Tasks / Leadership

Write a performance review

Can the model write a review that is clear, fair and specific enough to change what the person does next?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 16 graded outputs by 8 models. 50% were usable with at most a quick edit.

Reliably right

Judges outcomes, not activity100% pass
Every rating change and keep is justified by outcomes against goals, with shipping and activity treated as context, not the result.
GPT-6 Astra · ChatGPT · Fourteen ratings, three managers
Owns the manager's part100% pass
Ana plainly acknowledges her Q3 mistake and commits to reviewing goal metrics first in every monthly 1:1.
Opus 5.5 · Claude · Shipped a lot, moved nothing
Clear and direct, with care97% pass
The note to Ravi is direct, specific about what needs to change, and framed to help him succeed; the whole pre-read is clear and actionable.
GPT-6 Astra · ChatGPT · Fourteen ratings, three managers

Where it slips

Identifies material uncertainty50% pass
The output does not name any specific unknowns that could change the rating or how they would be resolved.
Opus 5.5 · Claude · Shipped a lot, moved nothing
Notices who's underrated69% pass
It does not propose Exceeds or ask Sara to justify Meets despite Nora beating both goals; it merely keeps Meets, missing the underrating.
GPT-6 Luna · API · Fourteen ratings, three managers
Catches the inflated team78% pass
The output proposes lowering Ben from Exceeds to Meets, failing to keep Ben at Exceeds as required; Ben beat both outcome goals and should remain Exceeds.
Opus 5.5 · Claude · Fourteen ratings, three managers

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.

Clear and direct, with care
Does the review say plainly what needs to change, in words the person couldn't misread, while showing it is written to help them succeed?
Passes when Every main point is stated directly, with the specific change expected and the support on offer.
Kim ScottLenny’s Podcast
Judges outcomes, not activity
Does the review judge the person on the outcomes they achieved against their goals, rather than on how much they shipped or how busy they were?
Passes when Rates against the goals' outcomes, with the evidence, and treats output as context, not the result.
Product and growth leadersOn Lenny’s Podcast
Names gaps you could see
Is each area to improve described as specific, observable behaviour with what good would look like, rather than labels like 'be more strategic'?
Passes when Every gap names what the person did or didn't do, in a specific situation, and what doing it well would look like.
Product and growth leadersOn Lenny’s Podcast
Weighs the whole period
Does the review weigh the whole review period, rather than letting one recent event or one strong impression decide the verdict?
Passes when Covers the whole period in proportion, and explicitly guards against a recent event or one impression dominating.
Product and growth leadersOn Lenny’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 · and 3 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 Director of Product at Northgate, and you're facilitating Thursday's calibration for 14 PMs across three managers (Ravi, Sara and Tom). Write the pre-read, in no more than 1,100 words: the ratings you'd change and why, each with the evidence, the ones you'd keep, and a short note to Ravi about his ratings. Use the guidance below.\n\nThe proposed ratings and evidence are attached.

What the model was given2 items: calibration_guidance.md, proposed_ratings.csv (one row per PM, as each manager submitted it)

calibration_guidance.md7 lines · Download

# Calibration guidance

- Ratings judge outcomes against goals first, then how the work was done. Shipping is not an outcome.
- Any rating other than Meets needs written evidence of outcomes.
- A Below rating needs documented earlier feedback on the gap.
- Expected spread across the org: most PMs Meets; Exceeds for clear outperformance on outcomes.
- The calibration facilitator proposes changes; managers can respond before ratings are final.

proposed_ratings.csv (one row per PM, as each manager submitted it)pm,manager,proposed_rating,goal_1,goal_1_result,goal_2,goal_2_result,evidence_cited_by_manager Aisha,Ravi,Greatly exceeds,Raise trial conversion 8% to 11%,8.4%,Ship self-serve billing,Shipped,Shipped 9 features; great energy; leadership loves her Ben,Ravi,Exceeds,Cut onboarding time 10 to 5 days,4.5 days,Raise activation 30% to 38%,39%,Hit both goals Chloe,Ravi,Exceeds,Grow API usage 20%,+4%,Launch partner portal,Shipped,Partner portal launched on time; strong specs Dev,Ravi,Exceeds,Reduce support tickets per account 25%,-27%,NPS from 31 to 40,41,Hit both goals; mentored two APMs Emma,Ravi,Exceeds,Expansion revenue +15%,+6%,Ship usage dashboard,Shipped,Very responsive to Sales; shipped 6 Sales requests Femi,Sara,Meets,Raise weekly retention 41% to 46%,46%,Cut churn of new accounts 20%,-22%,"Hit both goals, steady" Gita,Sara,Below,Raise invoices paid online 35% to 45%,47%,Launch reminders v2 by September,"Launched, rolled back after 4 days (support not briefed)",Reminders launch in September was a mess Hugo,Sara,Meets,Ship mobile app v2,Shipped late,Mobile weekly actives +20%,+3%,Delivered a hard project Ines,Tom,Exceeds,Cut checkout drop-off 30% to 22%,21%,Launch two payment methods,Shipped,"Hit both, specific evidence" Jon,Tom,Meets,Search success 60% to 70%,66%,Ship filters,Shipped,"Good progress, more to do" Kai,Tom,Meets,Reduce refund rate 4% to 3%,3.1%,Fraud losses -20%,-24%,Solid year Lena,Tom,Below,Grow marketplace listings 25%,+9%,Launch seller analytics,Not launched,"Missed both goals; feedback given in Q2 and Q3 1:1s, documented" Mo,Tom,Meets,Cut time to first sale for new sellers 21 to 14 days,15 days,Seller NPS +5,+6,"Nearly hit, good stakeholder work" Nora,Sara,Meets,Raise payment success 92% to 95%,95.4%,Cut payment support tickets 30%,-35%,"Quiet, delivered"

What a strong answer doesThe answer key the graders mark against

Judges each PM on outcomes against their goals, as the guidance says. Ravi rated all five of his PMs Exceeds or above, but only Ben and Dev hit both goals; Aisha (Greatly exceeds) missed her conversion goal (8.4% against 11%), Chloe missed API usage (+4% against +20%) and Emma missed expansion (+6% against +15%), and his evidence is output ('shipped 9 features', 'responsive to Sales'). Proposes Aisha, Chloe and Emma at Meets (or lower for Chloe, whose outcome goal was far off), keeps Ben and Dev at Exceeds. Gita (Sara, Below) beat her main goal (47% against 45%) and the Below rests on one September launch, with no documented feedback, which the guidance requires: Meets, with the launch-readiness gap documented now. Nora (Sara, Meets) beat both goals: a candidate for Exceeds. Lena (Tom, Below) has documented feedback and missed both goals: Below stands. Hugo shipped late and missed his outcome: Meets is generous but defensible, or Below needs documentation first. The note to Ravi is direct and respectful: rate on outcomes, not activity, with what evidence he'd need.

Critical failures (cap the score at 40)

Keeps Aisha at Greatly exceeds

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6 AstraChatGPT100Usable with a quick edit GPT-6.1 SolAPI100Usable with a quick edit Haiku 5.5API100Usable with a quick edit Gemini 3.8 FlashAPI98Usable with a quick edit Opus 5.5Claude88Needs more work GPT-6 LunaAPI82Needs more work Sonnet 5.5API77Needs more work Gemini 3.5 Flash-LiteGemini38Needs more work

Haiku 5.5API

Usable with a quick edit13 of 13 checks passed

The pre-read gives specific changes, evidence, and a clear note to Ravi; it can be used with light edits and does not require re-checking facts.

Output

Calibration pre-read: 14 PMs, Ravi / Sara / Tom

Purpose: Thursday's calibration. Managers can respond to these proposals before ratings are final.

Rules I applied (from calibration guidance): - Ratings start from outcomes against goals. Shipping is an output, and it doesn't count as an outcome. - Any rating other than Meets needs written outcome evidence. - Below needs documented earlier feedback on the gap. Where that's missing, I've capped the rating at Meets. - Working definition for Exceeds (for discussion): both outcome goals beaten. One beat and one hit is Meets.

Summary: 6 changes, 1 hold, 7 keeps. Proposed spread after changes: 3 Exceeds, 10 Meets, 1 Below.

---

Ratings I'd change

Aisha (Ravi): Greatly exceeds → Meets - Trial conversion reached 8.4% against an 11% target. That's about 13% of the intended 3-point lift. - "Shipped self-serve billing" is an output. Nine features shipped is also an output. - "Great energy" and "leadership loves her" are not outcome evidence. - Greatly exceeds is not supported. Below would need documented feedback on the conversion gap, which the submission doesn't cite.

Chloe (Ravi): Exceeds → Meets - API usage grew 4% against a 20% target, which is one-fifth of the goal. - The partner portal shipped on time. That's an output, and it doesn't offset the miss. - "Strong specs" describes how the work was done, not what it achieved. - Ravi should show what the portal changed in usage, if anything. If the outcome is attributable and I've missed it, I'll revisit.

Emma (Ravi): Exceeds → Meets - Expansion revenue grew 6% against a 15% target, which is 40% of the goal. - The usage dashboard shipped, and six Sales requests were delivered. Both are outputs. Responsiveness to Sales is a good working habit, but it isn't an outcome.

Ines (Tom): Exceeds → Meets (open to revisiting) - Checkout drop-off reached 21% against a 22% target. That's a clean beat, but only by one point. - Two payment methods shipped. The record has no adoption or conversion metric for them, so that goal is an output. - Under the working definition, one beat plus one output is Meets. If Tom has adoption data for the new payment methods, I'll revisit Exceeds on Thursday.

Nora (Sara): Meets → Exceeds - Payment success reached 95.4% against a 95% target. - Payment-related support tickets fell 35% against a 30% target. - Both outcome goals were beaten, the same pattern as Ben. "Quiet" isn't a criterion, and the outcomes support a higher rating than proposed.

---

Ratings on hold (need documentation before Thursday)

Gita (Sara): Below → Meets (Below only if documentation exists) - Invoices paid online reached 47% against a 45% target. That's an outcome hit. - Reminders v2 launched, was rolled back after four days, and support wasn't briefed. That's a real failure in how the work was done, and it needs a written account and a postmortem. - Below requires documented earlier feedback on the gap. None is cited, and one of two outcome goals was hit. - Sara: if you have documented feedback on the launch-readiness issue, I'll reconsider Below.

Hugo (Sara): Meets (hold) - Mobile weekly actives grew 3% against a 20% target, which is 15% of the goal. The app shipped late. - "Delivered a hard project" is an output description, and the outcome miss is large. - Meets is the ceiling the current record supports. If the mobile gap was raised in 1:1s before Q3, Below is defensible. Sara, please confirm before Thursday.

---

Ratings I'd keep

PM	Rating	Why
Ben (Ravi)	Exceeds	Both beaten: onboarding 4.5 days vs 5; activation 39% vs 38%. Cite the numbers, not "hit both goals."
Dev (Ravi)	Exceeds	Both beaten: support tickets per account −27% vs −25%; NPS 41 vs 40. Mentoring two APMs is a useful addition.
Femi (Sara)	Meets	Retention exactly at target (46%); churn beat (−22% vs −20%). One beat, one hit.
Jon (Tom)	Meets	Search success 66% vs 70%, about 60% of the gain. Filters shipped. Partial progress is Meets.
Kai (Tom)	Meets	Refund rate 3.1% vs 3.0%, a narrow miss. Fraud losses −24% vs −20%, a beat.
Lena (Tom)	Below	Both goals missed (listings +9% vs +25%; seller analytics not launched). Feedback documented in Q2 and Q3 1:1s. Tom, please attach those notes to the file.
Mo (Tom)	Meets	Time to first sale 15 days vs 14 target, about 86% of the gain. Seller NPS +6 vs +5.

---

Note to Ravi

Ravi, five of your five ratings are above Meets, and three of them (Aisha, Chloe, Emma) rest on shipping, responsiveness and reputation. The outcome numbers in the same rows don't support those ratings: 8.4% vs 11%, +4% vs +20%, and +6% vs +15%.

Ben and Dev are your strongest calls because their evidence is outcomes, and that's the standard we should use for all five.

I'm not suggesting Aisha, Chloe or Emma had weak years. Nine features and an on-time portal are real work. But ratings have to be tied to the goals we set.

Before Thursday, could you: 1. For Aisha, Chloe and Emma, write the outcome analysis for each goal. Include what was tried, what moved, and what you think caused the gap. 2. Tell us whether any feedback on those gaps was documented earlier in the year. If it was, Below becomes available. 3. Respond to the proposed changes, so we can settle them on Thursday.

Happy to walk through this 1:1 before the session.

---

Expected spread after changes

Greatly exceeds: 0
Exceeds: 3 (Ben, Dev, Nora)
Meets: 10
Below: 1 (Lena), with Gita and Hugo pending documentation

This is within the guidance: most PMs at Meets, Exceeds reserved for clear outperformance on outcomes.

Grades and run details

Decision model 100 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly70%
passAddresses the actual decision86%
passRespects explicit constraints33%
passIdentifies material uncertainty98%
passAvoids unsupported claims67%
passProduces the required deliverable85%
passCatches the inflated team100%
passRescues Gita from one bad launch100%
passNotices who's underrated100%
passClear and direct, with care100%
passJudges outcomes, not activity100%
passNames gaps you could see79%
passWeighs the whole period79%

Run

Run: #1
API response time: 47 s
Submitted: 8 Oct 2026

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit2 of 13 checks passed

The output is incomplete, missing the keep list and Ravi note, and cannot be used without redoing the reasoning and filling major gaps.

Output

To: Ravi, Sara, Tom From: Director of Product Subject: Pre-Read: Thursday PM Calibration (Northgate)

Team,

Ahead of our calibration session this Thursday, I have reviewed the proposed ratings, outcomes, and evidence for all 14 PMs across our org.

Per our calibration guidance, we evaluate PMs first on outcomes against goals, not on activity. Shipping features, being responsive to sales, or having great energy are inputs, not results. Furthermore, any rating other than "Meets" requires rigorous, quantifiable evidence of goal achievement, and "Below" ratings require documented prior feedback.

Below is my proposed adjustment of ratings, the rationale and evidence for changes, and the list of ratings we are keeping as submitted.

---

Part 1: Proposed Rating Changes & Rationale

#### 1. Aisha (Manager: Ravi) * Proposed by Manager: Greatly Exceeds * My Proposed Rating: Below * Evidence & Rationale: * Goal 1: Raise trial conversion from 8% to 11% $\rightarrow$ Result: 8.4% (Missed by a wide margin). * Goal 2: Ship self-serve billing $\rightarrow$ Result: Shipped (Violation of guidance: "Shipping is not an outcome"). * Rationale: Aisha missed her primary core metric goal entirely (gaining only 0.4% against a 3% target) and fell back on "shipping" and "great energy" for her second goal. Per our framework, failing the core quantitative goal while relying on output means this is a Below performance. Ravi, we will need to document the performance gap and prior feedback for Aisha in this cycle.

#### 2. Chloe (Manager: Ravi) * Proposed by Manager: Exceeds * My Proposed Rating: Meets * Evidence & Rationale: * Goal 1: Grow API usage 20% $\rightarrow$ Result: +4% (Severe miss). * Goal 2: Launch partner portal $\rightarrow$ Result: Shipped on time

Grades and run details

Decision model 65 · LLM judge 14

Decision model checks

passUses the supplied evidence correctly70%
passAddresses the actual decision28%
partialRespects explicit constraints20%
failIdentifies material uncertainty88%
passAvoids unsupported claims33%
partialProduces the required deliverable75%
partialCatches the inflated team95%
failRescues Gita from one bad launch83%
failNotices who's underrated95%
passClear and direct, with care55%
passJudges outcomes, not activity98%
passNames gaps you could see69%
passWeighs the whole period21%

Run

Run: #1
Time to output: 3 s
Submitted: 5 Oct 2026

GPT-6.1 SolAPI

Usable with a quick edit13 of 13 checks passed

The pre-read is well-structured, uses evidence correctly, and makes clear proposals. A PM could use it with at most light editing for tone or emphasis, without re-checking facts or redoing reasoning.

Output

Thursday calibration pre-read

From: Director of Product, Northgate Scope: 14 PMs across Ravi, Sara and Tom Status: Facilitator proposals—not final ratings. Managers should respond with additional evidence before we finalize.

Calibration approach

We will assess outcomes against goals first, then how the work was done. Shipping, responsiveness, strong specifications and leadership visibility are relevant context, but do not substitute for customer or business outcomes.

Exceeds requires written evidence of clear outperformance, not simply completing planned work. Below requires both evidence of underperformance and documented earlier feedback about the gap. Missing feedback does not make weak performance satisfactory; it does mean we cannot support Below on the submitted record.

Ratings I propose changing

PM	Proposed change	Evidence and rationale
Aisha — Ravi	Greatly exceeds → Meets	Trial conversion reached 8.4% against an 11% goal, from an 8% baseline. Self-serve billing shipped, but no resulting business or customer impact is supplied. Nine features, energy and leadership enthusiasm do not establish exceptional outcomes. The conversion miss is substantial; without documented earlier feedback, the record also does not support Below.
Chloe — Ravi	Exceeds → Meets	API usage grew 4% against a 20% goal. The partner portal launched on time, but its adoption or impact is not documented. Strong specifications and timely delivery do not offset the outcome shortfall sufficiently to support Exceeds.
Emma — Ravi	Exceeds → Meets	Expansion revenue grew 6% against a 15% goal. The usage dashboard and six Sales requests shipped, but there is no evidence that they delivered additional outcomes. Responsiveness to Sales is positive working behavior, not evidence of exceeding the revenue goal.
Gita — Sara	Below → Meets	Online invoice payment reached 47% against a 45% goal, from 35%—a strong outcome. Reminders v2 was rolled back after four days because Support was not briefed, a material execution and coordination failure. However, the submission contains no documented earlier feedback on that gap, which is required for Below. Meets recognizes the mixed record; it does not excuse the launch failure.
Ines — Tom	Exceeds → Meets	Checkout drop-off fell to 21% against a 22% goal, from 30%, and two payment methods shipped. This is successful delivery with some outcome overperformance. On the current record, however, modest overdelivery on checkout plus a launch does not establish clear overall outperformance. Adoption, incremental conversion or revenue from the payment methods could strengthen the case for Exceeds.
Nora — Sara	Meets → Exceeds	Payment success reached 95.4% against a 95% goal, from 92%; payment support tickets fell 35% against a 30% reduction goal. Both measured outcomes surpassed their targets. This is comparable to the outcome-based cases supporting Ben and Dev. “Quiet” should neither diminish nor enhance the rating.

Ratings I propose keeping

PM	Keep	Evidence and rationale
Ben — Ravi	Exceeds	Onboarding fell to 4.5 days against a five-day goal, from ten; activation reached 39% against 38%, from 30%. Both customer outcomes exceeded their targets.
Dev — Ravi	Exceeds	Support tickets per account fell 27% against a 25% reduction goal; NPS reached 41 against 40, from 31. Both outcomes surpassed targets. Mentoring two APMs strengthens the assessment of how the work was done, but is not the primary justification.
Femi — Sara	Meets	Weekly retention reached 46%, exactly the goal, from 41%; new-account churn fell 22% against a 20% reduction goal. Strong goal attainment, with some overdelivery, supports Meets rather than a clear-outperformance rating.
Hugo — Sara	Meets	Mobile v2 shipped late, and mobile weekly actives grew only 3% against a 20% goal. “Delivered a hard project” does not establish outcome success. This is a significant concern, but no documented earlier feedback is supplied to support Below.
Jon — Tom	Meets	Search success improved from 60% to 66% against a 70% goal; filters shipped. Meaningful progress, but the principal outcome remained short of target.
Kai — Tom	Meets	Refund rate reached 3.1% against a 3% goal, from 4%; fraud losses fell 24% against a 20% reduction goal. One near miss and one exceeded outcome support Meets overall.
Lena — Tom	Below	Listings grew 9% against a 25% goal, and seller analytics did not launch. The submission explicitly cites documented Q2 and Q3 1:1 feedback. Both the performance gap and earlier-feedback requirement are evidenced.
Mo — Tom	Meets	Time to first sale fell to 15 days against a 14-day goal, from 21; seller NPS rose six points against five. Near attainment on one outcome and overdelivery on the other support Meets. Stakeholder work is positive supporting context.

Note to Ravi

Your submission rates all five PMs above Meets, but three cases rely heavily on shipping, responsiveness or visibility despite substantial misses on their stated outcome goals. Please revisit Aisha, Chloe and Emma using the same outcome standard that supports Ben and Dev. If you have additional attributable impact or documented earlier feedback, bring it before Thursday. We are seeking consistent judgments, not enforcing a quota.

Proposed spread: 10 Meets, three Exceeds and one Below.

Grades and run details

Decision model 100 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly68%
passAddresses the actual decision84%
passRespects explicit constraints49%
passIdentifies material uncertainty74%
passAvoids unsupported claims67%
passProduces the required deliverable90%
passCatches the inflated team100%
passRescues Gita from one bad launch100%
passNotices who's underrated100%
passClear and direct, with care98%
passJudges outcomes, not activity99%
passNames gaps you could see81%
passWeighs the whole period79%

Run

Run: #1
API response time: 37 s
Submitted: 6 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 11

Addresses the actual decisionRightMixedRight

Haiku 5.5 · API

The output clearly proposes specific rating changes, keeps, and holds, with conditions (missing documentation) that would alter the calls.

Gemini 3.5 Flash-Lite · Gemini

The output is cut off and does not commit to a complete answer; it lacks the ratings to keep and the note to Ravi.

GPT-6.1 Sol · API

The pre-read commits to specific rating changes and keeps, and states that additional evidence from managers could change the ratings before finalization.

Respects explicit constraintsRightWrongRight

Haiku 5.5 · API

The output is a pre-read within 1,100 words, addresses the required elements (changes, keeps, note to Ravi), and follows the format.

Gemini 3.5 Flash-Lite · Gemini

The brief requires a pre-read with changed ratings, kept ratings, and a note to Ravi; the output is incomplete and omits the keep list and note.

GPT-6.1 Sol · API

The output is a pre-read under 1,100 words, includes the ratings to change and keep with evidence, and a short note to Ravi, as requested.

Identifies material uncertaintyRightWrongRight

Haiku 5.5 · API

It identifies the unknowns (documentation for Gita and Hugo, adoption data for Ines) and says how they would resolve the ratings.

Gemini 3.5 Flash-Lite · Gemini

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

GPT-6.1 Sol · API

It names unknowns (e.g., missing adoption/revenue data for Ines, possible additional evidence from Ravi) and says how they would be resolved or change the call.

Avoids unsupported claimsRightMixedRight

Haiku 5.5 · API

Interpretations (e.g., '40% of the goal') are clearly based on arithmetic; no causes or forecasts are presented as established fact without support.

Gemini 3.5 Flash-Lite · Gemini

It proposes a Below rating for Aisha without the documented earlier feedback the guidance requires, presenting the rating as justified when the evidence does not support it.

GPT-6.1 Sol · API

Interpretations and judgments are clearly presented as the facilitator's proposals, not as established facts, and no unsupported causal claims are made.

Produces the required deliverableRightWrongRight

Haiku 5.5 · API

The pre-read is complete with changes, keeps, a dedicated note to Ravi, and could be used by a PM with only minor edits.

Gemini 3.5 Flash-Lite · Gemini

The pre-read is incomplete, missing the keep list and Ravi note, and cannot be acted on without major gaps.

GPT-6.1 Sol · API

The pre-read is complete, in the right form for the Director of Product, within the word limit, and usable as-is for the calibration meeting.

Catches the inflated teamRightWrongRight

Haiku 5.5 · API

It names Aisha, Chloe and Emma, cites their outcome misses, proposes lowering them to Meets, and keeps Ben and Dev at Exceeds.

Gemini 3.5 Flash-Lite · Gemini

It names Aisha and Chloe but does not mention Emma, so it fails to catch all three inflated ratings.

GPT-6.1 Sol · API

The pre-read names Aisha, Chloe, and Emma with their missed outcome goals and proposes lowering them to Meets, while keeping Ben and Dev at Exceeds.

Rescues Gita from one bad launchRightWrongRight

Haiku 5.5 · API

It raises Gita to Meets citing her beat on the main goal and the lack of documented feedback, and asks for the launch gap to be documented.

Gemini 3.5 Flash-Lite · Gemini

Gita is not mentioned at all, so the output does not rescue her from the Below rating.

GPT-6.1 Sol · API

It raises Gita to Meets, noting she beat her main goal and the Below rating rests on one launch without the required documented earlier feedback, and implies the gap should be documented now.

Notices who's underratedRightWrongRight

Haiku 5.5 · API

It raises Nora from Meets to Exceeds because she beat both outcome goals, using her payment success and ticket reduction results.

Gemini 3.5 Flash-Lite · Gemini

Nora is not mentioned, so the output does not notice she is underrated.

GPT-6.1 Sol · API

It raises Nora to Exceeds, citing that she beat both outcome goals, and notes that 'quiet' should not affect the rating.

Clear and direct, with careRightMixedRight

Haiku 5.5 · API

The note to Ravi is direct, names the three PMs and the numbers, asks for specific outcome analyses and feedback documentation, and offers support.

Gemini 3.5 Flash-Lite · Gemini

The output is incomplete and lacks the full note to Ravi; it does not say plainly everything that needs to change.

GPT-6.1 Sol · API

The note to Ravi is direct and respectful, stating exactly what needs to change and what evidence would help, and the whole pre-read is clear and actionable.

Names gaps you could seeRightMixedRight

Haiku 5.5 · API

Gaps are described as specific results (conversion 8.4% vs 11%, API +4% vs +20%, app late and actives +3%, launch rollback) and what good would look like is implied by the goals.

Gemini 3.5 Flash-Lite · Gemini

It does not describe gaps as specific, observable behaviors with what good would look like; it only states missed targets.

GPT-6.1 Sol · API

Gaps are described as specific, observable results (e.g., 'trial conversion reached 8.4% against an 11% goal', 'reminders v2 rolled back because support was not briefed').

Weighs the whole periodRightMixedRight

Haiku 5.5 · API

The review uses the full-year goal results for every PM, and Gita's launch failure does not dominate her rating because the other goal was met and documentation is absent.

Gemini 3.5 Flash-Lite · Gemini

It does not weigh the whole review period or guard against recency; it only mentions goal results without context of the full period.

GPT-6.1 Sol · API

The pre-read weighs the full year's goals and results for each PM, and for Gita it explicitly balances the strong main goal with the launch failure, avoiding recency bias.

All got right 2

Uses the supplied evidence correctlyRightRightRight

Haiku 5.5 · API

All claimed numbers and facts appear in the supplied CSV or are derived by straightforward arithmetic; nothing about the current situation is invented.

Gemini 3.5 Flash-Lite · Gemini

All factual claims about current performance and results are taken correctly from the supplied context.

GPT-6.1 Sol · API

Every factual claim about current performance, results, and documented feedback is taken directly from the supplied proposed_ratings.csv or follows by arithmetic.

Judges outcomes, not activityRightRightRight

Haiku 5.5 · API

Every rating change is argued from outcome attainment against goals, with outputs and activity treated as context not the result.

Gemini 3.5 Flash-Lite · Gemini

For the parts it covers, it judges on outcomes against goals, not on activity or shipping.

GPT-6.1 Sol · API

Every rating is justified by outcomes against goals, with shipping and activity treated as context, not the result.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	93.8	92.3	2	None
2	Haiku 5.5withAPI	93.8	88.5	2	None
3	GPT-6.1 SolwithAPI	97.9	84.6	2	None
4	Opus 5.5withClaude	91.8	85.2	2	None
5	GPT-6 LunawithAPI	94.1	77.7	2	None
6	Gemini 3.8 FlashwithAPI	89.7	76.9	2	None
7	Sonnet 5.5withAPI	90.1	74.2	2	None
8	Gemini 3.5 Flash-LitewithGemini	74.4	49.5	2	None

What you get for the cost

Haiku 5.5 · API matches GPT-6 Astra · ChatGPT at about a fortieth of the cost.

Best valueNobody 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)
GPT-6 Luna · API	82	1.0×
Haiku 5.5 · API	91	1.7×
Gemini 3.5 Flash-Lite · Gemini	61	1.9× (partly estimated)
Gemini 3.8 Flash · API	82	12×
GPT-6.1 Sol · API	89	13×
Sonnet 5.5 · API	81	31×
Opus 5.5 · Claude	87	61×
GPT-6 Astra · ChatGPT	91	70×

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

Reviewing the performance of a PM you manage.

Why it matters

Reviews fail by being kind and vague, or by judging a whole year on its worst month. Either way, the person doesn't know what to change.

What good looks like

Clear and direct, with care
Judges outcomes, not activity
Names gaps in observable terms
Weighs the whole period, not the last month

Deliberately not measured

HR policy compliance beyond what the brief supplies

Capability tested

Feedback and judgement

The failure we’re looking for

Vague, diplomatic feedback, or a verdict driven by one recent event

Grading

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

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

Reliably right

Where it slips

How it’s graded

Clear and direct, with care

Judges outcomes, not activity

Names gaps you could see

Weighs the whole period

Plus our standard checks

The tasks

The outputs

Output

Calibration pre-read: 14 PMs, Ravi / Sara / Tom

Ratings I'd change

Ratings on hold (need documentation before Thursday)

Ratings I'd keep

Note to Ravi

Expected spread after changes

Grades and run details

Decision model checks

Run

Output

Part 1: Proposed Rating Changes & Rationale

Grades and run details

Decision model checks

Run

Output

Thursday calibration pre-read

Calibration approach

Ratings I propose changing

Ratings I propose keeping

Note to Ravi

Grades and run details

Decision model checks

Run

Check by check

Where they differ 11

All got right 2

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured