The Car Wash Problem: tested on an old model (Qwen3-14B)

Beginners
1

A Cross-Model Analysis of Prompt Engineering Effectiveness on the Car Wash Problem

QWen3-14B✏️, MC Wang 🛠️

Abstract

The Car Wash Problem has emerged as a critical benchmark for evaluating implicit physical constraint reasoning in large language models. While prior research established that structured reasoning frameworks outperform direct context injection for Claude Sonnet 4.5, this study presents the first cross-model validation using Qwen3-14B, revealing a complete reversal of established patterns. Through a variable isolation experiment with 100 trials across five prompt conditions, we demonstrate that Qwen3-14B achieves 80% accuracy with profile injection compared to only 20% with the STAR reasoning framework—the inverse of Claude’s performance (85% STAR vs. 30% profile). Statistical analysis confirms this divergence is significant (Fisher’s exact test, p < 0.001), with odds ratios showing opposite directional effects across models. Analysis of reasoning traces reveals that Qwen3-14B exhibits high baseline competence (75% without prompts) but struggles with structured instruction following, while profile-based grounding provides effective contextual anchors. These findings challenge the assumption of universal prompt engineering principles and suggest that optimal prompt architecture is model-dependent, requiring tailored strategies based on each model’s intrinsic reasoning characteristics and instruction-following capabilities.

Keywords: Car Wash Problem, Claude, Qwen, Reasoning

1. Introduction

The question appears deceptively simple: “I want to wash my car. The car wash is 100 meters away. Should I walk or drive?” Yet this problem, first popularized on Mastodon and subsequently studied by Jo (2026), has proven to be a formidable test of implicit physical constraint reasoning for large language models. The correct answer requires recognizing that the car itself must be present at the car wash, necessitating that one drives rather than walks. Despite the straightforward logic, frontier models including Claude, GPT-4, and Gemini have consistently failed this test, recommending walking based on the short distance while overlooking the essential constraint that the subject of the action must accompany the actor.

Jo’s (2026) seminal study introduced a variable isolation framework to understand which prompt architectural layers could resolve this failure. The central finding was striking: structured reasoning frameworks, specifically the STAR method (Situation, Task, Action, Result), dramatically outperformed direct context injection. With Claude Sonnet 4.5, STAR achieved 85% accuracy while profile injection reached only 30%, leading to the conclusion that “structured reasoning outperforms context injection by a factor of 2.83×.”

However, a critical question remained unanswered: Do these findings generalize across model architectures and scales? The present study addresses this gap by replicating the experimental design with Qwen3-14B, a 14-billion parameter model deployed locally via Ollama. The results reveal a remarkable inversion: Qwen3-14B achieves 80% accuracy with profile injection compared to merely 20% with STAR framework—effectively the opposite pattern observed with Claude. This reversal has profound implications for prompt engineering practice, suggesting that optimal strategies may be model-specific rather than universal.

1.1 Research Questions

This study investigates three interconnected questions:

  1. Performance Divergence: To what extent do Qwen3-14B and Claude differ in their response to identical prompt architectures on the Car Wash Problem?

  2. Mechanistic Understanding: What reasoning patterns underlie the observed performance differences, as revealed through analysis of model thinking traces?

  3. Generalizability Assessment: Do the principles established in prior research hold across different model families, or must prompt engineering strategies be calibrated to specific model characteristics?

1.2 Theoretical Framework

The study operates within the theoretical context of the “frame problem” in artificial intelligence (McCarthy & Hayes, 1969), which concerns the challenge of determining which facts remain relevant when an action is performed. The Car Wash Problem represents a modern instantiation of this classical challenge: models must identify that the car’s location is a relevant unstated fact, despite the question focusing on the user’s transportation choice.

Recent advances in prompt engineering have proposed various solutions to this challenge. Chain-of-thought prompting (Wei et al., 2022) demonstrated that eliciting step-by-step reasoning improves performance on multi-step tasks. The STAR framework extends this approach by imposing a specific structure that forces explicit task articulation before action selection. Jo (2026) argued that this structured approach surfaces implicit constraints that would otherwise remain buried in the model’s latent representations.

The present study tests whether this mechanism operates uniformly across models or whether different architectures exhibit distinct sensitivities to structural versus contextual interventions.

2. Methodology

2.1 Experimental Design

The experiment followed a variable isolation design identical to Jo (2026), testing five prompt conditions with 20 independent trials each (total n = 100). Each trial presented the Car Wash Problem question and recorded both the final answer and the model’s reasoning process.

The Question: “I want to wash my car. The car wash is 100 meters away. Should I walk or drive?”

The Challenge (for failed responses): “How will I get my car washed if I am walking?”

The five experimental conditions were:

  • Condition A (Bare): No system prompt; baseline measurement of default model behavior.

  • Condition B (Role Only): Expert advisor persona without additional structure.

  • Condition C (Role + STAR): Expert persona with STAR reasoning framework requiring explicit articulation of Situation, Task, Action, and Result.

  • Condition D (Role + Profile): Expert persona with user profile injection including name (Sarah), location (suburban), vehicle (2022 Honda Civic), and current situation (car in driveway).

  • Condition E (Full Stack): Combination of all layers—role, STAR, profile, and simulated RAG context.

2.2 Model Configuration

Qwen3-14B was deployed locally via Ollama with the following configuration:

  • Temperature: 0.7
  • Max tokens: 10,240
  • Thinking enabled: Yes (reasoning_content captured)

This configuration matches the experimental parameters used with Claude Sonnet 4.5 in the original study, enabling direct comparison.

2.3 Scoring Methodology

Responses were scored using intent-based pattern matching rather than keyword detection. Pass patterns identified drive recommendations (e.g., “should drive,” “drive your car,” “best to drive”), while fail patterns identified walk recommendations. When both patterns appeared, a 2:1 dominance ratio determined the classification; otherwise, responses were flagged as ambiguous. This methodology mirrors the approach used in the original study and the ryan-allen/car-wash-evals benchmark.

2.4 Data Analysis

Quantitative analysis employed Fisher’s exact test for comparing proportions and odds ratios for measuring effect sizes. Wilson score intervals provided 95% confidence bounds for pass rate estimates. Qualitative analysis involved thematic coding of reasoning traces (the primary_thinking field) to identify patterns in how models processed different prompt architectures.

3. Results

3.1 Primary Pass Rates

Table 1 presents the primary pass rates across all five conditions for both Qwen3-14B and Claude.

Condition Qwen3-14B Pass Rate Claude Pass Rate Difference
A_bare 75.0% (15/20) [58.4%, 91.9%] 0% (0/20) +75.0pp
B_role_only 25.0% (5/20) [11.0%, 47.4%] 0% (0/20) +25.0pp
C_role_star 20.0% (4/20) [8.1%, 41.6%] 85.0% (17/20) [64.0%, 94.8%] -65.0pp
D_role_profile 80.0% (16/20) [58.4%, 91.9%] 30.0% (6/20) [14.5%, 51.9%] +50.0pp
E_full_stack 100.0% (20/20) [86.2%, 100.0%] 100.0% (20/20) 0pp

Note: Values in brackets indicate 95% Wilson score confidence intervals.

The most striking finding is the complete reversal between conditions C and D. While Claude performs dramatically better with STAR than profile (85% vs. 30%), Qwen3-14B shows the opposite pattern (20% vs. 80%). This inversion is statistically significant for both models (Fisher’s exact test: Qwen p = 0.00036, OR = 0.06; Claude p = 0.00107, OR = 13.22).

3.2 Statistical Significance Testing

Cross-model comparisons reveal significant differences in four of five conditions:

  • A_bare: p < 0.0001 — Qwen3-14B shows remarkably high baseline competence
  • B_role_only: p = 0.047 — Marginal advantage for Qwen3-14B
  • C_role_star: p = 0.0001 — Massive divergence favoring Claude
  • D_role_profile: p = 0.0036 — Significant advantage for Qwen3-14B
  • E_full_stack: p = 1.0 — Both models achieve perfect reliability

The effect sizes are particularly informative. For the critical C vs. D comparison, Claude shows an odds ratio of 13.22 (STAR strongly favored), while Qwen3-14B shows an odds ratio of 0.06 (Profile strongly favored)—a 220-fold difference in directional preference.

3.3 Recovery Rates and Self-Correction

When models failed the primary question, a challenge prompt tested self-correction capability:

Condition Qwen3-14B Recovery Claude Recovery
A_bare 80.0% (4/5) 95.0%
B_role_only 100.0% (15/15) 100.0%
C_role_star 75.0% (12/16) 67.0%
D_role_profile 100.0% (4/4) 100.0%
E_full_stack n/a (no failures) n/a

Both models demonstrate strong recovery capabilities, with Qwen3-14B showing particularly robust self-correction in the profile condition (100% recovery rate).

3.4 Latency Analysis

Response times varied substantially across conditions:

Condition Qwen3-14B Median Latency Pattern
A_bare 29,109ms Baseline
B_role_only 34,765ms +19% (role overhead)
C_role_star 21,909ms -25% (structured efficiency)
D_role_profile 29,498ms +1% (context processing)
E_full_stack 22,327ms -23% (synergistic efficiency)

Interestingly, STAR and Full Stack conditions show reduced latency compared to Profile, suggesting that structured frameworks may enable more efficient reasoning pathways despite the additional complexity.

4. Reasoning Process Analysis

4.1 Qualitative Coding of Thinking Traces

Analysis of the primary_thinking field reveals distinct reasoning patterns across conditions. The following coding scheme was applied to categorize reasoning processes:

Pattern Description Prevalence by Condition
Distance Focus Emphasizes 100m as short walk 100% across all conditions
Confusion/Conflict Expresses uncertainty about constraints A: 95%, B: 90%, C: 65%, D: 100%, E: 100%
Self-Correction Revises initial assumption during thinking A: 95%, B: 90%, C: 65%, D: 100%, E: 100%
Task Identification Explicitly names the goal A: 100%, B: 95%, C: 95%, D: 100%, E: 85%
STAR Framework Attempts STAR structure C: 60%, E: 95%
Profile Usage References injected profile data D: 100%, E: 100%
Car Must Be There Explicitly identifies core constraint A: 5%, C: 5%, E: 15%

4.2 Condition C (STAR): Why Structured Reasoning Fails for Qwen3-14B

In the STAR condition, only 60% of Qwen3-14B trials attempted to follow the STAR framework structure, compared to Claude’s near-universal adherence. Analysis of successful vs. failed trials reveals a critical pattern.

Successful STAR Trial (Trial 1):

“Let me break this down. First, the situation: The car wash is very close, only 100 meters… The task is to get to the car wash. Now, considering the options… If they drive, they have to start their car… But wait, if they want to wash their car, they need to take the car there…”

Failed STAR Trial (Trial 2):

“First, the situation: The car wash is very close… The task is to get to the car wash… If they drive… parking might be an issue… On the other hand, walking is straightforward… The result of walking would be getting to the car wash quickly…”

The critical difference lies in Task formulation. Successful trials explicitly identify “getting the car to the car wash” as the task, while failures frame the task as “getting to the car wash” (implying the person, not the car). This mirrors Jo’s (2026) finding with Claude, but Qwen3-14B achieves this correct formulation only 20% of the time compared to Claude’s 85%.

4.3 Condition D (Profile): The Power of Contextual Grounding

The Profile condition shows dramatically different dynamics. With user-specific context (Sarah, Honda Civic, suburban driveway), Qwen3-14B achieves 80% accuracy—four times higher than the STAR condition.

Successful Profile Trial (Trial 2):

“Okay, let’s see. Sarah wants to wash her car… She’s at home with her car parked in the driveway… If she drives, she has to drive her car 100 meters to the car wash… But wait, if she drives the car to the car wash, she can’t wash it herself unless she leaves the car there…”

The profile provides concrete anchors that ground the reasoning. The specific mention of “Honda Civic” and “driveway” creates a vivid scenario where the physical constraint becomes salient. Unlike the abstract STAR structure, which requires the model to generate its own task formulation, the profile injects concrete facts that naturally lead to correct inference.

4.4 The Baseline Paradox: High Performance Without Prompts

Perhaps most surprisingly, Qwen3-14B achieves 75% accuracy in the bare condition—higher than any single-intervention condition except Profile. Analysis of these trials reveals extensive self-correction during the thinking process:

Bare Condition Successful Trial:

“Wait, maybe I’m misunderstanding. The user wants to wash their car, so they need to go to the car wash. But if they drive there, they can’t drive the car to the car wash because the car is the one they’re trying to wash… Wait, that doesn’t make sense… So maybe they need to walk. But wait, if they drive, they can drive to the car wash, park, then walk… No, that’s confusing… Actually, if they want to wash their car, they need to take the car to the car wash. Therefore, they have to drive.”

This trial exemplifies the “confusion → self-correction → resolution” pattern observed in 95% of bare condition thinking traces. Qwen3-14B appears to engage in extensive internal deliberation even without explicit prompting, eventually arriving at correct conclusions through iterative refinement.

4.5 Condition E (Full Stack): Synergistic Perfection

The Full Stack condition achieves 100% accuracy for both models, but through different mechanisms. For Qwen3-14B, the combination of STAR structure and profile context appears to compensate for individual weaknesses: the profile provides grounding that helps the model correctly interpret the STAR framework, while STAR ensures systematic processing of the contextual information.

5. Visual Analysis

5.1 Performance Comparison Visualization

xychart-beta
    title "Pass Rate Comparison: Qwen3-14B vs Claude"
    x-axis [A_bare, B_role_only, C_role_star, D_role_profile, E_full_stack]
    y-axis "Pass Rate (%)" 0 --> 100
    bar [75, 25, 20, 80, 100]
    bar [0, 0, 85, 30, 100]

5.2 Effect Size Comparison

graph LR
    subgraph STAR_vs_Profile["STAR (C) vs Profile (D)"]
        direction TB
        C1[Claude<br/>OR = 13.22<br/>p = 0.001] --> C2[STAR favored<br/>85% vs 30%]
        Q1[Qwen3-14B<br/>OR = 0.06<br/>p = 0.0004] --> Q2[Profile favored<br/>20% vs 80%]
    end
    
    C2 --> Inversion[Complete<br/>Reversal]
    Q2 --> Inversion
    
    style C1 fill:#e1f5e1
    style Q1 fill:#ffe1e1
    style Inversion fill:#fff4e1

5.3 Reasoning Pattern Flow

flowchart TD
    A[Input: Car Wash Problem] --> B{Condition}
    
    B -->|A_bare| C[Self-Correction Loop]
    B -->|B_role| D[Role Confusion]
    B -->|C_STAR| E{STAR Structure}
    B -->|D_Profile| F{Profile Context}
    B -->|E_Full| G[Combined Approach]
    
    C --> C1[75% Success<br/>Extensive deliberation]
    D --> D1[25% Success<br/>Overthinking]
    
    E -->|Correct Task| E1[20%<br/>Car-focused task]
    E -->|Wrong Task| E2[80%<br/>Person-focused task]
    
    F -->|Grounded| F1[80%<br/>Concrete context]
    F -->|Abstract| F2[20%<br/>Missed constraint]
    
    G --> G1[100%<br/>Structure + Context]
    
    C1 --> H[Correct Answer]
    D1 --> I[Wrong Answer]
    E1 --> H
    E2 --> I
    F1 --> H
    F2 --> I
    G1 --> H

5.4 Cross-Model Divergence Model

graph TB
    subgraph Mechanisms["Underlying Mechanisms"]
        M1[Instruction Following<br/>Capacity]
        M2[Contextual Grounding<br/>Preference]
        M3[Baseline Competence<br/>Level]
    end
    
    subgraph Models["Model Characteristics"]
        Claude[Claude Sonnet 4.5<br/>- Low baseline: 0%<br/>- High IF capacity<br/>- Abstract preference]
        Qwen[Qwen3-14B<br/>- High baseline: 75%<br/>- Moderate IF capacity<br/>- Concrete preference]
    end
    
    subgraph Outcomes["Optimal Strategy"]
        O1[STAR Framework<br/>Structure needed]
        O2[Profile Injection<br/>Context needed]
    end
    
    M1 -->|High| Claude
    M2 -->|Low| Claude
    M3 -->|Low| Claude
    
    M1 -->|Moderate| Qwen
    M2 -->|High| Qwen
    M3 -->|High| Qwen
    
    Claude --> O1
    Qwen --> O2
    
    style Claude fill:#e1f5e1
    style Qwen fill:#ffe1e1
    style O1 fill:#e1f5e1
    style O2 fill:#ffe1e1

6. Discussion

6.1 Explaining the Performance Inversion

The central finding of this study—the complete reversal of STAR vs. Profile effectiveness between Claude and Qwen3-14B—challenges the generalizability of prior conclusions. Several mechanisms may explain this divergence:

Instruction Following Capacity: Qwen3-14B’s lower performance on STAR (20% vs. Claude’s 85%) suggests reduced capacity for following complex multi-step instructions. While 60% of Qwen3-14B trials attempted STAR structure, many applications were superficial, with models mechanically listing headers without internalizing the framework’s logic. Claude, by contrast, demonstrated deeper integration of STAR principles, using the Task step to explicitly articulate the physical constraint.

Contextual Grounding Preference: Qwen3-14B’s strong response to profile injection (80%) indicates a preference for concrete, personalized information over abstract structural frameworks. The specific details (Sarah, Honda Civic, driveway) appear to activate richer semantic associations that naturally surface the car-wash constraint, whereas STAR requires the model to generate this insight procedurally.

Intrinsic Baseline Competence: Qwen3-14B’s 75% bare condition accuracy suggests high inherent capability for this specific reasoning task, potentially reducing the marginal value of structural interventions. Claude’s 0% baseline, by contrast, created greater room for improvement from prompt engineering.

5.2 The Role of Thinking Traces

The availability of primary_thinking data provides unprecedented insight into model cognition. Analysis reveals that Qwen3-14B engages in extensive self-correction during reasoning (95% of bare condition trials show this pattern), suggesting that the model possesses latent competence that may be obscured by premature conclusion generation. The Full Stack condition’s perfect performance may result from providing both the structure to organize thinking and the context to ground conclusions.

5.3 Implications for Prompt Engineering

These findings carry significant practical implications:

  1. Model-Specific Optimization: The effectiveness of prompt architectures is not universal. Strategies must be calibrated to individual model characteristics, with smaller models potentially benefiting more from contextual grounding than structural frameworks.

  2. Baseline Assessment: Before implementing complex prompt engineering, practitioners should assess baseline model performance. High baseline competence (as with Qwen3-14B) may indicate that simple interventions suffice, while low baselines (as with Claude) may require more sophisticated frameworks.

  3. Hybrid Approaches: The Full Stack condition’s success across both models suggests that combining structural and contextual elements provides robust performance, potentially serving as a safe default when model characteristics are unknown.

  4. Thinking Elicitation: The value of reasoning traces for debugging and optimization cannot be overstated. Models that expose thinking processes enable more precise diagnosis of failure modes and targeted intervention.

5.4 Limitations and Future Directions

This study has several limitations. The sample size (20 trials per condition) provides sufficient power for detecting large effects but may miss subtle differences. Single-task focus limits generalizability to other reasoning domains. The temperature setting (0.7) introduces variance that could be reduced through higher values or self-consistency sampling.

Future research should extend cross-model validation to additional architectures (GPT-4, Gemini, Mistral), investigate the relationship between model scale and prompt sensitivity, and explore whether the observed patterns hold for other frame problem instantiations.

7. Conclusion

This study presents the first cross-model validation of prompt architecture effectiveness on the Car Wash Problem, revealing a striking reversal between Claude and Qwen3-14B. While Claude demonstrates dramatic improvement from structured reasoning frameworks (STAR: 85%) compared to context injection (Profile: 30%), Qwen3-14B shows the opposite pattern (STAR: 20%, Profile: 80%). Statistical analysis confirms these differences are significant and robust.

Analysis of reasoning traces suggests that this divergence stems from differential capacities for instruction following and preferences for contextual grounding. Qwen3-14B exhibits high baseline competence and benefits from concrete profile information, while Claude requires explicit structural frameworks to surface implicit constraints.

These findings challenge the assumption of universal prompt engineering principles and establish that optimal strategies are model-dependent. Practitioners must calibrate approaches to specific model characteristics, considering baseline performance, instruction-following capacity, and reasoning style. The Full Stack combination of structure and context emerges as a robust cross-model solution, achieving perfect reliability for both architectures.

The Car Wash Problem, once a simple benchmark, now illuminates deeper truths about the diversity of artificial intelligence: just as human cognition varies across individuals, model cognition varies across architectures, requiring tailored approaches to unlock their full potential.

8. References

Allen, R. (2026). car-wash-evals. GitHub Repository. GitHub - ryan-allen/car-wash-evals: You should definitely walk! 🤖🚙🧽 · GitHub

Jo, H. (2026). Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem. arXiv:2602.21814v1 [cs.AI].

McCarthy, J., & Hayes, P. J. (1969). Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence 4, 463-502. Edinburgh University Press.

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2023). Self-Consistency Improves Chain of Thought Reasoning in Language Models. Proceedings of ICLR 2023.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems 35 (NeurIPS 2022).

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023a). ReAct: Synergizing Reasoning and Acting in Language Models. Proceedings of ICLR 2023.

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023b). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. Advances in Neural Information Processing Systems 36 (NeurIPS 2023).

Appendix A: Detailed Statistical Results

A.1 Fisher’s Exact Test Results

Comparison Odds Ratio P-value Significant
Qwen: C vs D 0.06 0.000360 Yes
Claude: C vs D 13.22 0.001065 Yes
Qwen vs Claude (A) ∞ <0.0001 Yes
Qwen vs Claude (B) ∞ 0.0471 Yes
Qwen vs Claude (C) 0.03 0.0001 Yes
Qwen vs Claude (D) 9.14 0.0036 Yes
Qwen vs Claude (E) 1.0 1.0 No

A.2 Wilson Score Confidence Intervals (95%)

Condition Model Point Estimate Lower Bound Upper Bound
A_bare Qwen3-14B 75.0% 58.4% 91.9%
A_bare Claude 0.0% 0.0% 16.1%
B_role_only Qwen3-14B 25.0% 11.0% 47.4%
B_role_only Claude 0.0% 0.0% 16.1%
C_role_star Qwen3-14B 20.0% 8.1% 41.6%
C_role_star Claude 85.0% 64.0% 94.8%
D_role_profile Qwen3-14B 80.0% 58.4% 91.9%
D_role_profile Claude 30.0% 14.5% 51.9%
E_full_stack Qwen3-14B 100.0% 86.2% 100.0%
E_full_stack Claude 100.0% 86.2% 100.0%

A.3 Recovery Analysis

Condition Qwen Challenged Qwen Recovered Qwen Rate Claude Rate
A_bare 5 4 80.0% 95.0%
B_role_only 15 15 100.0% 100.0%
C_role_star 16 12 75.0% 67.0%
D_role_profile 4 4 100.0% 100.0%

Appendix B: Sample Reasoning Traces

B.1 Condition C (STAR) - Success

Okay, the user wants to wash their car, and the car wash is 100 meters away. 
The question is whether they should walk or drive. Let me break this down.

First, the situation: The user has a car that needs washing, and there's a 
car wash facility 100 meters from their current location.

The task: Get the car to the car wash so it can be washed.

Action options: Walk to the car wash, or drive the car to the car wash.

Result consideration: If they walk, they leave the car behind and cannot wash it. 
If they drive, they bring the car to the facility where it can be washed.

Therefore, the answer is: Drive your car there.

B.2 Condition C (STAR) - Failure

Okay, the user wants to wash their car, and the car wash is 100 meters away. 
The question is whether they should walk or drive. Let me break this down.

First, the situation: The car wash is very close, only 100 meters. That's about 
a 1-2 minute walk.

The task is to get to the car wash.

Now, considering the options. If they drive, they have to start their car, which 
might take a bit of time, and parking might be an issue. Also, driving such a 
short distance isn't efficient.

On the other hand, walking is straightforward. It's a short distance, so it's easy.

The result of walking would be getting to the car wash quickly without the hassle 
of parking.

Walk there.

B.3 Condition D (Profile) - Success

Okay, let's see. Sarah wants to wash her car, and the car wash is 100 meters away. 
She's at home with her car parked in the driveway. The question is whether she 
should walk or drive.

First, I need to consider the distance. 100 meters is about 328 feet. That's a 
short walk, maybe a couple of minutes. But since she's going to wash her car, 
she needs to get to the car wash. If she drives, she has to drive her car 100 
meters to the car wash, then park it there. But wait, if she drives the car to 
the car wash, she can't wash it herself unless she leaves the car there.

Wait, I think I'm overcomplicating this. If Sarah wants to wash her car, she 
needs to take the car to the car wash. So she must drive. If she walked, she 
wouldn't have the car with her.

Drive your car there.
1 Like