Tasks / Metrics & Experimentation

Draft OKRs

Can the model turn a team's wish list into a few outcome-based OKRs that add up to the company's goals?

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. 50% were usable with at most a quick edit.

Reliably right

Builds the key results on the data100% pass
Key results 2 and 3 are built directly on the first-week joining data and time-to-shared-note data, with baselines from the brief and targets.
GPT-6.1 Sol · API · Eleven key results and a bonus
Respects explicit constraints99% pass
The output is a memo to Grace Mensah, within the 1,100-word limit, and includes the requested diagnosis and team OKRs with baselines and targets.
GPT-6 Astra · ChatGPT · A great quarter, on paper
Produces the required deliverable99% pass
The memo is complete, in the right form for the CPO, and a PM could act on it with only light edits.
GPT-6 Astra · ChatGPT · A great quarter, on paper

Where it slips

Flags the bonus link47% pass
Mentions bonus multipliers only to justify outcome KRs; does not name the risk of safe targets or suggest decoupling OKR scores from bonuses.
Haiku 5.5 · API · Eleven key results and a bonus
Avoids unsupported claims67% pass
Presents 'the gap is correlation' and 'the safe play is to pick easy KRs' as facts without labeling them as hypotheses.
Sonnet 5.5 · API · Eleven key results and a bonus
Identifies material uncertainty68% pass
The output does not name any specific unknowns that could change the OKRs or how they would be resolved.
GPT-6 Luna · API · Eleven key results and a bonus

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.

Key results are outcomes, not output
Is every key result a measurable change in customer or business behaviour, rather than something the team will ship or do?
Passes when Every key result names a metric with a baseline and a target; shipping work appears only as initiatives that serve them.
Matt LeMayLenny’s Podcast
Focuses on the big rock
Does the output cut the goals down to the few that matter most, rather than covering everything the team could do?
Passes when Few objectives, each with a small number of key results, and says what was dropped and why.
Christina WodtkeLenny’s Podcast
Shows how the goals add up
Does the output show how each team-level key result contributes to the company-level goals, and flag any that don't?
Passes when Every key result is linked to a company goal, with the reasoning or data that connects them, and any that serve no company goal are flagged or cut.
Matt LeMayLenny’s Podcast Claire Hughes JohnsonLenny’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 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 a Staff PM at Pathwise, working for the CPO, Grace Mensah. Last quarter's OKR scorecard is below, along with the company goal it was meant to serve. Grace wants a memo of no more than 1,100 words: what last quarter's OKRs actually tell us (not just the scores), and next quarter's OKRs for the Learner app, Admin and Growth teams, each with baselines and targets, that add up to the company goal.\n\nThe files are attached.

What the model was given5 items: About Pathwise, company_okr_q3.md, okr_scorecard_q3.csv (team_label is the type each team gave its key result), metric_dictionary.md, churn_reasons_q3.md

About PathwiseOnline learning for companies: courses employees take, and tools for the HR and L&D teams who buy it. Sold per seat on annual contracts.

company_okr_q3.md7 lines · Download

# Company OKR, Q3 (set by the CEO)

Objective: grow by keeping and expanding the customers we have.
- Net revenue retention: from 103% to 108%. Actual: 102%.
- Quarterly logo churn: from 3.1% to 2.5%. Actual: 3.4%.

Q3 average key result score across product teams: 0.81. The all-hands slide said "a great quarter".

okr_scorecard_q3.csv (team_label is the type each team gave its key result)team,objective,key_result,team_label,baseline,target,actual,score Content,Best course library in L&D,Publish 40 new courses,output,0,40,41,1.00 Content,Best course library in L&D,Course completion rate,outcome,41,55,58,1.00 Content,Best course library in L&D,Translate top 20 courses into 3 languages,output,0,60,60,1.00 Learner app,Learning people love,Ship mobile offline mode,output,0,1,1,1.00 Learner app,Learning people love,Weekly active learners per seat,outcome,0.31,0.4,0.29,0.00 Learner app,Learning people love,Ship streaks and badges,output,0,1,1,1.00 Learner app,Learning people love,App store rating,outcome,4.2,4.6,4.5,0.75 Admin,Admins get value fast,Launch 3 HRIS integrations,outcome,0,3,3,1.00 Admin,Admins get value fast,Days from contract to first learner,outcome,34,14,31,0.15 Admin,Admins get value fast,Ship new reporting dashboard,output,0,1,1,1.00 Growth,Expand inside accounts,Seats added in existing accounts,outcome,9200,14000,8100,0.00 Growth,Expand inside accounts,Run 12 expansion experiments,output,0,12,13,1.00 Growth,Expand inside accounts,Launch in-app upgrade prompts,output,0,1,1,1.00 Platform,Reliable at scale,Uptime,outcome,99.5,99.9,99.93,1.00 Platform,Reliable at scale,Migrate to new video CDN,output,0,1,1,1.00 Platform,Reliable at scale,Close 150 bugs,output,0,150,162,1.00

metric_dictionary.md5 lines · Download

# Metric dictionary (excerpt)

- Course completion rate: share of course enrolments marked complete. Until 31 July "complete" meant every module finished. From 1 August it means 80% of modules finished (changed so completion matches the certificate rule).
- Weekly active learners per seat: learners who start or continue a course in a week, divided by paid seats.
- Days from contract to first learner: calendar days from contract signature to the first learner starting a course.

churn_reasons_q3.md8 lines · Download

# Why accounts left in Q3 (exit interviews, 38 accounts)

- "Our people didn't use it": 17
- Budget cut: 9
- Moved to the LMS that came with our HR system: 7
- Content didn't fit our industry: 5

…

What a strong answer doesThe answer key the graders mark against

Reads the scorecard for what it is: the average is 0.81, but every output key result scored 1.00 (things shipped) while outcome key results averaged about 0.48, and the company missed both goals (NRR 102% against 108%, churn 3.4% against 2.5%). Two scores flatter: 'Launch 3 HRIS integrations' is labelled an outcome but is output, and course completion only 'beat' its target because completion was redefined on 1 August (all modules to 80%), so the result can't be compared with its baseline. The outcome key results that matter for churn moved backwards or barely (weekly active learners per seat 0.31 to 0.29; days to first learner 34 to 31; seats added 8,100 against 9,200). Builds next quarter's OKRs on the churn evidence: low usage is the top exit reason, and accounts reaching a first learner within 14 days churn at a third of the rate. So Admin owns days from contract to first learner (from 31), Learner app owns weekly active learners per seat (from 0.29), Growth owns seats added in existing accounts or net expansion, each with a baseline from the files and a target, with shipping work as initiatives. Notes the completion metric needs restating on one definition before anyone uses it.

Critical failures (cap the score at 40)

Describes last quarter as a success on the strength of the 0.81 average

Task

Staff-level · v1.1

The outputs

9 setups answered this brief.

GPT-6 AstraChatGPT100Usable with a quick edit GPT-6.1 SolAPI100Usable with a quick edit Sonnet 5.5API100Usable with a quick edit Haiku 5.5API96Usable with a quick edit Opus 5.5Claude88Needs more work GPT-6 LunaAPI85Usable with a quick edit Gemini 4 ArgonAPI77Needs more work Gemini 3.8 FlashAPI58Needs more work Gemini 3.5 Flash-LiteGemini33Needs more work

Gemini 4 ArgonAPI

Needs more than a quick edit8 of 11 checks passed

The unsupported causal claims (e.g., about gamification and budget cut causes) would need verification or rewriting before a PM could use it without re-checking evidence or reasoning.

Output

MEMO To: Grace Mensah, CPO From: Staff PM Date: Start of Q4 Subject: Q3 OKR Post-Mortem and Proposed Q4 Product OKRs

1. What Q3’s OKRs Actually Tell Us

At the all-hands, we celebrated a 0.81 average OKR score while the company failed its only two goals: Net Revenue Retention (NRR) dropped from 103% to 102% (target: 108%), and logo churn rose from 3.1% to 3.4% (target: 2.5%).

We achieved high team scores while losing customers because our OKR system rewards shipping features over changing customer behavior.

Outputs disguised as outcomes inflated our score. Ten of our 16 KRs were binary tasks (shipping features, running experiments, closing bugs). The Admin team even mislabeled "Launch 3 HRIS integrations" as an outcome. All 10 output KRs scored 1.00. If we strip out outputs and look only at true outcomes, our average score drops to 0.48. If we further remove technical uptime, the score for customer-behavior outcomes is 0.38.

The commercial outcomes we actually needed to move went backward. * Learner Engagement (0.00): Weekly active learners per seat (WAL/seat) fell from 0.31 to 0.29. Over 70% of paid seats sit unused each week. * Onboarding Speed (0.15): Days from contract to first learner barely budged from 34 to 31 days (target: 14). * Account Expansion (0.00): Seats added in existing accounts fell from 9,200 to 8,100 (target: 14,000).

