activation first, then downstream retention; monitor connection failures, abandonment and user-reported trust and usefulness.

Design proposal and next quarter’s goal

A progress bar is worth testing if it tracks meaningful steps toward understanding spending. It should not encourage completion of low-value setup tasks. A celebration screen belongs after a genuine milestone and should reinforce the insight gained, not interrupt access to it. Neither should take priority over bank-connection and categorisation friction.

Replace the 61% → 75% checklist goal with a goal for the new activation definition. I propose 27% → 35% within seven days, provisionally, subject to instrumentation validation and early experiment results. That is a planning target—not a forecast supported by this dataset.

Keep checklist completion as a diagnostic. Judge success by increased meaningful activation without sacrificing trust or retention, and confirm the retention effect once next quarter’s cohorts mature.

Grades and run details

Decision model 77 · LLM judge 100
Decision model checks
  • failUses the supplied evidence correctly77%
  • passAddresses the actual decision80%
  • passRespects explicit constraints38%
  • passIdentifies material uncertainty80%
  • partialAvoids unsupported claims45%
  • passProduces the required deliverable40%
  • passComputes retention by behaviour33%
  • passTests before trusting the correlation98%
  • passActivation defined by what predicts retention100%
  • failGets the base of every number right80%
  • passGets people to value sooner82%
Run
Run
#1
Time to output
30 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 6

Uses the supplied evidence correctlyRightRightMixed
Gemini 4 Argon · API

All factual claims about current numbers and performance are taken correctly from the supplied context or follow from arithmetic.

Gemini 3.8 Flash · API

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

GPT-6 Astra · ChatGPT

All factual claims about the current situation are directly supported by the supplied context or correct arithmetic.

Addresses the actual decisionRightMixedRight
Gemini 4 Argon · API

Output commits early and unambiguously to redefining activation as bank + 10 categorised and abandoning the checklist goal.

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.

GPT-6 Astra · ChatGPT

Commits early to a clear activation definition and changes, and states that validation and experiment results would change the call.

Identifies material uncertaintyWrongWrongRight
Gemini 4 Argon · API

Does not name the key uncertainty (correlation vs causation) or say how it would be resolved; only mentions dilution risk without a resolution plan.

Gemini 3.8 Flash · API

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

GPT-6 Astra · ChatGPT

Names the observational nature, threshold uncertainty, and need for validation; says how to resolve them.

Avoids unsupported claimsWrongWrongRight
Gemini 4 Argon · API

Presents cause-effect interpretations ('causing 39% to drop off', 'users are likely just bored') as fact, without labelling them as hypotheses.

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.

GPT-6 Astra · ChatGPT

Hypotheses and forecasts are clearly labelled as such, and confident claims are supported by the evidence.

Tests before trusting the correlationWrongWrongRight
Gemini 4 Argon · API

Does not note that the retention links are correlations, nor propose a test to drive the activation behaviour and check 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.

GPT-6 Astra · ChatGPT

Notes the correlation risk and proposes a specific test (redesigned flow vs. today, measuring retention) to check causation.

Gets the base of every number rightRightRightMixed
Gemini 4 Argon · API

All percentages and rates are correctly derived from the supplied data, and denominators are clear (e.g., retention rates use the behaviour cohort as base).

Gemini 3.8 Flash · API

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

GPT-6 Astra · ChatGPT

All percentages are computed from the correct denominators, and bases are stated or clear from context.

All got right 5

Respects explicit constraintsRightRightRight
Gemini 4 Argon · API

Output is a memo to Omar, under 800 words, covers the requested components, and respects the form and reader.

Gemini 3.8 Flash · API

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

GPT-6 Astra · ChatGPT

The review is under 800 words, addresses Omar, and covers all requested elements.

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

Produced a complete, usable memo within the length limit, addressed to Omar as a review.

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.

GPT-6 Astra · ChatGPT

The output is a complete review with recommendation, evidence, changes, and view on proposal and goal, usable as is.

Computes retention by behaviourRightRightRight
Gemini 4 Argon · API

Correctly computed Day-60 retention for each behaviour, including 28% overall, 30.4% checklist, 48.2% bank, 64.6% categorise, 78.0% both.

Gemini 3.8 Flash · API

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

GPT-6 Astra · ChatGPT

Computes day-60 retention for each first-week behaviour and compares with the 28% average.

Activation defined by what predicts retentionRightRightRight
Gemini 4 Argon · API

Defines activation by retention prediction (bank + 10 categorised) and ranks fixes by their effect on that event.

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.

GPT-6 Astra · ChatGPT

Defines activation by retention (bank + categorise 10+) and ranks fixes by their effect on that event.

Gets people to value soonerRightRightRight
Gemini 4 Argon · API

Recommends removing upfront checklist steps and making bank connection step 1, directly shortening the path to first value.

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.

GPT-6 Astra · ChatGPT

Main fixes cut or defer configuration steps before value and bring bank connection and categorisation forward.

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 72% of 53 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI83.6100.02None
2GPT-6 AstrawithChatGPT86.195.52None
3GPT-6.1 SolwithAPI83.690.92None
4Haiku 5.5withAPI88.086.72None
5GPT-6 LunawithAPI92.781.82None
6Opus 5.5withClaude81.478.42None
7Gemini 4 ArgonwithAPI73.665.22None
8Gemini 3.8 FlashwithAPI71.451.92None
9Gemini 3.5 Flash-LitewithGemini60.039.42None

What you get for the cost

GPT-6 Luna · API scores within 5 points of Sonnet 5.5 · API at about a thirtieth of the cost.

Task score against relative cost for each setup. The table below has the same figures.4050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreSonnet 5.5 · API: task score 93, 32× the cheapestGPT-6 Astra · ChatGPT: task score 91, 122× the cheapest (1 of 2 outputs estimated)GPT-6.1 Sol · API: task score 88, 15× the cheapestHaiku 5.5 · API: task score 88, 1.8× the cheapestGPT-6 Luna · API: task score 88, the cheapestOpus 5.5 · Claude: task score 79, 101× the cheapest (1 of 2 outputs estimated)Gemini 4 Argon · API: task score 68, 15× the cheapestGemini 3.8 Flash · API: task score 59, 13× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 47, 3.4× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · APIGPT-6 Astra · ChatGPTGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Luna · APIOpus 5.5 · ClaudeGemini 4 Argon · APIGemini 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 · API881.0×
Haiku 5.5 · API881.8×
Gemini 3.5 Flash-Lite · Gemini473.4× (partly estimated)
Gemini 3.8 Flash · API5913×
GPT-6.1 Sol · API8815×
Gemini 4 Argon · API6815×
Sonnet 5.5 · API9332×
Opus 5.5 · Claude79101× (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