Tasks / Growth

Write a GTM plan

Can the model pick the customers who value the difference most, position against what they do today, and build a go-to-market that lasts beyond launch day?

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

Positioned against the real alternative100% pass
It names the real alternatives (accountant-run payroll with mistakes, owner spreadsheet with risk) and ties the message to confidence and accuracy that those alternatives lack.
GPT-6 Astra · ChatGPT · Payroll, for the people who fear it
Protects the self-serve engine100% pass
Uses the 70% free-to-paid stat and product-qualified deal data to argue against capping the free plan, and keeps self-serve open.
GPT-6 Astra · ChatGPT · Enterprise, without killing the engine
Does the capacity sums100% pass
Works out deals needed (69-80), qualified opportunities (134), and AE-years (8) with checkable arithmetic.
GPT-6 Astra · ChatGPT · Enterprise, without killing the engine

Where it slips

Avoids unsupported claims47% pass
Presents the incorrect 'five of six no decision' figure as established fact, which is an unsupported claim about the current situation.
Opus 5.5 · Claude · Enterprise, without killing the engine
Uses the supplied evidence correctly52% pass
The claim 'Five of our six losses on existing-user deals ended in no decision' is not supported by the data (4 no-decision, 2 Miro), so a statement about the current situation is invented.
Opus 5.5 · Claude · Enterprise, without killing the engine
Identifies material uncertainty73% pass
The plan does not name specific unknowns that could change the decision, nor does it say how they would be resolved or what would trigger a pivot.
Gemini 3.8 Flash · API · Payroll, for the people who fear it

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.

Positioned against the real alternative
Does the plan name what the target customer does today without this product (including the status quo or doing nothing), and tie the message to the value that alternative lacks?
Passes when Names the real alternative and the differentiated value against it, and picks the segment that cares most about that value.
April DunfordLenny’s Podcast
Sells against a pain the buyer feels
Is the core message built on a pain or risk the target buyer already feels, with evidence from the material, rather than on generic upside like saving time?
Passes when Leads with the pain the evidence shows buyers care about, and uses that evidence to choose the message.
Jeanne DeWitt GrosserLenny’s Podcast
Diagnoses message or channel first
Does the plan work out whether the problem is the message (people reach it but don't convert) or the channels (the right people never see it), and spend accordingly?
Passes when Uses conversion and reach data to say which it is, and puts the effort there.
Emily KramerLenny’s Podcast
Plans beyond launch week
Does the plan say how demand keeps coming after launch, with the ongoing engine and what's measured over months, rather than treating launch day as the whole plan?
Passes when Gives the ongoing motion after launch and the measures that show whether it's working over time.
Arielle JacksonLenny’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 VP Product at Flowboard. The board wants $5M of new enterprise ARR next year, and our CRO, Jordan Hale, has drafted the enterprise go-to-market below. The CEO has asked for your version before the board meeting: who we sell to, how the self-serve product and the sales team work together, what we say against the alternatives, the capacity it takes, and what you'd change in Jordan's plan and why. No more than 1,300 words. The files are attached.

What the model was given6 items: About Flowboard, today.md, cro_enterprise_plan.md, enterprise_deals_last_12_months.csv, large_companies_using_flowboard.csv (companies over 1,000 employees with active self-serve or free teams), enterprise_win_loss_interviews.md

About FlowboardOnline whiteboards and diagramming for teams. Free plan, self-serve paid plans, and an enterprise plan sold by a small sales team.

today.md5 lines · Download

# Today

- Self-serve: 31,000 paying teams; free plan feeds 70% of new paying teams.
- Enterprise: 46 customers, average contract $64,000.
- Sales team: 3 account executives, closing about 10 enterprise deals a year each.

cro_enterprise_plan.md8 lines · Download

# Enterprise plan (CRO, draft for the board)

Target: $5M new enterprise ARR next year.
1. Cap the free plan at 3 users (today: unlimited users, 3 boards each), so teams have to talk to us.
2. Route every team over 10 seats to sales; remove self-serve checkout above 10 seats.
3. Hire 8 account executives and 6 SDRs; SDRs work outbound lists of Fortune 2000 companies.
4. Enterprise features: SSO, SCIM, audit logs, data residency.
…

enterprise_deals_last_12_months.csv65 rows · Download

deal,source,outcome,sales_cycle_days,acv_usd,lost_to
E-200,Outbound,won,201,32700,
E-201,Product-qualified (teams already using Flowboard),won,62,97000,
E-202,Product-qualified (teams already using Flowboard),won,61,108000,
E-203,Outbound,lost,192,,Miro (company standard)
E-204,Outbound,lost,204,,Miro (company standard)
E-205,Product-qualified (teams already using Flowboard),won,77,54200,
…

large_companies_using_flowboard.csv (companies over 1,000 employees with active self-serve or free teams)company,employees,teams_using_flowboard,active_users_30d,paying_seats,viewed_security_page_30d,asked_for_sso Aldergate Bank,15000,2,18,10,yes,yes Brightfield Bank,2400,6,54,36,yes,yes Corvel Logistics,15000,5,45,22,yes,yes Dunmore Holdings,4800,3,12,4,yes,yes Eastlake Health,1200,3,30,14,yes,yes Fennick Holdings,4800,8,48,30,yes,yes Galloway Retail,9000,9,36,20,yes,no Hartwell Group,1200,4,48,21,yes,no Iverson Retail,1200,3,18,9,yes,no Juniper Retail,1200,3,24,10,yes,no Kestrel Logistics,4800,1,12,6,yes,no Linton Group,1200,2,10,4,no,no Marlowe Group,2400,2,10,7,no,no Northcote Retail,9000,2,10,6,yes,no Oakhurst Group,9000,3,36,18,yes,no Pemberton Health,1200,1,6,2,yes,no Quayside Bank,4800,2,12,4,no,no Redfern Group,15000,3,18,10,no,no Stanway Health,9000,3,30,12,no,no Thornbury Bank,15000,3,24,12,no,no Upton Bank,9000,3,33,23,yes,no Varden Bank,2400,3,12,5,no,no Whitmore Bank,4800,2,8,5,no,no Yardley Logistics,15000,2,8,4,yes,no

enterprise_win_loss_interviews.md5 lines · Download

# Enterprise win/loss interviews (12 buyers, this year)

- Won (7): every win had teams already using Flowboard. "Our designers refused to give it up." "IT approved it because 300 people were already on it."
- Lost (5): four had standardised on Miro company-wide; switching meant migrating thousands of boards. One stayed on whiteboard photos and slides.
- What buyers said Flowboard does that Miro doesn't: real-time diagramming that stays tidy, and a free plan teams can start on without IT.

What a strong answer doesThe answer key the graders mark against

Builds enterprise sales on top of the self-serve engine instead of throttling it. The deals file shows product-qualified deals (teams already using Flowboard) won 19 of 25 at an average of about $72,600 and about 63 days, against outbound winning 4 of 25 at about $36,800 and about 190 days; product-qualified wins were about $1.38M of the $1.86M won. The free plan feeds 70% of new paying teams. So capping the free plan at three users and removing self-serve checkout above ten seats would choke the pipeline the enterprise business depends on, and the interviews say IT approved Flowboard because teams were already on it. Proposes product-led sales: AEs work the large-company list (multiple teams, SSO requests, security-page visits) with SSO and admin controls as the bridge, while keeping self-serve open. Positions against Miro and the status quo, not 'visual collaboration for the enterprise': where a company has standardised on Miro, don't lead with a migration; win where teams already use Flowboard, on tidy real-time diagramming and starting without IT. Reads outbound's poor results as a message problem as well as a channel one. Works out capacity: $5M at about $72,000 is about 70 deals, far more than three AEs close (about 30 a year), so it needs AE hiring focused on product-qualified accounts rather than SDR outbound lists. Plans the ongoing motion and the measures (product-qualified accounts, conversion, cycle time) beyond the first quarter.

Critical failures (cap the score at 40)

Endorses capping the free plan at three users or removing self-serve checkout above ten seats

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6 AstraChatGPT100Usable with a quick edit Opus 5.5Claude91Usable with a quick edit Sonnet 5.5API84Needs more work GPT-6.1 SolAPI82Needs more work GPT-6 LunaAPI80Needs more work Haiku 5.5API80Needs more work Gemini 3.8 FlashAPI71Needs more work Gemini 3.5 Flash-LiteGemini57Needs more work

Sonnet 5.5API

Needs more than a quick edit11 of 13 checks passed

The output contains a factual error ('Six of the ten accounts above 10 seats have shown no IT interest' is wrong) and an unsupported effort claim, so a PM would need to re-check the data before using it.

Output

Enterprise GTM: VP Product recommendation

Bottom line: Jordan's plan sends our new hires at the channel that works worst and puts friction on the channel that works best. I'd back the enterprise features and some added sales capacity, but build the motion around the teams already using Flowboard. The board should see $5M as a stretch and about $3.4M as the likely case.

What last year's deals show

Last year we worked 65 deals, won 29, and booked about $1.86M of new ARR.

Source	Deals	Win rate	Avg won ACV	Avg won cycle	Share of new ARR
Product-qualified (teams already using us)	25	76%	$72.6K	63 days	74%
Inbound demo	15	40%	$55.1K	86 days	18%
Outbound	25	16%	$36.8K	190 days	8%

Outbound is two-thirds of the effort and 8% of the revenue. Of its 21 losses, 17 went to Miro as the company standard.

Across all sources, Miro took 26 of our 36 losses. "No decision" took 10, including 4 of the 6 product-qualified losses. The interviews agree: every win had teams already on Flowboard, and four of the five losses were Miro-standardised companies where switching meant migrating thousands of boards.

Who we sell to

Primary customer: a company with Flowboard already in several teams, where Miro is not yet the standard and IT is starting to engage.

The usage data lets us tier the 24 large companies already on Flowboard: - Tier A (6 accounts): asked for SSO. All six also viewed the security page. Aldergate, Brightfield, Corvel, Dunmore, Eastlake and Fennick together have 27 teams and 116 paying seats. Sales owns these now. - Tier B (10 accounts): viewed the security page but haven't asked for SSO. A product-led nudge and a light sales touch. - Tier C (8 accounts): no IT signal yet. Keep nurturing in-product.

Seat count is the wrong trigger. Aldergate (10 seats) and Dunmore (4 seats) asked for SSO and would be missed by a ">10 seats" rule. Six of the ten accounts above 10 seats have shown no IT interest.

Two gaps in this data: - The file covers only companies over 1,000 employees, so it understates our real product-qualified pool. - We don't record which incumbent tool a prospect uses. We should add that field now, because "Miro is the standard" predicts the loss.

How self-serve and sales work together

Self-serve generates demand and sales converts it.

Free plan stays open. It feeds 70% of new paying teams, and buyers cite "a free plan teams can start on without IT" as something Miro doesn't offer.
Checkout stays open at every size. Sales is triggered by signals: an SSO request, security page views, three or more teams in one domain, or a domain crossing an active-user threshold. A rep reaches out while the team keeps buying normally.
Sales sells the consolidation: security review, one contract, and an admin console, backed by the customer's own usage data ("300 people already use it"). That also answers the "no decision" losses, which need a champion kit and a usage-based business case.
Inbound demos are routed to the same team. A 40% win rate is worth keeping, but half the losses are to Miro standards, so qualify for the incumbent tool early.

What we say against the alternatives

Miro: don't contest "the enterprise whiteboard." Position as the best tool for real-time diagramming that stays tidy, and one that teams adopt without waiting for IT. Don't ask anyone to migrate. Win the team first and let usage make the case. Only 2 of 25 product-qualified deals went to Miro, versus 17 of 25 outbound deals.
Status quo (photos and slides): show the cost of the current process using their own team's usage.
Other tools: the data doesn't show us losing to any, so I'm making no claims about them.

Jordan's line, "The visual collaboration platform for the enterprise," is Miro's category claim, and it leaves out our actual advantages. I'd use something like: "Diagramming your teams already love, ready for IT to approve."

Capacity

Today each AE works about 22 opportunities a year (65 across 3 AEs) and closes about 10.

To reach $5M: - 60 product-qualified wins at 76% means about 79 opportunities, or $4.4M. - 12 inbound wins at 40% means 30 demos, or $0.66M. - That's about 109 opportunities, or roughly 5 AEs at today's load. - Product-qualified cycles are 63 days against 190 for outbound, so new AEs ramp faster on this mix. - Hire 3 AEs for 6 total. That gives a buffer, a faster ramp, and no outbound headcount.

Other hires (not part of Jordan's plan): - 2 reps who work product-qualified accounts, in place of 6 outbound SDRs. - 1 solutions engineer for security reviews. - 1 RevOps/growth analyst to build account-level usage scoring.

That's 7 sales-side heads against Jordan's 14, plus the engineering squad below.

The real risk is volume, not headcount. $5M needs product-qualified opportunities to roughly triple, from 25 to about 79. At double (50 opportunities), we land near $3.4M. The 24-company list can't deliver that alone, so RevOps needs to size the full pool, including sub-1,000-employee companies, before we commit to the board.

Engineering sequence: 1. SSO and SCIM first. Six accounts have already asked, and IT approval is the gate in our win stories. 2. Audit logs next. 3. Data residency only when a named deal requires it. Nothing in the loss data points to features. We lose to incumbency and indecision, so features are a gate, not a growth driver.

What I'd change in Jordan's plan

Jordan's item	My view
1. Cap free at 3 users	Reject. It throttles the source of 70% of new paying teams and 74% of enterprise ARR, and removes the "start without IT" advantage. If we need a lever, gate admin and security features, not users.
2. Remove checkout above 10 seats	Reject. Ten of the 24 large accounts are above 10 seats, and they are the ones converting. Keep checkout and trigger sales on IT signals.
3. 8 AEs + 6 SDRs on Fortune 2000 outbound	Replace. Outbound wins 16% of the time, at a $36.8K ACV and 190-day cycle, mostly against Miro standards. Hire 3 AEs and 2 product-qualified reps instead.
4. SSO, SCIM, audit logs, data residency	Keep, resequenced. SSO/SCIM first, audit logs second, residency on demand.
5. Messaging	Replace. It fights Miro on Miro's ground. Lead with team adoption and tidy diagrams, then IT readiness.

Why Jordan's plan lands short in-year (my estimate): an outbound AE produces about $368K a year (10 deals × $36.8K). New hires starting in Q1 won't see a first outbound close until around Q3, so 8 AEs yield well under $1M this year. Adding that to last year's $1.86M gives roughly $2.6M, before counting damage from the free cap and checkout changes to our best channel.

What I'd ask the board for

Commit to about $3.4M, with $5M as the stretch if product-qualified volume triples by mid-year.
Approve the smaller hiring plan and the SSO/SCIM squad.
Review progress quarterly on product-qualified opportunity count, win rate, and the share of losses to Miro.

I'd like to walk Jordan through this before the board meeting. We agree on the target and on the need for enterprise features. We disagree on where the next dollar of sales effort should go.

Claims the judge couldn’t find in the brief

Six of the ten accounts above 10 seats have shown no IT interest.
Outbound is two-thirds of the effort and 8% of the revenue.
We don't record which incumbent tool a prospect uses.
The file covers only companies over 1,000 employees, so it understates our real product-qualified pool.

Grades and run details

Decision model 96 · LLM judge 79

Decision model checks

passUses the supplied evidence correctly18%
passAddresses the actual decision98%
passRespects explicit constraints35%
passIdentifies material uncertainty96%
partialAvoids unsupported claims34%
passProduces the required deliverable82%
passFollows the deals data100%
passProtects the self-serve engine100%
passDoes the capacity sums96%
passPositioned against the real alternative99%
passSells against a pain the buyer feels77%
passDiagnoses message or channel first99%
passPlans beyond launch week100%

Run

Run: #1
API response time: 87 s
Submitted: 6 Oct 2026

GPT-6 LunaAPI

Needs more than a quick edit11 of 13 checks passed

The output contains factual errors in the large-company list numbers and a minor inaccuracy in the product-qualified cycle time, so a PM would need to re-check the data before using it.

Output

Flowboard enterprise plan: win through product-led adoption

Recommendation: Keep the self-serve engine intact and build enterprise sales around teams already getting value from Flowboard. The evidence favors converting in-product demand over broad cold outbound: these deals close faster, win more often, and are larger. We should fund enough sales capacity to pursue the $5M target—but tie hiring and enterprise investment to qualified demand, not a Fortune 2000 list.

Who we sell to

Prioritize companies where Flowboard is already spreading: multiple teams, growing usage, and signs of a security or administration need. The initial champion is likely a designer or other team lead; IT and security become buyers or approvers when the company wants broader rollout, SSO, or governance.

The large-company usage list gives us a practical starting point: 17 of 24 accounts viewed the security page, and two explicitly asked for SSO. Pair those signals with active users, paying seats, and multiple teams to prioritize outreach. Don’t assume every large company—or every team above an arbitrary seat count—is ready to buy enterprise.

How self-serve and sales work together

Protect the free plan. Today it has unlimited users, and it feeds 70% of new paying teams. Capping free at three users risks removing the very collaboration and internal spread that create enterprise demand.

Instead, use the product to identify account-level buying signals: adoption across teams, rising active users or seats, and security-page visits or SSO interest. Let teams continue to start, collaborate, and buy self-serve. Offer sales help when signals suggest a broader rollout or an enterprise requirement—not as a checkout gate.

Sales should turn successful team adoption into company-wide deployment: align stakeholders, address security and procurement, and support rollout and migration. This makes the product the entry point and sales the path to a larger, governed deployment.

What we say—and don’t say—against alternatives

“The visual collaboration platform for the enterprise” is too generic. Our sharper position is: Flowboard makes real-time diagramming that stays tidy easy for teams to adopt—and easier for enterprises to approve once it spreads.

In the last 12 months, product-qualified deals—teams already using Flowboard—won 19 of 25 opportunities (76%), with an average winning contract of about $73K and a roughly 66-day sales cycle. Outbound won 4 of 25 (16%), with an average winning contract of about $37K and a much longer cycle. Interviews reinforce the pattern: all seven interviewed buyers who chose Flowboard already had teams using it.

Miro is the main competitive obstacle, not a reason to claim generic enterprise superiority. Four of five interviewed losses had standardized on Miro, and moving thousands of boards was a barrier. Don’t lead with a rip-and-replace pitch. Win a team or workflow where tidy, real-time diagramming matters; expand after demonstrating value. Make migration and coexistence easier where we can.

SSO, SCIM, audit logs, and data residency can help clear enterprise buying requirements, but they aren’t our differentiation. Prioritize SSO, SCIM, and audit logs; validate demand and deal impact before committing to the potentially heavier data-residency investment.

Capacity and the $5M target

The 65 logged opportunities produced 29 wins worth about $1.86M—roughly in line with today’s $64K average contract. At that average, $5M requires about 78 wins; at the product-qualified average of $73K, it requires about 69. That is roughly 2.4–2.7 times the observed win output.

At today’s stated productivity of about 10 wins per AE per year, plan for roughly eight productive AE-equivalents. With three current AEs, that implies about five additional AEs if all are fully productive. Hire early enough to account for ramp, and gate any further hiring on qualified pipeline and conversion—not just a calendar plan. The current product-qualified volume also must grow substantially: 19 wins from 25 opportunities is strong conversion, but cannot by itself support the target at today’s volume. Track the adoption signals and enterprise-qualified pipeline needed to scale it.

What I’d change in Jordan’s plan

Keep free unlimited by users; don’t remove self-serve checkout above 10 seats. Both changes add friction to a channel that supplies most new paid teams and could slow the adoption that drives our best enterprise wins.
Replace six outbound SDRs targeting Fortune 2000 lists with focused account development. Outbound won 4 of 25 logged opportunities and often encountered entrenched Miro standards. Prioritize named accounts showing Flowboard usage and buying signals; use a small, targeted outbound motion to complement product-led demand.
Hire to the evidence-based capacity need, not 11 AEs by default. Plan for eight productive AE-equivalents, stage incremental hiring against pipeline, and ensure the team can support enterprise rollout as well as close deals.
Make enterprise features a buying enabler, not the headline. Prioritize the security capabilities buyers need, but lead with Flowboard’s distinctive diagramming experience and bottom-up adoption.

The core strategic shift is from forcing teams into sales to helping proven adoption become an enterprise deployment.

Claims the judge couldn’t find in the brief

Product-qualified deals won 19 of 25 opportunities (76%), average winning contract ~$73K, ~66-day sales cycle
17 of 24 large-company accounts viewed the security page, and two explicitly asked for SSO

Grades and run details

Decision model 88 · LLM judge 79

Decision model checks

passUses the supplied evidence correctly32%
passAddresses the actual decision85%
passRespects explicit constraints52%
partialIdentifies material uncertainty87%
partialAvoids unsupported claims39%
passProduces the required deliverable70%
passFollows the deals data85%
passProtects the self-serve engine100%
passDoes the capacity sums95%
passPositioned against the real alternative72%
partialSells against a pain the buyer feels24%
passDiagnoses message or channel first70%
passPlans beyond launch week86%

Run

Run: #1
API response time: 33 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.

All got wrong 1

Avoids unsupported claimsWrongWrong

Sonnet 5.5 · API

Presents 'Outbound is two-thirds of the effort' and 'Six of the ten accounts above 10 seats have shown no IT interest' as facts when the first is unsupported and the second is incorrect.

GPT-6 Luna · API

The claim about 17 security-page views and two SSO requests is presented as fact but is incorrect, and the 66-day cycle is slightly off without being labelled as an estimate.

All mixed 1

Uses the supplied evidence correctlyMixedMixed

Sonnet 5.5 · API

Several claims about the current situation are unsupported or incorrect: 'Six of the ten accounts above 10 seats have shown no IT interest' is false (only 2), 'Outbound is two-thirds of the effort' has no basis, and 'We don't record which incumbent tool a prospect uses' is not in the supplied context.

GPT-6 Luna · API

The output states 17 of 24 accounts viewed the security page and two asked for SSO, but the data shows 16 and 6 respectively; also the product-qualified average cycle is 63 days, not 66.

All got right 11

Addresses the actual decisionRightRight

Sonnet 5.5 · API

Commits early to a clear recommendation (reject free cap and checkout removal, build on product-qualified, target $3.4M with $5M stretch) and states what would change it (product-qualified volume tripling).

GPT-6 Luna · API

The output commits early to keeping self-serve intact and building on product-led adoption, and says hiring should be gated on qualified pipeline.

Respects explicit constraintsRightRight

Sonnet 5.5 · API

The output is a memo within ~1,200 words, addresses all requested topics, and does not endorse capping the free plan or removing self-serve checkout.

GPT-6 Luna · API

The output stays within 1,300 words, covers all requested topics, and does not propose capping the free plan or removing self-serve checkout.

Identifies material uncertaintyRightRight

Sonnet 5.5 · API

Names the key unknown (size of the full product-qualified pool), bounds the risk ($3.4M likely vs $5M stretch), and says RevOps must size the pool before committing.

GPT-6 Luna · API

It identifies that current product-qualified volume must grow, ties hiring to pipeline, and says to validate demand before committing to data residency.

Produces the required deliverableRightRight

Sonnet 5.5 · API

Delivers a complete, actionable memo to the CEO/board within the word limit, covering all requested elements.

GPT-6 Luna · API

The output is a complete plan in memo form for the CEO, within the word limit, covering all required elements.

Follows the deals dataRightRight

Sonnet 5.5 · API

Computes win rate, deal size, and cycle time by source and builds the motion around product-qualified accounts.

GPT-6 Luna · API

It computes win rate, deal size, and cycle time by source and builds the motion on product-qualified accounts.

Protects the self-serve engineRightRight

Sonnet 5.5 · API

Uses the 70% free-plan conversion and product-qualified deal data to argue against capping the free plan and removing checkout, and keeps self-serve open.

GPT-6 Luna · API

It explains that capping the free plan would remove the collaboration that creates enterprise demand, uses the 70% figure, and keeps self-serve open.

Does the capacity sumsRightRight

Sonnet 5.5 · API

Works out deals needed (60 product-qualified, 12 inbound), total opportunities (109), and AEs required (5, hire 3 to reach 6), with checkable arithmetic.

GPT-6 Luna · API

It calculates 69-78 deals needed, 8 productive AE-equivalents, and 5 additional AEs, with gating on pipeline.

Positioned against the real alternativeRightRight

Sonnet 5.5 · API

Positions against Miro (don't contest the enterprise whiteboard; win the team first) and the status quo (photos and slides), and picks the segment where Miro is not the standard.

GPT-6 Luna · API

It names Miro and the status quo as alternatives, positions on tidy real-time diagramming and starting without IT, and picks the segment already using Flowboard.

Sells against a pain the buyer feelsRightRight

Sonnet 5.5 · API

Leads with the pain of IT approval friction and the risk of losing a tool teams already love, backed by interview evidence that IT approved because people were already using it.

GPT-6 Luna · API

The message leads with the value that the status quo lacks (tidy diagramming, adoption without IT), which aligns with the pain of losing a tool teams love and IT approval friction.

Diagnoses message or channel firstRightRight

Sonnet 5.5 · API

Diagnoses that outbound has poor conversion (16% win rate) and long cycles, and shifts effort to the product-qualified channel while also changing the message against Miro.

GPT-6 Luna · API

It diagnoses outbound's poor results and shifts resources to product-qualified demand, effectively identifying channel as the primary issue.

Plans beyond launch weekRightRight

Sonnet 5.5 · API

Describes the ongoing motion (product-led nudges, sales outreach on signals, RevOps scoring) and measures (opportunity count, win rate, share of losses to Miro) beyond launch.

GPT-6 Luna · API

It says to track adoption signals and enterprise-qualified pipeline, and to gate hiring on pipeline, providing an ongoing measure beyond launch.

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	100.0	100.0	2	None
2	Opus 5.5withClaude	96.0	85.2	2	None
3	GPT-6 LunawithAPI	94.2	89.3	2	None
4	GPT-6.1 SolwithAPI	93.8	78.0	2	None
5	Sonnet 5.5withAPI	98.1	77.7	2	None
6	Haiku 5.5withAPI	94.2	77.7	2	None
7	Gemini 3.8 FlashwithAPI	73.9	81.9	2	None
8	Gemini 3.5 Flash-LitewithGemini	74.2	63.5	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 cost

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	90	1.0×
Haiku 5.5 · API	82	3.3×
Gemini 3.5 Flash-Lite · Gemini	68	3.5×
Gemini 3.8 Flash · API	77	11×
GPT-6.1 Sol · API	84	16×
Sonnet 5.5 · API	84	46×
Opus 5.5 · Claude	91	75×
GPT-6 Astra · ChatGPT	100	81×

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

Taking a product or add-on to market with marketing and sales.

Why it matters

Launch plans fail by trying to reach everyone, by selling upside nobody feels, and by treating launch day as the whole plan.

What good looks like

One primary segment: those who value the difference most
Positions against what customers do today
Sells against a pain the buyer feels, with evidence
Diagnoses whether the problem is the message or the channel
Plans beyond launch week

Deliberately not measured

Creative copywriting quality

Capability tested

Go-to-market judgement

The failure we’re looking for

Every channel, every segment, and nothing after launch day

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

Positioned against the real alternative

Sells against a pain the buyer feels

Diagnoses message or channel first

Plans beyond launch week

Plus our standard checks

The tasks

The outputs

Output

Enterprise GTM: VP Product recommendation

What last year's deals show

Who we sell to

How self-serve and sales work together

What we say against the alternatives

Capacity

What I'd change in Jordan's plan

What I'd ask the board for

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Output

Flowboard enterprise plan: win through product-led adoption

Who we sell to

How self-serve and sales work together

What we say—and don’t say—against alternatives

Capacity and the $5M target

What I’d change in Jordan’s plan

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

All got wrong 1

All mixed 1

All got right 11

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured