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 edit8 of 11 checks passed

Unsupported claims about the value of naming/theme and the visibility of bank-connection drop-off would need correction or removal before use.

Output

Onboarding review: what activation should mean

To: Omar | From: Onboarding PM | Data: August cohort (4,000 sign-ups), day-60 retention

Summary

Activation is flat because we are measuring the wrong thing. Finishing the setup checklist barely predicts whether someone stays. Connecting a bank and categorising 10+ transactions in week one does predict it strongly. I recommend we redefine activation around that, rebuild onboarding to get people there faster, and replace next quarter's checklist goal.

What activation should mean

Activated = connected a bank AND categorised 10+ transactions in the first week.

This is the moment Tidepool delivers its core promise: seeing where your money goes. Only 27% of August sign-ups reached it, so our real activation rate is less than half the 61% we report.

The evidence

Day-60 retention by first-week behaviour (baseline: 28% of all sign-ups retained):

First-week behaviourUsersRetained if did itRetained if didn't
Completed setup checklist2,438 (61%)30%24%
Created a budget1,230 (31%)33%26%
Opened app 4+ days2,180 (55%)40%14%
Connected a bank2,000 (50%)48%8%
Categorised 10+ transactions1,380 (35%)65%9%
Bank + 10+ categorised1,080 (27%)78%10%

Five things stand out:

  1. The checklist is a weak signal. Completers retain at 30% versus 24% for non-completers, close to the 28% baseline. 1,696 people completed it and still churned.
  2. Bank plus categorisation is the strongest signal. Users who did both retain at 78%. They make up 27% of sign-ups but 75% of everyone retained at day 60.
  3. Each half is weak on its own. About 920 people connected a bank without categorising, and they retained at 13%. About 300 categorised without a bank, and they retained at 16%. The value comes from real transactions that the user has made sense of.
  4. The biggest leaks are at the steps that matter. Half of sign-ups never connect a bank. Of those who do, 46% never reach 10 categorised transactions.
  5. Budget creation isn't a good activation metric. Its retention lift is small and only 31% do it. That is worth investigating separately for a budgeting app.

Caveats: this is one cohort and the data is correlational. Motivated users may simply do more of everything. The gaps are large enough to act on, but we should confirm cause with experiments rather than assume it.

What to change in onboarding

  1. Put bank connection first. Today it comes after a four-item checklist that we lose 39% of users on. Naming the account and picking a theme add no value at signup and can move to settings. Ask for notifications later, once there is something worth notifying about, such as a first spending summary. The savings goal becomes optional after categorisation.
  2. Make connecting a bank feel safe and fast. Address security concerns at the moment of asking, with plain-language reassurance about read-only access. Offer the most popular banks up front. Instrument the drop-off between starting and finishing the connection, which we can't currently see.
  3. Guide the first 10 categorisations straight after connecting. Show recent transactions with suggested categories so each one is a one-tap confirm. End with an immediate payoff: "Here's where your money went this month."
  4. Recover people who stall. Send a reminder to anyone who connected but hasn't categorised within 48 hours. That group is about 920 people a month retaining at 13%.

Each change should ship as an A/B test with day-60 retention as the outcome. That is how we confirm the relationship is causal.

The design team's proposal

A celebration screen and a progress bar are reasonable patterns, but aimed at the checklist they would polish a step that doesn't drive retention. I'd ask design to point the same ideas at the new milestone:

  • A progress bar for "connect bank, then categorise 10 transactions."
  • A celebration on the 10th categorisation, paired with the first spending insight.

That is a small redirect of their work, not a rejection of it.

Next quarter's goal

I recommend we retire the 61% → 75% checklist goal. Even if we hit it, and every extra completer gained the full 6-point retention gap (which is generous), that would mean roughly 30–35 more retained users per 4,000 sign-ups.

Compare moving just 10% of sign-ups (400 people) into the bank-plus-categorised group. If even a fraction of that 68-point retention gap is causal, the payoff is several times larger.

Proposed goal: raise week-one bank connection plus 10+ categorised transactions from 27% to 35%, with day-60 retention as the guardrail metric. We should set the final target once the first experiment shows how much the metric moves.

Next steps

  • This week: confirm the new activation definition with you and analytics, and backfill it for June and July to check it holds across cohorts.
  • Weeks 2–4: run an A/B test of bank-first onboarding with the checklist deferred, and add instrumentation to the bank connection flow.
  • Weeks 4–8: test the guided categorisation flow and the redirected progress bar and celebration screen.

Claims the judge couldn’t find in the brief

  • Naming the account and picking a theme add no value at signup.
  • We can't currently see drop-off between starting and finishing the bank connection.

Grades and run details

Decision model 73 · LLM judge 75
Decision model checks
  • failUses the supplied evidence correctly61%
  • passAddresses the actual decision91%
  • passRespects explicit constraints43%
  • passIdentifies material uncertainty79%
  • failAvoids unsupported claims23%
  • passProduces the required deliverable64%
  • passComputes retention by behaviour40%
  • passTests before trusting the correlation100%
  • passActivation defined by what predicts retention100%
  • failGets the base of every number right74%
  • passGets people to value sooner83%
Run
Run
#1
Time to output
32 s
Submitted
5 Oct 2026

Needs more than a quick edit4 of 11 checks passed

The output misstates the baseline activation rate (34% instead of 27%) and lacks a causation test, so a PM would need to re-check the numbers and add a test plan.

Output

To: Omar, Head of Product From: PM, Onboarding Date: September 2024 Subject: Onboarding Review & Activation Strategy

---

1. What Activation Should Mean for Us

Currently, our definition of activation—completing a checklist of cosmetic preferences (name, theme, savings goal, notifications)—is flawed. It measures vanity completion rather than value realization.

As a personal finance app, Tidepool delivers core value when users connect their financial reality and engage with it. Looking at our August cohort (4,000 sign-ups), the data tells a clear story about Day 60 retention:

  • Checklist completers retain at 30.4% (742/2,438).
  • Bank connectors retain at 48.2% (964/2,000).
  • Users who categorise 10+ transactions retain at 64.5% (891/1,380).
  • Users who do BOTH (Connect + Categorise) retain at 78.0% (842/1,080).

Recommendation: We must redefine "Activation" from “completed the setup checklist” to “Connected a bank AND categorised at least 10 transactions within their first week.” This anchors our success metric to actual product utility, shifting our focus from cosmetic setup to establishing long-term financial habits.

---

2. The Evidence: Where We Are Losing Users

Our current funnel drops off steeply: 1. Sign up: 4,000 (100%) 2. Setup checklist: 2,440 (61%) 3. Connect a bank: 2,000 (50%) 4. Categorise 10 transactions: 1,360 (34%)

The biggest leak is bank connection (dropping 11 percentage points right after the checklist) and transaction categorisation (dropping another 16 points).

Interestingly, the checklist itself acts as a barrier. Users are spending cognitive load on naming accounts and picking themes before experiencing the core product. Meanwhile, power features like "Created a budget" (33% retention) and opening the app 4+ days are strong secondary indicators, but bank linking and categorisation are the undisputed gateways to retention.

---

3. What to Change in Onboarding

To drive our new definition of activation, we need to restructure the first-time user experience (FTUX):

  • Kill the upfront setup checklist: Strip out non-essential steps (naming accounts, picking themes, notifications) from the critical path. Move them to profile settings or prompt for them after value has been proven.
  • Accelerate time-to-bank: Move bank connection immediately following sign-up. Users downloaded Tidepool to see their money; let them see it instantly via Plaid/Aggregator integration.
  • Gamify the "aha" moment: Guide users smoothly from bank connection directly into categorising their first 10 transactions. Use smart auto-categorisation with a clear, frictionless bulk-approval or swipe UI to make reaching the 10-transaction milestone effortless.

---

4. Verdict on the Design Proposal and Next Quarter's Goal

The Design Team’s Proposal (Celebration screen + Progress bar): * Verdict: Reject in its current form. Adding a progress bar and celebration screen for the existing checklist optimizes the wrong metric. It puts lipstick on a superficial activation loop. * Pivot: If we redesign onboarding to focus on connecting a bank and categorising transactions, then we can use a celebration screen and progress bar—not to celebrate picking a theme, but to celebrate linking an account and hitting that crucial 10-transaction milestone.

Next Quarter's Goal (Raise checklist completion from 61% to 75%): * Verdict: Abandon this goal. Optimizing checklist completion is a local maximum that misaligns product and user success. Chasing 75% checklist completion will likely result in higher drop-offs further down the funnel and flat-to-declining Day 60 retention. * New Goal: We should replace this with a target to raise our true activation rate (Bank Connected + 10 Transactions Categorised) from the current baseline (~34%) to 50% next quarter. This directly correlates with our actual business health and user retention.

Claims the judge couldn’t find in the brief

  • The checklist itself acts as a barrier.
  • Users are spending cognitive load on naming accounts and picking themes before experiencing the core product.
  • Bank linking and categorisation are the undisputed gateways to retention.

Grades and run details

Decision model 50 · LLM judge 33
Decision model checks
  • failUses the supplied evidence correctly37%
  • passAddresses the actual decision28%
  • passRespects explicit constraints25%
  • failIdentifies material uncertainty91%
  • failAvoids unsupported claims85%
  • passProduces the required deliverable41%
  • partialComputes retention by behaviour33%
  • failTests before trusting the correlation98%
  • passActivation defined by what predicts retention98%
  • failGets the base of every number right64%
  • passGets people to value sooner92%
Run
Run
#1
Time to output
5 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 correctlyRightWrongWrong
Gemini 4 Argon · API

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

Opus 5.5 · Claude

Two claims about the current situation ('naming and theme add no value at signup' and 'we can't currently see drop-off') are not in the supplied context and are not arithmetic derivations.

Gemini 3.5 Flash-Lite · Gemini

The output states 'the checklist itself acts as a barrier' and similar causal claims as facts, which are not in the supplied context and do not follow by arithmetic.

Addresses the actual decisionRightRightMixed
Gemini 4 Argon · API

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

Opus 5.5 · Claude

Commits early to redefining activation to bank + categorised, changing onboarding, and retiring the checklist goal, framed for Omar, with conditions (experiments) that would change the call.

Gemini 3.5 Flash-Lite · Gemini

The output does not state what result or condition would change its recommendation, so it fails to commit with a clear change condition.

Identifies material uncertaintyWrongRightWrong
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.

Opus 5.5 · Claude

Names the correlation vs causation risk, bounds it with the data, and proposes A/B tests with day-60 retention to resolve it.

Gemini 3.5 Flash-Lite · Gemini

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

Computes retention by behaviourRightRightWrong
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.

Opus 5.5 · Claude

Computes day-60 retention for each behaviour: checklist ~30%, bank+categorised ~78%, average 28%, and uses them.

Gemini 3.5 Flash-Lite · Gemini

The output does not compute the overall average retention (28%) and does not compare the subgroup rates to it.

Tests before trusting the correlationWrongRightWrong
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.

Opus 5.5 · Claude

Notes the correlation risk and proposes a specific A/B test driving the behaviour with retention as the measure.

Gemini 3.5 Flash-Lite · Gemini

The output does not note that the retention differences are correlations and does not propose a test to confirm causation.

Gets the base of every number rightRightMixedWrong
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).

Opus 5.5 · Claude

All derived percentages (30%, 78%, 27%, 13%, 16%, etc.) check out against the supplied data, and bases are stated or clear.

Gemini 3.5 Flash-Lite · Gemini

The baseline for the new activation rate is given as ~34%, but the correct rate for 'connected bank AND categorised 10+' is 1,080/4,000 = 27%.

All got wrong 1

Avoids unsupported claimsWrongWrongWrong
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.

Opus 5.5 · Claude

Presents 'naming and theme add no value' and 'we can't see drop-off' as established facts without labelling them as hypotheses or assumptions.

Gemini 3.5 Flash-Lite · Gemini

Interpretations like 'the checklist itself acts as a barrier' and 'undisputed gateways' are presented as established fact without being labelled as hypotheses.

All got right 4

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.

Opus 5.5 · Claude

The output is a review addressed to Omar, within 800 words, and the proposed changes would enforce the new activation definition in practice.

Gemini 3.5 Flash-Lite · Gemini

The output is a memo to Omar, under 800 words, and addresses the requested topics.

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

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

Opus 5.5 · Claude

Delivers a complete onboarding review memo to Omar, within the word limit, with actionable recommendations and next steps.

Gemini 3.5 Flash-Lite · Gemini

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

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.

Opus 5.5 · Claude

Defines activation by retention prediction (bank + categorised) and ranks fixes by their effect on that event, not on checklist completion.

Gemini 3.5 Flash-Lite · Gemini

Activation is redefined based on retention prediction (bank + categorise), and fixes are judged 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.

Opus 5.5 · Claude

Main fixes cut or defer steps before first value (move naming/theme to settings, defer notifications, make savings goal optional) and guide categorisation right after bank connection.

Gemini 3.5 Flash-Lite · Gemini

The main fixes cut or defer checklist steps before value and move bank connection and categorisation earlier.

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