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Latest drop: Haiku 5.5 · 8 Oct
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 16 graded outputs by 8 models. 56% were usable with at most a quick edit.

Reliably right

  1. Reports outcomes and learning, not just output100% pass
    Leads with outcome metrics (ARR, active teams, SLA, pipeline) and what the team learned (pipeline redefinition, invoice ticket surge), not just activities.
    Sonnet 5.5 · API · On track, mostly
  2. 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
  3. A real 7-slide deck100% pass

Where it slips

  1. Owns the problem55% 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
  2. Identifies material uncertainty61% pass
    The output identifies the overlap uncertainty for decision A but does not name any material unknown that could change the call for decision B, such as the reliability of the Payments lead's estimate or the impact of pausing activation experiments.
    Opus 5.5 · Claude · A quarterly business review deck, in brand
  3. Flags the redefined pipeline63% 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.

  1. 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

  2. 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 PodcastChristian 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 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 Head of Product at Cobalt. Write this fortnight's update for the CEO and the exec team, in no more than 450 words, from the weekly metrics and the team notes below. It should say where we stand, what's wrong, what we're doing about it, and what you need from them.

What the model was given3 items: About Cobalt, weekly_metrics_weeks_1_to_8.csv, team_notes.md
About CobaltTeam scheduling software, sold per seat.
weekly_metrics_weeks_1_to_8.csv8 rows · Download
week,new_arr_signed_usd,qualified_pipeline_usd,weekly_active_teams,support_backlog_tickets,support_sla_met_pct
1,182000,2410000,8210,140,96
2,205000,2380000,8260,152,95
3,168000,2350000,8240,171,94
4,214000,2290000,8300,198,91
5,199000,2230000,8280,260,86
6,221000,3140000,8310,344,79
…
team_notes.md7 lines · Download
# Notes from the teams

- Sales ops: from week 6, "qualified pipeline" includes active free trials over 10 seats (they used to be excluded).
- Data: weekly active teams for week 7 is missing because of a pipeline outage; it's being backfilled.
- Support: the backlog grew after the September pricing change (tickets about invoices doubled). SLA target is 90% answered within one business day.
- Quarter target: $2.4M new ARR in 12 weeks ($200,000 a week). Weeks 1 to 8 total: see the file.
- Support hiring: two agents approved, start date not set. Decision needed: whether to pause the planned price change for existing customers in week 10 until the backlog is cleared.
What a strong answer doesThe answer key the graders mark against

Leads with the takeaway: new ARR is ahead of plan ($1.67M in eight weeks against $1.6M), but support is breaking and pipeline is weaker than it looks. Flags that the jump in qualified pipeline in week 6 (from about $2.23M to $3.14M) is the redefinition to include large trials; on the old definition pipeline fell every week from $2.41M to $2.23M, a risk for the rest of the quarter. Reports the support problem plainly: backlog from 140 to 455 tickets and SLA from 96% to 72% against a 90% target since the September pricing change, with what's being done (two agents approved). Notes week 7's active teams are missing and being backfilled rather than hiding it. Asks for the decisions that need the execs: pause the week-10 price change for existing customers until the backlog clears, and set the start date for the support hires. Separates what the team is handling from what needs them.

Critical failures (cap the score at 40)
  • Presents qualified pipeline as having grown about 40%
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Sonnet 5.5API98Usable with a quick editGPT-6 AstraChatGPT96Usable with a quick editGPT-6.1 SolAPI92Usable with a quick editHaiku 5.5API87Needs more workOpus 5.5Claude79Needs more workGPT-6 LunaAPI77Usable with a quick editGemini 3.8 FlashAPI63Needs more workGemini 3.5 Flash-LiteGemini58Needs more work
GPT-6 LunaAPI

Usable with a quick edit8 of 12 checks passed

The output is clear and well-structured; a PM could quickly add the missing like-for-like pipeline trend and decision dates without re-checking evidence or redoing reasoning.

Output

Cobalt | Fortnightly exec update

Where we stand - We’ve signed $1.673M in new ARR in weeks 1–8, against a 12-week target of $2.4M. That’s $73K ahead of target pace; we need another $727K, or about $182K/week, over the final four weeks. The last two weeks were our strongest, at $236K and $248K. - Qualified pipeline is reported at $3.35M, but it is not directly comparable with earlier weeks: since week 6, it includes active free trials over 10 seats. Treat the increase as a definition change, not proven pipeline growth. - Weekly active teams are broadly flat: 8,190 in week 8 vs. 8,210 in week 1. Week 7 is missing due to a data pipeline outage and is being backfilled.

What’s wrong - Support is the clearest operational risk. Backlog has climbed from 140 to 455 tickets; SLA performance has fallen from 96% to 72%, well below the 90% target. The backlog grew after September’s pricing change, with invoice-related tickets doubling. - Two support agents are approved, but their start date is still unset.

What we’re doing - We’ll work with Support to identify the highest-volume invoice issues and prioritize product fixes that reduce avoidable contacts. - We’re backfilling week 7 active-team data and will keep pipeline reporting clear about the changed qualification definition.

What we need - Decide whether to pause the week 10 price change for existing customers until the support backlog is cleared. Given the SLA and invoice-ticket trend, I recommend pausing it. - Set start dates for the two approved support agents.

Grades and run details

Decision model 83 · LLM judge 69
Decision model checks
  • passUses the supplied evidence correctly72%
  • partialAddresses the actual decision52%
  • passRespects explicit constraints74%
  • partialIdentifies material uncertainty70%
  • passAvoids unsupported claims43%
  • passProduces the required deliverable97%
  • partialFlags the redefined pipeline98%
  • passReports the support breach plainly100%
  • partialOwns the problem49%
  • passReady for the reader as delivered80%
  • passAsks leaders only for the big calls79%
  • passReports outcomes and learning, not just output98%
Run
Run
#1
API response time
13 s
Submitted
6 Oct 2026
Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit6 of 12 checks passed

The ARR total is wrong ($1.73M instead of $1.673M), a factual error that would require re-checking the evidence before use.

Output

To: CEO & Exec Team From: Head of Product, Cobalt Subject: Fortnightly Update (Weeks 7–8)

Here is where we stand for the last two weeks, the critical issues we face, our action plan, and where I need your input.

Where We Stand

We are currently outperforming our top-line targets. We’ve brought in $1.73M in new ARR across 8 weeks against our $1.6M pro-rata goal ($200k/week), closing Week 8 at a strong $248k. Pipeline looks healthy on paper, jumping to $3.35M. However, caveat emptor: Sales Ops adjusted the pipeline definition in Week 6 to include active free trials over 10 seats, masking underlying organic pipeline velocity. Furthermore, weekly active teams dipped to 8,190 in Week 8 (Week 7 data is currently missing due to a data pipeline outage, currently being backfilled).

What’s Wrong

Customer Support is in a critical state. Our support backlog has exploded to 455 tickets, and SLA compliance has plummeted to 72% (target: 90%).

This was triggered by the September pricing change. Invoicing-related tickets have doubled, and our current support bandwidth is completely overwhelmed. While two new hires have been approved, we do not yet have confirmed start dates.

What We’re Doing About It

  1. Stabilizing Support: We are exploring short-term triage options, including engineering-led documentation updates to deflect self-service billing questions.
  2. Data Recovery: Data engineering is actively backfilling the Week 7 active teams metric to restore full visibility.
  3. Pipeline Audit: Sales Ops is recalibrating the pipeline reporting to separate true enterprise/mid-market opportunities from the newly included free trials so we can accurately forecast Q3 close rates.

What I Need From You

We face an immediate strategic decision regarding Week 10:

  • Decision Needed: Should we pause the planned price change for existing customers scheduled for Week 10 until the current support backlog is cleared and SLAs are back above 90%?

Proceeding with the existing customer rollout while our SLA is at 72% risks a wave of churn and avoidable escalation tickets. I recommend pausing, but I need a final call from the exec team so Product and Support can communicate the adjustment internally and externally.

Claims the judge couldn’t find in the brief

  • We’ve brought in $1.73M in new ARR across 8 weeks

Grades and run details

Decision model 75 · LLM judge 46
Decision model checks
  • partialUses the supplied evidence correctly21%
  • partialAddresses the actual decision49%
  • passRespects explicit constraints24%
  • partialIdentifies material uncertainty68%
  • partialAvoids unsupported claims52%
  • passProduces the required deliverable51%
  • partialFlags the redefined pipeline97%
  • passReports the support breach plainly100%
  • partialOwns the problem52%
  • passReady for the reader as delivered37%
  • passAsks leaders only for the big calls54%
  • passReports outcomes and learning, not just output92%
Run
Run
#1
Time to output
3 s
Submitted
5 Oct 2026
GPT-6 AstraChatGPT

Usable with a quick edit12 of 12 checks passed

The output is accurate, complete, and well-structured; a product manager could use it with at most light wording edits, without re-checking evidence or redoing reasoning.

Output

Fortnightly product update — end of week 8

Bottom line: New ARR is ahead of pace, but support is deteriorating and usage is flat to slightly down. I recommend pausing the week-10 price change for existing customers until we restore support capacity and service levels.

Where we stand

  • New ARR: $484k signed in weeks 7–8, up 15% on the prior fortnight. We’ve signed $1.673M against a $1.6M pace target, putting us $73k ahead. We need another $727k over four weeks—about $182k per week—to hit the $2.4M quarter target.
  • Pipeline: Reported qualified pipeline is $3.35M, but the jump from week 6 includes a definition change: active free trials above 10 seats now count. We should not treat that increase as evidence of stronger demand. Before the change, pipeline had declined for five consecutive weeks.
  • Usage: Weekly active teams reached 8,190 in week 8, down 1.4% from week 6 and broadly flat versus week 1. Week 7 is missing due to a data outage, so we cannot assess the full fortnight’s trajectory.

What’s wrong

Support backlog increased from 344 to 455 tickets this fortnight, while one-business-day SLA attainment fell from 79% to 72%, against a 90% target. Invoice tickets doubled after September’s pricing change. Another price change risks adding demand to an already overloaded queue.

Bookings are encouraging, but they do not establish healthy adoption or retention; the usage and support signals warrant attention.

Priorities for the next fortnight

  • Support/Product: Triage invoice issues, identify recurring billing causes, and prioritise fixes and customer guidance that reduce repeat contacts. Track backlog and SLA daily.
  • Data/Sales Ops: Complete the week-7 usage backfill and provide a like-for-like pipeline view separating newly included trials.
  • Support hiring: Confirm start dates and ramp plans for the two approved agents.

What I need from the exec team

  1. Approve pausing the week-10 existing-customer price change. Resume only once the backlog is cleared and SLA performance is sustainably back at or above 90%.
  2. Secure start dates for both support hires this week and agree interim coverage if they cannot start before week 10.

Grades and run details

Decision model 92 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly48%
  • passAddresses the actual decision99%
  • passRespects explicit constraints43%
  • passIdentifies material uncertainty37%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable94%
  • passFlags the redefined pipeline96%
  • passReports the support breach plainly100%
  • partialOwns the problem38%
  • passReady for the reader as delivered78%
  • passAsks leaders only for the big calls85%
  • passReports outcomes and learning, not just output98%
Run
Run
#1
Time to output
17 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 7

Uses the supplied evidence correctlyRightWrongRight
GPT-6 Luna · API

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

Gemini 3.5 Flash-Lite · Gemini

The total new ARR is stated as $1.73M, but the sum of weeks 1-8 is $1.673M; this factual error means the output does not use the supplied evidence correctly.

GPT-6 Astra · ChatGPT

All factual claims about the current situation are directly supported by the supplied metrics or team notes, with no invented facts.

Addresses the actual decisionWrongWrongRight
GPT-6 Luna · API

The output recommends pausing the price change but does not state what result or condition would change that recommendation, as required.

Gemini 3.5 Flash-Lite · Gemini

The output recommends pausing the price change but does not state what result or condition would change that recommendation, as required.

GPT-6 Astra · ChatGPT

The output commits early to pausing the week-10 price change, frames it for the CEO/exec team, and states the condition (backlog cleared, SLA ≥90%) that would change the call.

Identifies material uncertaintyWrongWrongRight
GPT-6 Luna · API

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

Gemini 3.5 Flash-Lite · Gemini

It does not name the specific unknowns that could change the decision about the price change, nor say how they would be resolved or what would change the call.

GPT-6 Astra · ChatGPT

It identifies the missing week 7 data, the pipeline redefinition, and the condition for resuming the price change, naming the unknowns that affect the decision.

Avoids unsupported claimsRightWrongRight
GPT-6 Luna · API

Interpretations are clearly framed as recommendations or assessments, and no causes are presented as fact without evidence.

Gemini 3.5 Flash-Lite · Gemini

The $1.73M ARR figure is presented as fact but is not supported by the data; it is a confident claim that the evidence does not establish.

GPT-6 Astra · ChatGPT

Forecasts and interpretations are labelled as risks or opinions (e.g., 'risks adding demand', 'warrant attention'), not presented as established facts.

Produces the required deliverableRightMixedRight
GPT-6 Luna · API

The fortnightly update is complete, within word limit, and usable by the CEO and exec team with all required elements.

Gemini 3.5 Flash-Lite · Gemini

The update contains a material factual error in the ARR total, so the reader cannot act on it without correcting that number.

GPT-6 Astra · ChatGPT

The update is complete, within the word limit, structured for the named reader, and contains all required elements to act on.

Flags the redefined pipelineWrongWrongRight
GPT-6 Luna · API

The output names the redefinition but does not give the falling like-for-like trend on the old definition.

Gemini 3.5 Flash-Lite · Gemini

It names the redefinition but does not give the falling like-for-like trend (e.g., pipeline on the old definition fell from $2.41M to $2.23M).

GPT-6 Astra · ChatGPT

It explicitly names the redefinition (free trials over 10 seats now included) and states the like-for-like trend of five consecutive weekly declines.

Asks leaders only for the big callsMixedRightRight
GPT-6 Luna · API

The output asks for two decisions but does not provide a date by when they are needed, as the criterion requires.

Gemini 3.5 Flash-Lite · Gemini

It asks for the pause decision with a recommendation and a date, and separates it from the operational actions the team is taking.

GPT-6 Astra · ChatGPT

It separates the exec asks (approve pause, secure start dates) from the team's own priorities, with clear recommendations and timing.

All got right 5

Respects explicit constraintsRightRightRight
GPT-6 Luna · API

The update is under 450 words, addressed to the CEO and exec team, and covers the required sections.

Gemini 3.5 Flash-Lite · Gemini

The update is under 450 words and addressed to the CEO and exec team as requested.

GPT-6 Astra · ChatGPT

The update is under 450 words, addresses where we stand, what's wrong, what we're doing, and what's needed, and is written as a fortnightly update for the CEO and exec team.

Reports the support breach plainlyRightRightRight
GPT-6 Luna · API

The update gives the backlog and SLA numbers against the 90% target, links them to the pricing change, and asks for the pause decision.

Gemini 3.5 Flash-Lite · Gemini

It reports the backlog (455) and SLA (72% vs 90% target), links them to the pricing change, and asks for the pause decision.

GPT-6 Astra · ChatGPT

It reports the backlog (344 to 455) and SLA (79% to 72%) against the 90% target, links the cause to the pricing change, and asks for the pause decision.

Owns the problemRightRightRight
GPT-6 Luna · API

The support problem includes actions already taken (agents approved) and a recommended way forward (pause price change, product fixes).

Gemini 3.5 Flash-Lite · Gemini

For support it notes the approved hires and recommends pausing the price change; for data and pipeline it states the actions being taken.

GPT-6 Astra · ChatGPT

For the support problem, it lists actions already taken (triage, identify causes, fixes, daily tracking) and recommends pausing the price change with its trade-off.

Ready for the reader as deliveredRightRightRight
GPT-6 Luna · API

Written entirely for the named reader with no placeholders, lifted internal notes, or unclear owners.

Gemini 3.5 Flash-Lite · Gemini

The update is written for the exec team, uses no unexplained jargon from the brief, and all owners/dates are from the material or clearly proposed.

GPT-6 Astra · ChatGPT

The update is written entirely for the CEO/exec team, with no placeholder names, no lifted internal notes, and all owners and dates are either from the material or clearly proposed.

Reports outcomes and learning, not just outputRightRightRight
GPT-6 Luna · API

The update leads with outcome metrics and highlights what the team learned about the pipeline redefinition and the invoice-ticket cause.

Gemini 3.5 Flash-Lite · Gemini

It leads with outcome metrics (ARR, pipeline, support) and explains what changed (support broke after pricing change), showing learning.

GPT-6 Astra · ChatGPT

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.

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 76% of 89 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstraChatGPT94.292.92None
2Sonnet 5.5API84.696.42None
3Haiku 5.5API89.692.32None
4GPT-6.1 SolAPI80.896.22None
5Opus 5.5Claude86.377.52None
6GPT-6 LunaAPI76.766.82None
7Gemini 3.5 Flash-LiteGemini70.851.62None
8Gemini 3.8 FlashAPI66.351.92None

What you get for the cost

Haiku 5.5 · API scores within 5 points of GPT-6 Astra · ChatGPT at a tenth of the cost.

Task score against relative cost for each setup. The table below has the same figures.50607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 94, 26× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 90, 40× the cheapestHaiku 5.5 · API: task score 89, 2.6× the cheapestGPT-6.1 Sol · API: task score 88, 15× the cheapestOpus 5.5 · Claude: task score 81, 55× the cheapest (1 of 2 outputs estimated)GPT-6 Luna · API: task score 71, the cheapestGemini 3.5 Flash-Lite · Gemini: task score 60, the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 58, 13× the cheapestGPT-6 Astra · ChatGPTSonnet 5.5 · APIHaiku 5.5 · APIGPT-6.1 Sol · APIOpus 5.5 · ClaudeGPT-6 Luna · APIGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)

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