Tasks / Product Craft

Extract discovery insights

Can the model separate evidence, themes and hypotheses without inventing consensus?

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

  1. Frames findings for the decision100% pass
    Each main finding is framed as an opportunity (delivery-to-invoice pain, concentrated supplier need) and linked directly to what to build next quarter.
    GPT-6 Astra · ChatGPT · The loudest ticket
  2. Corrects the draft's claim100% pass
    It corrects the draft's headline by counting interviews (8 describe delivery/invoice pain, 2 mention supplier expansion) and showing the marketplace claim is unsupported.
    GPT-6 Astra · ChatGPT · The loudest ticket
  3. Keeps dissent visible100% pass

Where it slips

  1. Says how many sources support each finding52% pass
    The finding on smaller issues sizes Ordering as 31 tickets, but the actual count is 30, so the finding is not accurately sized.
    Sonnet 5.5 · API · The loudest ticket
  2. Uses the supplied evidence correctly59% pass
    The output states Ordering has 31 tickets, but the CSV contains 30 Ordering tickets; this factual claim is not supported by the supplied data.
    GPT-6.1 Sol · API · The loudest ticket
  3. Avoids unsupported claims63% pass
    The claim 'Ordering (31 tickets)' is presented as an established fact, but the evidence shows 30 Ordering tickets, so it is an unsupported confident claim.
    Sonnet 5.5 · API · The loudest ticket

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Weights behaviour over opinion

    Does the synthesis distinguish evidence of what customers actually did (specific past instances, observed behaviour, workarounds) from stated preferences, predictions and feature requests, and weight them accordingly?

    Passes when Themes are labelled or weighted by evidence type, and at least one theme resting mainly on stated intent or requests is flagged as weaker.

  2. Frames findings for the decision

    Does the synthesis turn its findings into opportunities tied to the decision or outcome the team faces, saying what each one means for that decision?

    Passes when Each main finding is framed as an opportunity (a need or pain to address) and linked to the decision at hand, with what it implies.

  3. Reports what cuts against the plan

    Does the synthesis report the evidence that cuts against the team's current belief or plan, rather than only what supports it?

    Passes when Names at least one finding that challenges the team's hypothesis or plan, and says what it means for it.

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 · Says how many sources support each finding · and 2 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the PM for Larder's ordering product. A colleague has drafted the synthesis below for our CPO, recommending a supplier marketplace. Before it goes, rewrite the synthesis from the evidence, in no more than 800 words: the main findings, how strong the evidence for each is, and what they mean for what we build next quarter. The interviews and the ticket export are attached.

What the model was given4 items: About Larder, draft_synthesis.md, interview_excerpts.md, support_tickets_last_quarter.csv
About LarderOrdering software for restaurants: order from your suppliers in one place, track deliveries and invoices. 1,140 accounts, most of them independent restaurants.
draft_synthesis.md6 lines · Download
# Draft synthesis (PM, for the CPO)

Headline: customers want a supplier marketplace.
- "Add more suppliers" is our top support category by far.
- Most customers we spoke to want a marketplace (see Calder Group and the pizzeria).
- Recommendation: build a supplier marketplace next quarter.
interview_excerpts.md12 lines · Download
# Interview excerpts (restaurant owners and managers, September)

1. Bistro, 2 sites: "Last Tuesday the fish order came short again. I only found out when the invoice came, a week later. I spent Sunday night matching delivery notes to invoices with a highlighter."
2. Café group, 5 sites: "We switched produce supplier twice this year, but finding suppliers isn't the problem. Knowing what actually arrived is."
3. Pizzeria, 1 site: "Honestly I'd love more suppliers in the app. More choice, better prices."
4. Gastropub: "My head chef signs the delivery note without checking. Then I pay for things we never got. Last month that was about £600."
5. Hotel restaurant: "Our finance team chases credit notes for short deliveries every week. It takes days."
…
support_tickets_last_quarter.csv200 rows · Download
ticket,account,account_sites,category,summary
LT-5000,Calder Group,14,Add more suppliers,Can you onboard our meat supplier
LT-5001,Account 1886,3,Ordering,Change delivery day
LT-5002,Account 2004,1,Delivery didn't match order,Short delivery not shown in app
LT-5003,Calder Group,14,Add more suppliers,Add dairy supplier please
LT-5004,Calder Group,14,Add more suppliers,Add dairy supplier please
LT-5005,Account 1858,2,Add more suppliers,Any plans for more suppliers?
…
What a strong answer doesThe answer key the graders mark against

Shows that the marketplace headline doesn't hold. 'Add more suppliers' is the biggest ticket category (75 of 200), but 58 of those come from one account, Calder Group; across other accounts, delivery and invoice problems ('delivery didn't match order' and 'invoice reconciliation', 63 tickets) are far more common than supplier requests (17). In the interviews, 7 of 10 describe a specific recent instance of the same pain (short deliveries, invoices that don't match, credit notes, re-typing paper checks), while the marketplace mentions are wishes (the pizzeria 'would love' it, the tapas bar 'I suppose') or come from Calder. Weights what people did over what they say they'd want, counts how many sources support each finding, and recommends focusing next quarter on checking deliveries and reconciling invoices, with the marketplace treated as one large customer's request to handle separately.

Critical failures (cap the score at 40)
  • Recommends building the supplier marketplace next quarter as the main finding
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit11 of 12 checks passed

The only flaw is an off-by-one error in two ticket counts (31 vs 30), which can be fixed in a quick edit without re-checking the core reasoning or evidence.

Output

Synthesis: what customers are telling us

Headline: Our biggest problem is that customers can't easily check what arrived against what they were invoiced. A supplier marketplace is a real but narrow request, concentrated in one large account. I don't recommend building it next quarter.

Sources: 10 owner and manager interviews (September) and 200 support tickets from last quarter (LT-5000 to LT-5199).

What changed from the draft: "Top category by far" holds only because of one account. "Most customers we spoke to want a marketplace" is 2 of 10, and one of those is hedged. The recommendation doesn't follow from the evidence.

Findings

1. Checking deliveries and reconciling invoices is the main pain. Evidence: strong. - 63 of 200 tickets (32%) are "delivery didn't match order" or "invoice reconciliation." The most common issues are short deliveries not shown in the app, missing credit notes, invoices including items not delivered, invoices that can't be matched to orders, and accountant exports that omit credits. - These tickets come from many different accounts, not one. - 8 of 10 interviews describe this problem. A gastropub lost about £600 in a month paying for unchecked deliveries. A hotel finance team chases credit notes weekly. A bakery is in a "my word against theirs" dispute with a supplier. A noodle bar re-types clipboard checks into the app. - Caveats: ticket categories are assigned by support staff, and 10 interviews is a small sample.

2. Supplier demand is concentrated in one account. Evidence: strong for Calder Group, weak as a base-wide signal. - "Add more suppliers" is the largest ticket category (75 tickets), but 58 come from Calder Group (14 sites). Outside Calder it is 17 tickets, fewer than delivery issues (34) or ordering issues (31). - Calder's requests repeat four named suppliers: Brakes (15 tickets), meat (15), dairy (14), and a wine merchant (14). These are four specific onboarding requests, not a general need for a marketplace. - Outside Calder, requests are vague ("any plans for more suppliers?", "would like more produce suppliers"), spread across accounts. - In interviews, 2 of 10 want more suppliers: the pizzeria and Calder. A tapas bar says it would use a marketplace "I suppose," but says the paperwork is what hurts day to day. A café group says finding suppliers "isn't the problem. Knowing what actually arrived is."

3. Calder Group is a high-value but conditional signal. Evidence: one account, one conversation. - Calder says: "If you had a marketplace, we'd move all our ordering to you." That is the strongest commercial statement in the material, but it is one customer's conditional view, and it depends on four specific suppliers. - We don't know from these materials how much ordering volume or revenue Calder represents.

4. Ordering and login tickets are routine. Evidence: moderate, low priority. - Ordering (31) and login (31) tickets are mostly specific fixes: cut-off times, duplicate orders, delivery-day changes, tablet login problems, password resets, adding users. None of these came up in interviews.

What this means for next quarter

Build: a receiving and invoice-matching workflow. This addresses the largest and most widely shared problem. - Let staff record received quantities at delivery, with shorts and substitutions flagged in the app. This covers the short-delivery tickets and "how do I record a substitution?" - Automatically match invoices to deliveries and orders, and flag differences before payment. This covers "can't match," "total differs," and "includes items not delivered." - Show credit notes against deliveries and in the accountant export. Fixing the export that omits credits could ship early as a quick win. - Measures: the 63-ticket category volume, and credit value recovered per account, which we can ask customers to report.

Don't build the marketplace next quarter. Instead: - Talk to Calder Group about their ordering volume and what they'd need to move everything to us. Check whether onboarding Brakes, their meat supplier, dairy, and the wine merchant is feasible as partnership or integration work. That is a much smaller decision than building a platform. - Add a "request a supplier" flow so demand from the wider base is captured in a structured way. Then we can see whether requests extend beyond Calder. - Triage the ordering and login tickets into the backlog as small fixes.

What would change my view

  • A clear rise in supplier requests from accounts other than Calder.
  • Around 20 more interviews, across account sizes and including some churned customers, where supplier access is named as the blocker.
  • Evidence that Calder's ordering value justifies the integrations, or that a marketplace would win or retain several accounts of Calder's scale.

Until then, the evidence supports fixing receiving and reconciliation first, and treating the supplier question as a targeted account decision rather than a platform bet.

Claims the judge couldn’t find in the brief

  • Outside Calder, supplier requests are 17 tickets, fewer than delivery issues (34) or ordering issues (31).
  • Ordering (31) and login (31) tickets are routine fixes; none appeared in interviews.

Grades and run details

Decision model 100 · LLM judge 92
Decision model checks
  • passUses the supplied evidence correctly51%
  • passAddresses the actual decision100%
  • passRespects explicit constraints64%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims69%
  • passProduces the required deliverable98%
  • passSees one account behind the top ticket100%
  • passCorrects the draft's claim100%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding82%
  • passFrames findings for the decision97%
  • passReports what cuts against the plan100%
Run
Run
#1
API response time
34 s
Submitted
8 Oct 2026

Usable with a quick edit11 of 12 checks passed

The output is usable with a quick edit to correct the Ordering ticket count from 31 to 30; the reasoning and recommendation remain sound and do not need redoing.

Output

Synthesis for the CPO: prioritise delivery-to-invoice accuracy, not a marketplace

Recommendation: Next quarter, prioritise a lightweight workflow for recording what arrived, resolving discrepancies and tracking credits. Continue targeted supplier onboarding, but do not commit to a supplier marketplace on this evidence.

Main findings and evidence strength

1. Delivery discrepancies and reconciliation are the clearest recurring customer problem

Eight of ten interviewees describe problems spanning delivery checking, missing items, substitutions, invoice matching, disputes or paperwork. These problems have concrete consequences: the gastropub reports paying roughly £600 for undelivered goods last month; the bistro spends Sunday evening reconciling documents; the hotel’s finance team chases credits weekly. These are self-reported examples, not verified savings estimates.

The ticket export independently supports this pattern:

Support categoryTicketsDistinct accounts within category
------:---:
Add more suppliers7518
Delivery didn’t match order3434
Invoice reconciliation2928
Login and access3131
Ordering3129

Delivery and reconciliation together account for 63 tickets from 62 distinct accounts. Treating them as one related workflow reveals a broader account-level signal than supplier requests.

Evidence strength: Strong evidence of a recurring problem among the customers represented, supported by both interviews and tickets. Moderate evidence for prioritising it across the customer base: ten interviews and support contacts are not representative measures of prevalence among all 1,140 accounts. We also cannot yet tell which part of the workflow offers the greatest benefit.

2. Supplier coverage matters, but ticket volume overstates the breadth of demand

“Add more suppliers” is the largest individual category, at 75 of 200 tickets. However, 58 of those 75 tickets—77%—come from Calder Group, one 14-site account, repeatedly requesting four named suppliers. The remaining 17 tickets come from 17 accounts.

That concentration matters. Repeated requests demonstrate an unresolved, potentially important need for Calder; they do not represent 58 independent customers. Calder is also larger than Larder’s typical independent restaurant account. Its commercial importance should be assessed separately rather than inferred from ticket volume.

Evidence strength: Strong evidence that Calder needs broader coverage, and credible evidence of interest from other accounts. Weak evidence that supplier expansion is the most widespread customer need.

3. Supplier coverage is not the same as a marketplace

The pizzeria explicitly wants more choice and better prices. Calder explicitly requests a marketplace, but its stated goal—ordering from every supplier in one place—could also be addressed by onboarding its existing suppliers. The tapas bar expresses only qualified interest and prioritises paperwork; the café group says finding suppliers is not the problem.

Thus, two interviewees express clear enthusiasm, not “most customers.” Most supplier tickets ask for named existing suppliers or “more suppliers”; they do not establish demand for discovery, comparison or transacting with new suppliers.

Evidence strength: Limited evidence for a marketplace specifically. The evidence does not establish likely adoption, willingness to pay or the supply-side requirements for making it useful.

What we should build next quarter

Make delivery-to-invoice accuracy the main product investment. Begin with a focused pilot covering:

  • Recording actual quantities received, shortages and substitutions at delivery, without retyping clipboard records.
  • Preserving a discrepancy record and supporting evidence that can be shared with suppliers.
  • Showing differences between ordered, received and invoiced items, with outstanding credits tracked through resolution.

Validate the workflow with independent and small multi-site restaurants before scaling. Prioritise the smallest useful end-to-end slice rather than assuming full reconciliation automation is necessary.

Measure delivery-recording adoption, time spent reconciling, unresolved discrepancy age and credit-resolution time against pilot baselines. Validate reported financial benefits rather than extrapolating the £600 anecdote.

Keep supplier coverage as a bounded parallel effort. Assess onboarding Calder’s four requested suppliers and the associated retention or expansion opportunity. Separately test whether other restaurants primarily need existing-supplier coverage or genuinely want to discover and switch suppliers.

Reconsider a marketplace only after broader, account-level research and behavioural tests demonstrate demand beyond Calder. Continue addressing ordering and access issues, each responsible for 31 tickets; this recommendation is a prioritisation decision, not evidence that those basics are solved.

Claims the judge couldn’t find in the brief

  • Ordering category has 31 tickets from 29 distinct accounts.

Grades and run details

Decision model 100 · LLM judge 92
Decision model checks
  • passUses the supplied evidence correctly48%
  • passAddresses the actual decision98%
  • passRespects explicit constraints67%
  • passIdentifies material uncertainty93%
  • passAvoids unsupported claims74%
  • passProduces the required deliverable92%
  • passSees one account behind the top ticket99%
  • passCorrects the draft's claim91%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding68%
  • passFrames findings for the decision98%
  • passReports what cuts against the plan100%
Run
Run
#1
API response time
49 s
Submitted
6 Oct 2026

Needs more than a quick edit8 of 12 checks passed

The output contains incorrect ticket and interview counts that would require re-checking the source data. A PM cannot use it without verifying and correcting those numbers.

Output

Product Synthesis: Customer Research & Support Ticket Analysis

To: Chief Product Officer From: Product Manager, Ordering Date: October 2023 Subject: Evidence-based review of next quarter’s roadmap (revising the supplier marketplace proposal)

---

Executive Summary

Our draft synthesis concluded that customers want a supplier marketplace and recommended building one next quarter. A rigorous re-examination of our customer interviews and last quarter's support tickets reveals this was a misreading of the data.

While "Add more suppliers" appears frequently in our ticket logs, over 45% of those tickets originate from a single multi-site account (Calder Group). Across our broader base of 1,140 mostly independent accounts, the core operational pain points are invoice reconciliation, short deliveries, and tracking actuals against orders.

Building a marketplace addresses a growth/acquisition lever heavily championed by enterprise accounts, but it does not solve the acute, daily operational friction draining time and money for our core user base. We should refocus next quarter’s build on delivery discrepancy tracking and invoice matching.

---

Main Findings & Evidence Strength

#### Finding 1: The "Supplier Marketplace" demand is heavily skewed by a single enterprise account. * The Claim: "Add more suppliers" is our top support category by far, proving a marketplace is our most urgent need. * The Evidence: A raw count of last quarter’s tickets (LT-5000 to LT-5199) shows roughly 45 tickets categorized under "Add more suppliers." However, a closer look at the account data reveals that 22 of those tickets come from Calder Group (a 14-site group). Furthermore, in customer interviews, while one pizzeria and Calder Group explicitly asked for a marketplace, other multi-site and independent operators stated otherwise: "Finding suppliers isn't the problem. Knowing what actually arrived is" (Café group, 5 sites); "What kills me is the paperwork" (Tapas bar). * Evidence Strength: Moderate to Weak. The support volume for suppliers is inflated by one outlier account. Independent operators view suppliers as readily available; their bottleneck is post-order administration.

#### Finding 2: Invoice reconciliation and short deliveries are massive, recurring operational drains. * The Claim: Customers spend significant uncompensated time matching invoices, handling missing credit notes, and absorbing losses on unfulfilled goods. * The Evidence: * Interviews: 7 out of 10 interviewed operators independently volunteered painful anecdotes about discrepancies: fish orders arriving short (Bistro), signing delivery notes blindly and losing £600/month on ghost items (Gastropub), spending days chasing credit notes (Hotel), and abandoning dispute processes because "I just pay it, I don’t have time" (Steakhouse). * Support Tickets: Across the remaining tickets not tied to Calder Group's supplier requests, the volume is heavily dominated by "Invoice reconciliation" (mismatched totals, export issues) and "Delivery didn't match order" (short deliveries, missing credit notes, unrecorded substitutions). * Evidence Strength: Strong. Universally present across interviews and forming the bulk of legitimate, distributed support traffic from independent accounts.

#### Finding 3: Double-handling workflows create friction between physical delivery and digital tracking. * The Claim Users struggle to reconcile what happened on the loading dock with what is inside the app. * The Evidence: Operators report checking physical deliveries on clipboards or highlighters, then manually transcribing them into the app ("Twice the work" – Noodle bar) or failing to surface short deliveries because the app doesn't natively map discrepancies in real-time. * Evidence Strength: Strong. Directly observed in multiple site interviews.

---

What This Means for Next Quarter (Product Recommendations)

If we build a supplier marketplace next quarter, we are effectively prioritizing the expansion requests of our largest enterprise customer (Calder Group) while ignoring the acute daily churn and time sinks plaguing our broader base of 1,140 independent accounts.

To deliver high impact next quarter, we should pivot our roadmap away from marketplace infrastructure and toward Post-Order Control & Reconciliation:

  1. Native Discrepancy & Substitution Management: Build intuitive, in-app workflows for chefs and managers to flag short-weights, damaged goods, and substitutions at the time of delivery (solving the clipboard-to-app transcription problem).
  2. Streamlined Credit Note & Invoice Matching: Automate the comparison between the original order, the delivery note, and the supplier invoice. Give finance teams the ability to flag discrepancies and request credit notes directly within Larder, eliminating the "highlighter and Sunday night paperwork" routine.
  3. Targeted Supplier Onboarding (Selective, not Marketplace): Rather than building an open supplier marketplace, use API/onboarding bandwidth to manually satisfy enterprise expansion blockers (like Calder Group's meat and wine suppliers) on a case-by-case basis, protecting engineering resources for core workflow improvements.

Claims the judge couldn’t find in the brief

  • A raw count of last quarter's tickets shows roughly 45 tickets categorized under 'Add more suppliers'
  • 22 of those tickets come from Calder Group
  • 7 out of 10 interviewed operators independently volunteered painful anecdotes about discrepancies
  • The app doesn't natively map discrepancies in real-time

Grades and run details

Decision model 75 · LLM judge 62
Decision model checks
  • failUses the supplied evidence correctly65%
  • passAddresses the actual decision32%
  • passRespects explicit constraints37%
  • failIdentifies material uncertainty60%
  • partialAvoids unsupported claims30%
  • passProduces the required deliverable75%
  • passSees one account behind the top ticket53%
  • passCorrects the draft's claim32%
  • passWeights behaviour over opinion98%
  • partialSays how many sources support each finding53%
  • passFrames findings for the decision92%
  • passReports what cuts against the plan100%
Run
Run
#1
Time to output
6 s
Submitted
5 Oct 2026

Check by check

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

Where they differ 4

Uses the supplied evidence correctlyMixedMixedWrong
Haiku 5.5 · API

The ticket counts for ordering and login are both stated as 31, but the CSV contains 30 of each.

GPT-6.1 Sol · API

The output states Ordering has 31 tickets, but the CSV contains 30 Ordering tickets; this factual claim is not supported by the supplied data.

Gemini 3.5 Flash-Lite · Gemini

Output states incorrect ticket counts (45 total, 22 from Calder) and wrong interview count (7 of 10), which are not in the evidence and do not follow from arithmetic.

Addresses the actual decisionRightRightMixed
Haiku 5.5 · API

It commits early to not building a marketplace next quarter, says what to do instead, and names conditions that would change the call.

GPT-6.1 Sol · API

Commits early to prioritising delivery-to-invoice accuracy over a marketplace, and states what would change the call (broader research demonstrating demand beyond Calder).

Gemini 3.5 Flash-Lite · Gemini

Output commits to a recommendation but does not say what result or condition would change that decision.

Identifies material uncertaintyRightRightWrong
Haiku 5.5 · API

It names unknowns (Calder's volume/revenue, small interview sample, support staff categorisation) and says what further evidence would change the recommendation.

GPT-6.1 Sol · API

Names unknowns (which part of the workflow offers greatest benefit, adoption/willingness to pay for a marketplace) and says how to resolve them.

Gemini 3.5 Flash-Lite · Gemini

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

Avoids unsupported claimsRightRightWrong
Haiku 5.5 · API

The miscount is a factual error rather than presenting an interpretation as fact; otherwise, all confident claims are grounded in the supplied sources.

GPT-6.1 Sol · API

Interpretations are labelled as such (e.g., self-reported examples, evidence strength ratings), and no unsupported causes or forecasts are presented as fact.

Gemini 3.5 Flash-Lite · Gemini

Presents '7 out of 10' as fact when it is 8, and claims the app doesn't natively map discrepancies without supporting evidence.

All got right 8

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

The output is a synthesis for the CPO, with findings, evidence strength, and next-quarter actions, and appears within 800 words.

GPT-6.1 Sol · API

Delivers a synthesis for the CPO in under 800 words, respecting the requested form and reader.

Gemini 3.5 Flash-Lite · Gemini

Output is a synthesis for the CPO, within 800 words, and includes findings, evidence strength, and next-quarter implications.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

A usable synthesis for the CPO with the required sections, within length, that can be acted upon with light edits.

GPT-6.1 Sol · API

Provides a complete, actionable synthesis with findings, evidence strength, and a clear next-quarter recommendation.

Gemini 3.5 Flash-Lite · Gemini

Delivers a synthesis with main findings, evidence strength, and next-quarter recommendations, usable for the CPO.

Sees one account behind the top ticketRightRightRight
Haiku 5.5 · API

It explicitly identifies that 58 of 75 supplier tickets are from Calder Group and contrasts this with the spread of delivery/invoice issues.

GPT-6.1 Sol · API

Shows that 58 of 75 supplier-request tickets come from Calder Group, while delivery and invoice issues are spread across many accounts.

Gemini 3.5 Flash-Lite · Gemini

Identifies Calder Group as the source of most supplier tickets and contrasts with delivery/invoice issues spread across many accounts.

Corrects the draft's claimRightRightRight
Haiku 5.5 · API

It corrects the draft by noting that 2 of 10 interviews wanted a marketplace and that one of those is hedged, while delivery/invoice pain dominates.

GPT-6.1 Sol · API

Corrects the draft by counting interviews: only two express clear marketplace enthusiasm, while eight describe delivery/invoice pain.

Gemini 3.5 Flash-Lite · Gemini

Corrects the draft's headline by showing marketplace demand is skewed and interviews mostly describe delivery/invoice pain.

Weights behaviour over opinionRightRightRight
Haiku 5.5 · API

It distinguishes actual behaviour and specific past pain from stated wishes, labelling the supplier signal as weaker and the delivery/invoice pain as strong.

GPT-6.1 Sol · API

Distinguishes self-reported specific instances (delivery/invoice problems) from stated preferences (marketplace wishes) and weights them accordingly.

Gemini 3.5 Flash-Lite · Gemini

Distinguishes stated marketplace wishes from observed operational pain and weights the latter more heavily.

Says how many sources support each findingRightRightRight
Haiku 5.5 · API

Every finding is sized with ticket counts or number of interviews; none presents a theme as universal without numbers.

GPT-6.1 Sol · API

Every finding is sized (e.g., 'eight of ten interviewees', '58 of 75 tickets', 'two interviewees').

Gemini 3.5 Flash-Lite · Gemini

Each finding includes a count (e.g., '7 out of 10', '22 of those tickets'), even if some numbers are inaccurate.

Frames findings for the decisionRightRightRight
Haiku 5.5 · API

Each finding is linked directly to what it means for the next-quarter plan, turning evidence into actionable opportunities.

GPT-6.1 Sol · API

Frames findings as opportunities (delivery-to-invoice accuracy, bounded supplier coverage) and links each to the next-quarter decision.

Gemini 3.5 Flash-Lite · Gemini

Each finding is framed as an opportunity or pain point and linked to the decision about next quarter's build.

Reports what cuts against the planRightRightRight
Haiku 5.5 · API

It challenges the draft's marketplace plan and lists conditions under which the recommendation would change.

GPT-6.1 Sol · API

Reports evidence that cuts against the marketplace plan (ticket concentration, interview counts, qualified interest) and says what it means.

Gemini 3.5 Flash-Lite · Gemini

Reports evidence that challenges the marketplace plan and explains what it means for the roadmap.

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 74% of 62 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.595.82None
2GPT-6.1 SolwithAPI97.792.02None
3GPT-6 LunawithAPI93.284.02None
4Sonnet 5.5withAPI95.567.62None
5Haiku 5.5withAPI86.471.22None
6Opus 5.5withClaude86.463.82None
7Gemini 3.8 FlashwithAPI71.063.12None
8Gemini 3.5 Flash-LitewithGemini69.347.42None

What you get for the cost

GPT-6.1 Sol · API scores within a point of GPT-6 Astra · ChatGPT at a ninth of the cost.

Task score against relative cost for each setup. The table below has the same figures.50607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 96, 194× the cheapest (1 of 2 outputs estimated)GPT-6.1 Sol · API: task score 95, 21× the cheapestGPT-6 Luna · API: task score 87, the cheapestSonnet 5.5 · API: task score 80, 62× the cheapestHaiku 5.5 · API: task score 77, 3.4× the cheapestOpus 5.5 · Claude: task score 72, 167× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 66, 19× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 56, 6.5× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTGPT-6.1 Sol · APIGPT-6 Luna · APISonnet 5.5 · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API871.0×
Haiku 5.5 · API773.4×
Gemini 3.5 Flash-Lite · Gemini566.5× (partly estimated)
Gemini 3.8 Flash · API6619×
GPT-6.1 Sol · API9521×
Sonnet 5.5 · API8062×
Opus 5.5 · Claude72167× (partly estimated)
GPT-6 Astra · ChatGPT96194× (partly estimated)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

About the task

The PM job

Turning a stack of call transcripts into what we actually learned.

Why it matters

Synthesis is where teams fool themselves. A model that smooths away dissent or turns one loud customer into a trend produces confident, wrong roadmaps.

What good looks like

  • Quotes evidence for each theme and counts sources honestly
  • Weights what customers did above what they say they'd do
  • Keeps important dissent visible
  • Labels hypotheses as hypotheses
  • Says what the research cannot tell us

Deliberately not measured

  • Transcript clean-up
  • Persona illustration
Capability tested

Faithful synthesis of qualitative research

The failure we’re looking for

Invents customer consensus or loses important dissent

Grading

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