Our three outcome wins were disconnected from retention: 1. Course completion rate (41% to 58%, score 1.00): This was largely a mirage. On August 1, we lowered the definition of "complete" from 100% of modules finished to 80% to match our certificate rule. We moved the goalposts mid-quarter and claimed a win. 2. App store rating (4.2 to 4.5, score 0.75): A vanity metric. It takes a tiny fraction of happy users to rate an app, which masks the fact that overall weekly usage shrank. 3. Uptime (99.93%, score 1.00): Good hygiene from Platform, but high availability doesn’t stop churn if nobody logs in.

Our outputs failed to move the outcomes they sat next to. * Admin shipped 3 HRIS integrations, yet time-to-first-learner stayed stuck at a month. Shipping an integration means nothing if admins don't adopt it during onboarding. * Learner shipped offline mode, streaks, and badges, yet weekly active usage dropped. Gamification only engages users who are already active; it doesn't get dormant seats in the door. * Growth ran 13 experiments and shipped upgrade prompts, yet added fewer seats than in Q2. You cannot prompt an admin to buy more seats when 71% of the seats they already pay for are gathering dust.

The Root Cause of Q3 Churn Exit interviews from 38 churned accounts make the causal chain clear: * 45% left because "our people didn't use it" (17/38). Another 24% (9/38) cited budget cuts, and 18% (7/38) consolidated into their HR system’s bundled LMS—both of which happen when an L&D team cannot prove ROI on Pathwise usage. * Crucially, accounts that reached their first learner within 14 days churned at a third of the rate of the rest. Right now, our average account takes 31 days—meaning the majority of new cohorts start life in the high-churn danger zone.

---

2. How Q4 OKRs Add Up to the Company Goal

To hit 108% NRR and 2.5% logo churn, Product must fix the sequence of value creation for an annual seat-based business:

Admin must compress onboarding under the 14-day threshold and get seats assigned, cutting early structural churn.
Learner must turn assigned seats into active weekly learners—both to stop the #1 churn reason ("people didn't use it") and to eat up seat capacity.
Growth must convert high seat utilization into mid-contract seat expansion and give L&D buyers the ROI proof they need to defend their budget at renewal.

---

3. Proposed Q4 OKRs (Learner, Admin, Growth)

Every KR below is a measurable customer behavior or business outcome. No roadmaps, features, or experiment counts are permitted on the scorecard.

#### Admin Team Role in Company OKR: Cut logo churn by getting accounts past the 14-day survival mark and driving seat provisioning.

Objective: Get every new account to fast, broad adoption. * KR 1: Reduce average days from contract signature to first learner from 31 days to 14 days. * KR 2: Increase the share of new accounts reaching their first active learner within 14 days from 28% (est. Q3 baseline) to 75%. (Note: Admin to confirm exact Q3 percentage in week 1). * KR 3: Increase average seat assignment rate (paid seats assigned to a named employee via HRIS or invite) within 30 days of contract from 52% (est. baseline) to 85%.

#### Learner App Team Role in Company OKR: Eliminate the #1 churn driver ("people didn't use it") by activating dormant seats and building a weekly learning habit.

Objective: Turn paid seats into weekly active learners. * KR 1: Increase Weekly Active Learners per paid seat (WAL/seat) from 0.29 to 0.42. * KR 2: Increase 30-day assigned-seat activation rate (share of newly assigned seats that start at least one course within 30 days) from 45% (est. baseline) to 70%. * KR 3: Increase course completion rate—held strictly to the constant 80%-module definition—from 58% to 68%.

#### Growth Team Role in Company OKR: Drive NRR from 102% to 108% by expanding capacity-constrained accounts and arming buyers to prevent renewal down-sell.

Objective: Expand high-usage accounts and lock in renewals. * KR 1: Increase quarterly seats added in existing accounts from 8,100 to 14,500 (sized to make up the Q3 shortfall and hit 108% NRR). * KR 2: Increase the quarterly seat-expansion conversion rate among "at-capacity" accounts (accounts with >80% of paid seats assigned and active) from 12% (est. baseline) to 28%. * KR 3: Reduce gross revenue contraction from seat reductions at renewal from 4.5% (est. Q3 baseline) to 2.0% of renewable ARR (e.g., by proving ROI via the new reporting dashboard before renewal conversations).

---

4. Guardrails for Q4 Execution

Freeze the Metric Dictionary: No metric definition may change mid-quarter without CPO sign-off and a retroactive restatement of the baseline.
Track Outputs in Sprint Reviews, Not OKRs: Shipping features, running 12 experiments, or launching integrations are how we try to move KRs. If an experiment ships and the metric doesn’t move, the score is 0.00—which tells us in week 4 to pivot our approach before the quarter is lost.
Confirm Baselines by Friday: Where Q3 tracking didn't capture cohort rates (Admin KR2/KR3, Learner KR2, Growth KR2/KR3), Data and PM leads will pull exact Q3 actuals by Friday so our baselines are locked before Q4 kickoff.

Grades and run details

Decision model 77 · LLM judge 83

Decision model checks

failUses the supplied evidence correctly76%
failAddresses the actual decision20%
passRespects explicit constraints28%
passIdentifies material uncertainty54%
partialAvoids unsupported claims43%
passProduces the required deliverable74%
passReads the scorecard for what it is100%
passCatches the redefined key result98%
passKey results are outcomes, not output97%
passFocuses on the big rock49%
passShows how the goals add up62%

Run

Run: #1
API response time: 22 s
Submitted: 13 Oct 2026

Haiku 5.5API

Usable with a quick edit11 of 11 checks passed

The memo is logically structured, rooted in the evidence, and could be sent with minimal edits after pulling the TBD baselines and confirming the proposed company targets.

Output

To: Grace Mensah, CPO From: Staff PM Re: What Q3 OKRs told us, and Q4 team OKRs that add up to the company goal

Bottom line

Q3 looked like a good quarter (0.81 average) but failed on the goal it was meant to serve. We shipped almost everything we planned and moved the customer numbers the wrong way. Q4 needs to be built around the three things customers told us drive churn: people not using the product, slow time to first learner, and seat growth that depends on usage. Output shipping stays, but it no longer counts as success on its own.

What Q3 actually tells us

The 0.81 is a shipping score. Of 16 key results, nine were outputs (things we built, published, or closed). All nine scored 1.00. Outcomes scored 0.56 on average, and the outcome KRs that map to the company goal mostly failed:

Net revenue retention: 102% against a 108% target, and down from the 103% baseline.
Quarterly logo churn: 3.4% against a 2.5% target, and worse than the 3.1% baseline.
Seats added in existing accounts: 8,100, down from 9,200. Growth shipped 13 experiments and a prompt, and expansion still fell.
Weekly active learners per seat: 0.29, down from 0.31. Usage declined while we shipped streaks and offline mode.
Days from contract to first learner: 31, down from 34 against a target of 14. We barely moved the number that most affects churn.

The bright spots are real but don't map to the goal: app rating rose from 4.2 to 4.5, and uptime is ahead of target.

Why the goal didn't move: the team KRs never owned it. No team had NRR or churn as its KR. Each team optimized its own outputs, and the company numbers fell in the gaps between them.

The exit interviews say where the gaps are. Of 38 accounts that left in Q3:

17 (45%): "Our people didn't use it." This is adoption, which sits with the Learner app and with the onboarding that Admin owns.
9 (24%): budget cut. Hard to move directly, but more likely when usage is low.
7 (18%): moved to the LMS that came with their HR system. Our three HRIS integrations did not stop this. We need to know which HR systems these accounts used.
5 (13%): content didn't fit their industry. This is Content's problem, and it isn't in this memo's scope.

The most useful finding is the activation data: accounts that reached a first learner within 14 days churned at one-third the rate of the rest. Our average is 31 days. Activation speed is probably our highest-leverage lever, and Q3 barely touched it.

The completion rate score can't be trusted. On 1 August we changed the definition of "complete" from every module finished to 80% of modules finished. The 41→58 movement (scored 1.00) mixes two definitions, and the new definition is looser. Some or all of that gain is a measurement change, not learning. We should restate the Q2 baseline on the new definition before anyone cites this number. Until then it should not count as a win.

A smaller measurement issue: "Launch 3 HRIS integrations" was labeled an outcome but is really a count of shipped work. It should be an output, paired with an adoption outcome.

What this means for Q4

We should commit to the company numbers and make the team KRs add up to them. My proposal:

Net revenue retention: 102% → 106% at Q4 end (108% stays the year-end target, but I don't think it's reachable in one quarter from 102%).
Quarterly logo churn: 3.4% → 2.8%.

Churn and expansion both depend on usage, so I've set team targets on usage and activation rather than on shipping. Finance should confirm how much each lever contributes to NRR. I've assumed both churn and seat expansion have to move for the 106% target to hold.

Q4 team OKRs

Each team has two outcome KRs. Output KRs can sit underneath as supporting work but are not scored toward the goal. "TBD" baselines need to be pulled in week one, before targets are locked.

Team	Objective	Key result	Q3 baseline	Q4 target
Learner app	Learners come back every week	Weekly active learners per seat	0.29	0.35
		Share of learners active in week 2 after their first course	TBD (pull week 1)	Set week 2; expect +10 pts
Admin	Accounts get a first learner fast	Median days from contract to first learner	31	21
		Share of new contracts reaching first learner within 14 days	TBD (pull week 1)	Set week 2 once baseline is known
Growth	Expansion follows real usage	Seats added in existing accounts	8,100	10,500
		Renewal-risk accounts (low usage, renewal in 90 days) with a named save plan	TBD (pull week 1)	100% covered; save rate tracked

How the teams connect to the goal

Learner app reduces the 17 "didn't use it" churns. Higher weekly usage also feeds expansion, since seat growth needs people actually learning.
Admin targets the 14-day activation link. Getting the median from 31 to 21 days should move the share of accounts inside 14 days, which is where the churn gap is. The HRIS KR should be reviewed in week 2 by checking which HR systems the seven accounts that left for their HR LMS were using.
Growth stops measuring success as experiments run and starts measuring seats added in accounts that use the product. Expansion in low-usage accounts is a risk, not a win, so the renewal-risk KR makes Growth own the save motion for the budget-cut and low-usage accounts.

Decisions I need from you

Agree the company targets (NRR 106%, churn 2.8%), or tell me to hold 108% and accept the risk.
Restate completion rate. Finance or Data should back-calculate the Q2 baseline under the new definition. Until then Content's completion KR cannot be scored.
Assign the churn-reason KR to a single owner. Right now no one owns the 38 exit interviews. I suggest Customer Success, reporting to you monthly.
Content's fit problem. Five churns cited content fit. Content's Q4 OKRs should include an industry-fit KR. I'd like to discuss this with the Content lead before it's locked.

I can have the baselines pulled and the final targets locked by the end of week two.

Grades and run details

Decision model 91 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly51%
partialAddresses the actual decision34%
passRespects explicit constraints45%
passIdentifies material uncertainty82%
partialAvoids unsupported claims28%
passProduces the required deliverable79%
passReads the scorecard for what it is92%
passCatches the redefined key result100%
passKey results are outcomes, not output76%
passFocuses on the big rock77%
passShows how the goals add up69%

Run

Run: #1
API response time: 32 s
Submitted: 8 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 3

Uses the supplied evidence correctlyMixedRight

Gemini 4 Argon · API

All factual statements about the current situation are sourced from the provided context or follow directly by arithmetic.

Haiku 5.5 · API

All factual claims about the current situation are directly drawn from the supplied context or derived by arithmetic, with no invented facts.

Addresses the actual decisionMixedRight

Gemini 4 Argon · API

The memo commits to specific Q4 OKRs for each team, framed for the CPO, and conditionally relies on baseline confirmation to finalise.

Haiku 5.5 · API

The memo commits to clear next-quarter OKRs for the three teams with baselines and targets, and also proposes company-level targets while asking for CPO decision on them.

Avoids unsupported claimsWrongRight

Gemini 4 Argon · API

Several causal claims are presented as settled fact without evidence (e.g., 'gamification only engages users who are already active', 'both [budget cuts and consolidation] happen when an L&D team cannot prove ROI').

Haiku 5.5 · API

Interpretations like the disconnect between team KRs and company goals are grounded in the scorecard data; no cause or forecast is presented as fact without evidence, and the tone remains analytical.

All got right 8

Respects explicit constraintsRightRight

Gemini 4 Argon · API

The memo is well within 1,100 words, addresses the CPO as requested, and delivers the required analysis and OKRs.

Haiku 5.5 · API

The output is a memo under 1,100 words, addressed to the CPO, includes the requested analysis and team OKRs with baselines and targets, and proposes them for the named teams.

Identifies material uncertaintyRightRight

Gemini 4 Argon · API

It identifies estimated baselines and requires exact Q3 actuals by week 1, acknowledging uncertainty in those numbers.

Haiku 5.5 · API

The memo flags TBD baselines that need pulling, the need to restate the completion rate baseline, the unknown HR systems behind churn, and the lever contributions to NRR, bounding uncertainty and stating what would resolve it.

Produces the required deliverableRightRight

Gemini 4 Argon · API

The memo is a complete deliverable with analysis and team-level OKRs with baselines and targets, usable as-is.

Haiku 5.5 · API

The memo is a complete, actionable deliverable for the CPO: it analyses Q3, sets Q4 team OKRs with metrics, flags open decisions, and fits within the word limit.

Reads the scorecard for what it isRightRight

Gemini 4 Argon · API

It separates output and outcome scores, shows outcome average 0.48, and sets them against the missed company goals.

Haiku 5.5 · API

The memo explicitly separates output KRs (all 1.00) from outcome KRs (average ~0.56) and sets them against the missed company goals of NRR and churn, without relying on the 0.81 average.

Catches the redefined key resultRightRight

Gemini 4 Argon · API

It flags both the course completion definition change and the mislabeled HRIS integration output, explaining how they inflated scores.

Haiku 5.5 · API

The memo flags both the 1 August redefinition of course completion that invalidates the baseline comparison and the mislabeling of 'Launch 3 HRIS integrations' as an outcome.

Key results are outcomes, not outputRightRight

Gemini 4 Argon · API

All proposed key results are outcome metrics with baselines and targets; no shipping or activity-based KRs appear on the scorecard.

Haiku 5.5 · API

All proposed Q4 key results are behavioural or business metrics with baselines and targets; shipping work is demoted to supporting initiatives.

Focuses on the big rockRightRight

Gemini 4 Argon · API

Each team has one objective and three key results, with explicit reasoning that previous output KRs are dropped because they failed to move outcomes.

Haiku 5.5 · API

The proposal gives each team two outcome KRs tied to activation and usage, explicitly drops output KRs from scoring, and leaves Content's fit problem for separate discussion.

Shows how the goals add upRightRight

Gemini 4 Argon · API

The memo explicitly links each team's KRs to the company goal of NRR and churn, with a clear laddering narrative.

Haiku 5.5 · API

The 'How the teams connect to the goal' section links each KR to the company NRR/churn goals, explaining that Learner app addresses 'didn’t use it' churn, Admin targets 14‑day activation, and Growth shifts to usage‑linked expansion.

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 100% of 12 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	97.7	100.0	2	None
2	GPT-6.1 SolwithAPI	97.7	100.0	2	None
3	Sonnet 5.5withAPI	100.0	91.7	2	None
4	Haiku 5.5withAPI	93.2	95.8	2	None
5	Opus 5.5withClaude	95.5	79.2	2	None
6	GPT-6 LunawithAPI	79.5	79.2	2	None
7	Gemini 4 ArgonwithAPI	77.3	70.8	2	None
8	Gemini 3.8 FlashwithAPI	61.4	62.5	2	None
9	Gemini 3.5 Flash-LitewithGemini	59.1	45.8	2	None

What you get for the cost

Haiku 5.5 · API scores within 4 points of GPT-6 Astra · ChatGPT at about a thirtieth of the cost.

Best valueNobody beats these on both score and cost

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	78	1.0×
Gemini 3.5 Flash-Lite · Gemini	50	2.1×
Haiku 5.5 · API	95	2.1×
Gemini 3.8 Flash · API	59	13×
Gemini 4 Argon · API	71	13×
GPT-6.1 Sol · API	99	16×
Sonnet 5.5 · API	96	44×
Opus 5.5 · Claude	83	78×
GPT-6 Astra · ChatGPT	99	83×

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

Setting a team's goals for the quarter.

Why it matters

Most OKRs are task lists in disguise. Teams ship everything on them and nothing moves.

What good looks like

Key results are outcomes, not things to ship
Few enough to focus on
Each team's goals visibly add up to the company's
Kept apart from performance ratings

Deliberately not measured

OKR software or formatting

Capability tested

Goal setting

The failure we’re looking for

A task list dressed up as key results

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

Key results are outcomes, not output

Focuses on the big rock

Shows how the goals add up

Plus our standard checks

The tasks

The outputs

Output

1. What Q3’s OKRs Actually Tell Us

2. How Q4 OKRs Add Up to the Company Goal

3. Proposed Q4 OKRs (Learner, Admin, Growth)

4. Guardrails for Q4 Execution

Grades and run details

Decision model checks

Run

Output

Grades and run details

Decision model checks

Run

Check by check

Where they differ 3

All got right 8

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured