Tasks / Leadership

Write a stakeholder update

Can the model turn messy project status into an honest update that leads with what the reader needs to know or decide?

Task type v1.2 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

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

Reliably right

Reports outcomes and learning, not just output100% pass
It leads with outcome metrics (ARR, support, usage) and reports a key learning (pipeline jump is a definition change, not demand growth), not just activities.
GPT-6 Astra · ChatGPT · On track, mostly
Leads with the honest headline100% pass
First slide headline includes the retention drop alongside the new-ARR beat, not just 'record quarter'.
GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
A real 7-slide deck100% pass

Where it slips

Uses the supplied evidence correctly51% pass
The year 2026 is not in the supplied context; all other factual claims are supported.
GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
Owns the problem53% pass
Does not state what the team has already done about the payments reliability miss or the NPS drop; only reports the problems.
GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
Flags the redefined pipeline58% pass
It names the redefinition but does not give the falling like-for-like trend (e.g., pipeline fell from $2.41M to $2.23M on the old definition).
GPT-6.1 Sol · API · On track, mostly

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.

Asks leaders only for the big calls
Does the update separate the decisions it needs from its readers (hard to reverse, high stakes) from the ones the team will make itself, and ask clearly for the former?
Passes when Names the decisions it needs from the readers, with a recommendation and a date, and says which calls the team is making itself.
Geoff CharlesLenny’s Podcast
Reports outcomes and learning, not just output
Does the update report progress as movement on the outcomes the team is accountable for and what it has learned, rather than as features shipped or dates hit?
Passes when Leads with the outcome metrics and what changed in the team's understanding, with shipping as supporting detail.
Melissa PerriLenny’s Podcast Christian IdiodiLenny’s Podcast

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 · Owns the problem · Ready for the reader as delivered · and 5 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 Staff PM for Ledgerly's Billing group. The quarterly business review with Lena (CEO) and the exec team is on 8 October; the deck goes out the day before as a pre-read. Using the material below, build the Q3 deck for Billing. The deck must: - Have at most 7 slides, each one 16:9. - Give every slide a headline that states its takeaway, not a topic (“NRR fell to 101% after the pricing change”, not “NRR”). - Report every OKR honestly, including the misses. - End with the decisions you need from the exec team, each with an owner and a date. - Use only Ledgerly's brand colours (below). Deliver it as a single self-contained HTML file: the slides stacked top to bottom, each exactly 1280 × 720 pixels, no external images, fonts or scripts, readable without JavaScript. Draw any charts in HTML or SVG. With the file, reply with a cover note to Lena of no more than 60 words.

What the model was given8 items: Ledgerly brand colours, Q3 OKRs (Finance sheet, final), What moved NRR, Launches, Customers, Team and budget, Decisions the exec team needs to make, Draft headline from the VP of Sales

Ledgerly brand coloursInk #1B2A41 (text and headlines). Teal #0F8B8D (the primary brand colour: accents, charts, on-track items). Amber #F2A541 (only for items that are at risk or missed; never decorative). Mist #E8EEF2 (backgrounds, table rules). White #FFFFFF. No other colours, no gradients. Body text at least 18px.

Q3 OKRs (Finance sheet, final)1. New ARR: target $2.40M, actual $2.61M (109%). 2. Net revenue retention: target 108%, actual 101.3% (Q2: 106.0%). 3. Activation: 45% of new sign-ups send a first invoice within 7 days; actual 38% (Q2: 36%). 4. Payments reliability: 99.95% target, actual 99.91% (one incident: 2 September, 4 hours of degraded payments during the card-fraud response).

What moved NRRPricing moved from per-seat to usage-based on 1 September. Three of the top 20 accounts downgraded in September (−$540k ARR combined), citing the new pricing. Harbor & Co ($310k ARR) churned to a competitor, citing no bulk invoicing. The Customer Success dashboard shows NRR at 102%, because it excludes churned customers under $5k ARR; Finance's 101.3% is the figure the board sees.

LaunchesBulk invoicing: promised for 12 October, now 26 October (23 October at best), because Payments paused the batch-send API during the card-fraud incident. The 14 October customer webinar (1,100 registered) has been moved to 28 October. AI-drafted support replies: live on the three non-billing queues since 21 September; median first-response time down 41%. Billing queue waits for updated pricing data and a refund-language block; eval re-run due 20 October.

CustomersNPS: 34 (Q2: 41). 62% of detractor comments mention the pricing change. 20 of the top 50 accounts renew in Q4, worth $2.1M ARR; Customer Success rates 7 of them ($900k) at risk, all citing pricing.

Team and budgetHires: 9 of 14 planned; two senior engineers left Payments in August (both regretted). Spend: 4% under budget, mostly the unfilled roles.

Decisions the exec team needs to make(a) Whether to hold the top-20 accounts on their old pricing for six months: Finance estimates it costs $420k of ARR this year; Customer Success thinks it protects most of the $900k at risk. (b) Whether to move two engineers from Growth to Payments until bulk invoicing ships, which the Payments lead says would secure 23 October; Growth would pause the activation experiments for six weeks.

Draft headline from the VP of Sales“Record quarter: best-ever new ARR, 109% of target.” (She'd like the deck to open with it.)

What a strong answer doesThe answer key the graders mark against

Opens with an honest headline for the quarter: new ARR beat target, but NRR fell to 101.3% against 108% after the pricing change, and activation and reliability missed. Does not lead with 'record quarter' on its own. Uses Finance's 101.3% (noting the CS dashboard's different definition if it mentions 102%). Shows each OKR with a plain status, amber only on the misses. Covers the NRR drivers (the three downgrades and Harbor & Co), the bulk-invoicing slip to 26 October (23 at best), NPS 41 to 34, and the $900k renewal risk. Closes with the two decisions, framed with their trade-offs, each with an owner and a date before the Q4 renewals. At most 7 slides, takeaway headlines, the brand palette only, and a short cover note.

Critical failures (cap the score at 40)

Reports the NRR, activation or reliability OKR as met or on track
Opens the deck with the 'record quarter' headline and omits the retention drop from the opening

Task

Staff-level · v1.13

The outputs

9 setups answered this brief.

GPT-6 AstraChatGPT92Usable with a quick edit Haiku 5.5API84Usable with a quick edit Opus 5.5Claude83Usable with a quick edit GPT-6.1 SolAPI81Usable with a quick edit Sonnet 5.5API70Usable with a quick edit Gemini 3.5 Flash-LiteGemini67Needs more work GPT-6 LunaAPI63Needs more work Gemini 4 ArgonAPI58Needs more work Gemini 3.8 FlashAPI56Needs more work

Gemini 3.8 FlashAPI

Needs more than a quick edit7 of 15 checks passed

The deck includes unsupported claims, a topic-label headline on the final slide, and lacks uncertainty and practice elements; a PM would need to rework these before sending.

Output

Open the deckThe file the model delivered, in a new tab

Lena – Q3 delivered record new ARR ($2.61M, 109% of target), but the September 1 usage pricing shift severely impacted retention. NRR fell to 101.3%, NPS dropped to 34, and $900k of Q4 renewals are at risk. The attached pre-read details our recovery plan; we require two exec decisions during Wednesday’s QBR on pricing grandfathering and engineering allocation.

Claims the judge couldn’t find in the brief

Mid-market and enterprise finance teams report difficulty forecasting Ledgerly costs compared to legacy seat licensing
Response times on billing questions remained elevated while non-billing queues improved
Exec team needs to decide on Billing AI support safeguards (refund policy text and pricing knowledge-base eval criteria)

Grades and run details

Decision model 60 · LLM judge 50

Decision model checks

passUses the supplied evidence correctly40%
failAddresses the actual decision62%
passRespects explicit constraints28%
failIdentifies material uncertainty38%
passAvoids unsupported claims14%
failProduces the required deliverable43%
passLeads with the honest headline23%
partialMakes the decisions clear56%
passHeadlines state the takeaway85%
passA real 7-slide deckby hand100%
passUses only the brand coloursby hand100%
failOwns the problem55%
failReady for the reader as delivered40%
partialAsks leaders only for the big calls61%
passReports outcomes and learning, not just output66%

Artefacts

Deck (html)

Run

Run: #1
API response time: 76 s
Submitted: 1 Oct 2026

Gemini 4 ArgonAPI

Needs more than a quick edit8 of 15 checks passed

The unsupported backfill timeline would need verification and correction, and the activation problem lacks any proposed action, so a PM could not use it with only a quick edit.

Output

Open the deckThe file the model delivered, in a new tab

Lena,

Attached is the Billing Q3 QBR pre-read for tomorrow’s meeting. While we beat our New ARR target ($2.61M, 109%), we missed our other three OKRs: NRR fell to 101.3% following the September pricing change, and bulk invoicing slipped two weeks.

Slide 6 outlines two urgent trade-offs on enterprise pricing and engineering allocation we need to decide tomorrow.

Claims the judge couldn’t find in the brief

Backfilling senior payments engineers takes 60–90 days.

Grades and run details

Decision model 57 · LLM judge 57

Decision model checks

failUses the supplied evidence correctly43%
failAddresses the actual decision71%
failRespects explicit constraints38%
passIdentifies material uncertainty49%
partialAvoids unsupported claims45%
partialProduces the required deliverable19%
passLeads with the honest headline82%
partialMakes the decisions clear74%
passHeadlines state the takeaway69%
passA real 7-slide deckby hand100%
passUses only the brand coloursby hand100%
partialOwns the problem79%
failReady for the reader as delivered48%
partialAsks leaders only for the big calls58%
passReports outcomes and learning, not just output58%

Artefacts

Deck (html)

Run

Run: #1
API response time: 51 s
Submitted: 13 Oct 2026

GPT-6.1 SolAPI

Usable with a quick edit10 of 15 checks passed

The deck is complete, uses correct data, and presents the required decisions clearly; a PM could use it with only minor wording tweaks.

Output

Open the deckThe file the model delivered, in a new tab

Lena — the Billing Q3 pre-read is below for 7 October, ahead of the 8 October QBR. It leads with the ARR beat while making the three OKR misses explicit, uses Finance’s NRR, and closes with two decisions on pricing protection and engineering capacity. Proposed owners and decision dates are included.

Grades and run details

Decision model 63 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly58%
failAddresses the actual decision35%
passRespects explicit constraints55%
partialIdentifies material uncertainty28%
passAvoids unsupported claims82%
failProduces the required deliverable22%
passLeads with the honest headline46%
passMakes the decisions clear31%
passHeadlines state the takeaway85%
passA real 7-slide deckby hand100%
partialUses only the brand coloursby hand100%
failOwns the problem23%
failReady for the reader as delivered46%
partialAsks leaders only for the big calls22%
passReports outcomes and learning, not just output30%

Artefacts

Deck (html)

Run

Run: #1
API response time: 1.6 min
Submitted: 29 Sept 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 9

Uses the supplied evidence correctlyMixedWrongRight

Gemini 3.8 Flash · API

Two claims about customer sentiment (forecasting difficulty, elevated billing response times) are not supported by the brief, and a third exec decision is invented.

Gemini 4 Argon · API

The claim that backfilling senior payments engineers takes 60–90 days is not in the supplied evidence and is presented as fact.

GPT-6.1 Sol · API

All claims about the current situation are directly supported by the brief or supplied context.

Addresses the actual decisionWrongWrongMixed

Gemini 3.8 Flash · API

The recommendations do not state what result or condition would change the call, as required.

Gemini 4 Argon · API

The deck commits to a recommendation but does not state what result or condition would change that recommendation for either decision.

GPT-6.1 Sol · API

The output clearly commits to the two decisions the exec team must make, with owners and dates, as the brief requires.

Respects explicit constraintsRightMixedRight

Gemini 3.8 Flash · API

All explicit constraints (7 slides, takeaway headlines, honest OKRs, decisions with owner/date, brand colours, cover note length) are respected.

Gemini 4 Argon · API

At most 7 slides, headlines, OKRs reported with amber only for misses, decisions with owners and dates, brand colours, self-contained HTML, cover note under 60 words.

GPT-6.1 Sol · API

The deck has 7 slides, takeaway headlines, reports all OKRs honestly, ends with decisions with owners and dates, and uses only brand colours (colour check deferred to browser).

Identifies material uncertaintyWrongMixedRight

Gemini 3.8 Flash · API

No unknowns that could change the decisions are named, nor how they would be resolved.

Gemini 4 Argon · API

No material unknowns are named (e.g., whether transferred engineers will deliver 23 Oct, if pricing hold will retain accounts) nor how they would be resolved.

GPT-6.1 Sol · API

It names the key unknowns (whether legacy pricing protects the $900k, whether the transfer secures 23 October) and bounds them with estimates.

Avoids unsupported claimsMixedWrongRight

Gemini 3.8 Flash · API

Interpretations about forecasting difficulty and billing response times are presented as fact without support, and an extra exec decision is added.

Gemini 4 Argon · API

The 60–90 day backfill timeline is presented as established fact with no supporting evidence.

GPT-6.1 Sol · API

Interpretations are clearly labelled as such, and no confident claims go beyond the supplied evidence.

Produces the required deliverableMixedRightMixed

Gemini 3.8 Flash · API

The HTML file and cover note are present, complete, and usable by the named reader.

Gemini 4 Argon · API

Single HTML file delivered, cover note within 60 words, slides stacked as required.

GPT-6.1 Sol · API

The self-contained HTML file with 7 slides is delivered, meeting the form and length requirements.

Headlines state the takeawayMixedRightRight

Gemini 3.8 Flash · API

Slide 7 headline is a topic label ('Exec decisions required...'), not a takeaway sentence.

Gemini 4 Argon · API

Every slide headline is a takeaway sentence, not a topic label.

GPT-6.1 Sol · API

Every slide headline is a takeaway sentence, not a topic label.

Uses only the brand coloursRightRightMixed

Gemini 3.8 Flash · API

No reason given.

Gemini 4 Argon · API

No reason given.

GPT-6.1 Sol · API

No reason given.

Owns the problemWrongWrongMixed

Gemini 3.8 Flash · API

For NRR and activation misses, the deck does not state what the team has already done to address them.

Gemini 4 Argon · API

The activation miss is reported with no action taken or recommended way forward; the material problem lacks a team response.

GPT-6.1 Sol · API

Each material problem is accompanied by actions already taken and the recommended options with trade-offs.

All mixed 1

Ready for the reader as deliveredMixedMixedMixed

Gemini 3.8 Flash · API

All owners and dates are plausible, no placeholders, and the language is appropriate for the CEO.

Gemini 4 Argon · API

Addressed to Lena, no placeholder owners, no internal notes, all dates aligned with the brief.

GPT-6.1 Sol · API

The deck is written entirely for Lena and the exec team, with owners and dates taken from the material or clearly proposed.

All got right 5

Leads with the honest headlineRightRightRight

Gemini 3.8 Flash · API

The first content slide headline includes the NRR miss alongside the new-ARR beat.

Gemini 4 Argon · API

First slide headline balances the record new ARR with the NRR fall and three missed OKRs.

GPT-6.1 Sol · API

The first content slide headline includes the retention drop alongside the ARR beat, not just the record quarter.

Makes the decisions clearRightRightRight

Gemini 3.8 Flash · API

Both required decisions are presented with trade-offs, owners, and dates before Q4 renewals.

Gemini 4 Argon · API

Slide 6 presents both decisions with trade-offs, owners, and 8 October deadline.

GPT-6.1 Sol · API

The deck ends with both decisions, each with its trade-off, an owner, and a date before the Q4 renewals.

A real 7-slide deckRightRightRight

Gemini 3.8 Flash · API

No reason given.

Gemini 4 Argon · API

No reason given.

GPT-6.1 Sol · API

No reason given.

Asks leaders only for the big callsRightRightRight

Gemini 3.8 Flash · API

The deck separates exec decisions from team actions and states which calls the team will make.

Gemini 4 Argon · API

Only the two big cross-functional trade-offs are escalated, with clear recommendations; team-level calls are not diluted.

GPT-6.1 Sol · API

It clearly separates the two high-stakes decisions for the exec team from operational calls, with a recommendation and date.

Reports outcomes and learning, not just outputRightRightRight

Gemini 3.8 Flash · API

The update leads with outcome metrics and what changed in understanding, with shipping as supporting detail.

Gemini 4 Argon · API

Outcome metrics lead, and the narrative explains what the team learned (pricing shock, bulk-invoicing gap cost, fraud impact).

GPT-6.1 Sol · API

The update leads with outcome metrics and what the team learned, with shipping details as supporting evidence.

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 71% of 118 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	96.3	92.9	2	None
2	GPT-6.1 SolwithAPI	73.3	96.2	2	None
3	Haiku 5.5withAPI	79.2	92.3	2	None
4	Sonnet 5.5withAPI	68.8	96.4	2	None
5	Opus 5.5withClaude	84.2	77.5	2	None
6	GPT-6 LunawithAPI	71.3	66.8	2	None
7	Gemini 4 ArgonwithAPI	70.0	63.2	2	None
8	Gemini 3.5 Flash-LitewithGemini	73.8	51.6	2	None
9	Gemini 3.8 FlashwithAPI	71.7	51.9	2	None

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Nobody beats these on both score and costCost partly estimated (app runs with no usage meter)

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
Gemini 3.5 Flash-Lite · Gemini	61	1.0× (partly estimated)
GPT-6 Luna · API	69	1.3×
Haiku 5.5 · API	84	2.6×
Gemini 3.8 Flash · API	61	13×
Gemini 4 Argon · API	65	15×
GPT-6.1 Sol · API	85	15×
GPT-6 Astra · ChatGPT	95	26× (partly estimated)
Sonnet 5.5 · API	82	40×
Opus 5.5 · Claude	80	55× (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

The PM job

Writing the weekly update, or the reply to an executive's question, that the team's credibility rests on.

Why it matters

Updates are where bad news gets softened. A fluent update that buries a slipped date or presents a guess as the cause does more harm than no update: leadership decides on it, and trust goes when the truth comes out.

What good looks like

Leads with the news and any decision needed
States dates, numbers and causes plainly, including bad news
Separates what is known from what is suspected
Brings a recommendation and what's been tried, not just the problem
Fits the reader and the length asked for

Deliberately not measured

Tone and formatting preferences
Writing style beyond clarity

Capability tested

Honest, decision-first status communication

The failure we’re looking for

Spin: burying or softening the bad news, or presenting a guess as fact

Grading

Decision model, LLM judge and a browser check (for the deck), calibrated against a blind PM review

Variants

A short written update · Staff level: a QBR deck in brand colours

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

Reliably right

Where it slips

How it’s graded

Asks leaders only for the big calls

Reports outcomes and learning, not just output

Plus our standard checks

The tasks

The outputs

Output

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Artefacts

Run

Output

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Artefacts

Run

Output

Grades and run details

Decision model checks

Artefacts

Run

Check by check

Where they differ 9

All mixed 1

All got right 5

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured