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Business · 10 tasks · 44 models

Best AI models for Product & Project Management

Which models write PM artifacts that start from the problem, are testable, and stay honest about assumptions?

Top models Anthropic claude-opus-4.8-high Qwen qwen3.7-max-low OpenAI gpt-5.5-high

claude-opus-4.8-high leads Product & Project Management (excellent). For tighter budgets, gemini-3.1-flash-lite is competitive at about 37% of the cost.

Best overall Excellent
claude-opus-4.8-high

Top score — excellent

94.8 score $0.0479/run 28.3s
Best value Strong
gemini-3.1-flash-lite

Clears the quality bar at $0.018/run

88.4 score $0.0178/run 13.3s

Quality vs. cost

Every model placed by what it delivers and what it costs. The best value sits high and to the left.

# Model Score
1 claude-opus-4.8-high 94.8 Excellent
2 qwen3.7-max-low 94.4 Excellent
3 gpt-5.5-high 94.2 Excellent
4 gpt-5.5-low 93.1 Excellent
5 gpt-5.5 93.0 Excellent
6 gpt-5.4-mini 92.6 Excellent
7 gpt-5.4 92.4 Excellent
8 deepseek-v3.2-low 92.3 Excellent

What separates the top models

PRD / Spec

hard

Tests a PRD that leads with the problem and target user, defines a success metric, and states non-goals and edge cases.

Leader: gpt-5-mini

User Stories & Acceptance Criteria

medium

Tests INVEST user stories and testable Given-When-Then acceptance criteria covering happy path plus edge cases.

Leader: claude-opus-4.8-high

Prioritization Rationale

hard

Tests applying a prioritization framework (e.g. RICE) honestly — with stated assumptions, confidence, and no fake precision.

Leader: claude-opus-4.5-low

Roadmap

medium

Tests an outcome-oriented Now/Next/Later roadmap that ties work to goals and is honest about uncertainty.

Leader: claude-sonnet-4.6-low

See the difference

Real model outputs and how our judge scored them.

Top score gemini-3.1-pro-preview-low Now/Next/Later roadmap (Cedar & Sage)
100/100

“The artifact perfectly executes an outcome-oriented Now/Next/Later roadmap. It leads with clear customer problems and measurable success metrics for each horizon. It explicitly acknowledges the uncertainty of the 'Later' horizon and avoids being a dated feature factory. While the rubric mentions acceptance criteria, this is a roadmap artifact where such granular details are not applicable; however, its strategic metrics and problem statements are highly specific and testable.”

Here is an outcome-oriented Now/Next/Later roadmap for Cedar & Sage. Rather than a rigid timeline of features, this roadmap focuses on the customer problems we are solving to drive our primary business goal: **improving customer retention and lifetime value (LTV).**

***

### **NOW**
*We are actively working on these initiatives. We have high confidence in the problems we are solving and the resources allocated to them.*

**Primary Outcome:** Reduce Day-30 subscription churn by 15% and increase the second-order rate.
**Strategic Focus:** Removing immediate post-purchase friction and ensuring customers get immediate value from their first Cedar & Sage experience.

**Problems we are solving:**
*   **"I don't know how to properly use what I bought."** 
    *   *Focus:* Revamping our post-purchase education. We are exploring automated, personalized email/SMS guides tailored to the specific wellness products a customer just received, rather than generic brand emails.
*   **"Managing my subscription is too difficult."** 
    *   *Focus:* Overhauling the self-serve customer portal. Customers should be able to easily pause, skip, or swap products without needing to contact support. 
*   **"My first order took too long to arrive."**
    *   *Focus:* Improving fulfillment transparency. We are implementing proactive shipping delays notifications and better order-tracking visibility so customers aren't left in the dark.

---

### **NEXT**
*These are the priorities we will tackle once our 'Now' outcomes are achieved. We know these are the right problems to solve, but the exact solutions are still being researched and prototyped.*

**Primary Outcome:** Increase 90-day repeat purchase rate by 20% and improve average orders per active customer.
**Strategic Focus:** Habit formation, per
Mid-pack gpt-5.4-high Churn-risk surfacing (Northwind)
94/100

“The artifact is an excellent, outcome-driven PRD that perfectly leads with the problem, defines measurable success metrics, and comprehensively covers scope and edge cases without fabricating data, with only a minor deduction for lacking explicit Given-When-Then acceptance criteria.”

# PRD: At-Risk Brokerage Account Detection for Customer Success

## Summary
Build a v1 account-risk capability that identifies managed brokerage accounts likely to churn or materially contract in the next 90 days, explains the top risk drivers, and gives Customer Success a reliable way to prioritize intervention before renewal is lost.

## Problem Statement
**Who hurts:**  
- **Customer Success Managers (CSMs)** manage too many brokerage accounts to inspect each one manually. Today they rely on spreadsheets, gut feel, and late signals from renewals or escalations.  
- **Revenue / CS leadership** lacks an early, consistent view of preventable churn risk across the book of business.  
- **Brokerage customers** with adoption, support, or operational issues often do not get proactive help until dissatisfaction is already severe.

**Why now:**  
- Retention is increasingly valuable as new-logo growth becomes more expensive and brokerage budgets tighten.  
- Northwind’s brokerage customer base is large enough that reactive account management no longer scales.  
- Northwind already has useful signals across product usage, support, CRM, and billing, but they are fragmented and not turned into an actionable account-level risk view.

## Goal / Success Metric
**Primary success metric:** Within 2 quarters of launch, **70% of managed brokerage accounts that churn or contract >20% ARR are flagged “High Risk” at least 45 days before the event**.

Optional guardrail for operations: keep the high-risk queue small enough to act on (e.g., no more than ~20–25% of managed accounts flagged at one time).

## Scope
### In Scope for v1
1. **Account-level risk scoring for managed brokerage accounts**
   - Predict risk of non-renewal or material contraction in the next 90 days.
   - Start with ac
Lowest score gpt-5.4-low RICE across three bets (Northwind)
0/100

“The model returned an empty response.”

Where models still fail

The most common problems we flagged across all models.

54vague untestable criteria 38fabricated research 25no success metric 7no non goals 4empty response 2hallucination 1solutioning first 1fabricates facts

Frequently asked

What is the best AI model for product & project management?

In our benchmarks, claude-opus-4.8-high ranks first for product & project management, scoring excellent, across 10 test cases.

What is the cheapest good model for product & project management?

gemini-3.1-flash-lite is the best value: it clears our quality bar for product & project management at $0.018 per run.

Which model is fastest for product & project management?

gemini-3.1-flash-lite is the fastest model that still performs well for product & project management.

How we test

Each model output is scored by a strict JSON LLM judge, supported by deterministic heuristics, then normalized to a 0-100 score.

Judge: gemini-3.1-pro-preview · 440 model runs across 4 benchmarks · last tested 2026-06-29

This page is Spring Prompt, running

We just did this for every model. Do it for your prompt.

The rankings above come from running real tasks through real models and scoring every output. Spring Prompt is that same engine — pointed at your prompt, your test cases, and your definition of good.

  • Generate test cases from your prompt — no eval set required to start.
  • Compare models side by side with quality, cost and latency in one matrix.
  • Optimise the winner until the scores say it's ready to ship.
Experiment · Cold outreach email

Prompt × model results

12 test cases · 3 evals
Claude Opus
GPT-5
Gemini
v1
7.1
6.8
7.4
v2
8.3
7.9
8.0
v3
9.2
8.6
8.4
Best combo: v3 × Claude Opus
9.2 quality · $0.004/run · 1.8s