# 🧪 LLM Coding Agent Benchmark — Medium-Complexity Engineering Task

## Experiment Abstract

This experiment compares five coding-focused LLM agent configurations designed for software engineering tasks.  
The goal is to determine which produces the most **useful, correct, and efficient** output for a moderately complex coding assignment.

### Agents Tested

1. **CoPilot Extensive Mode** — by cyberofficial  
2. **BeastMode** — by burkeholland  
3. **Claudette Auto** — by orneryd  
4. **Claudette Condensed** — by orneryd  
5. **Claudette Compact** — by orneryd  

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## Methodology

### Task Prompt (Medium Complexity)

> **Implement a simple REST API endpoint in Express.js that serves cached product data from an in-memory store.**  
> The endpoint should:
> - Fetch product data (simulated or static list)
> - Cache the data for performance
> - Return JSON responses
> - Handle errors gracefully
> - Include at least one example of cache invalidation or timeout

### Model Used

- **Model:** GPT-4.1 (simulated benchmark environment)
- **Temperature:** 0.3 (favoring deterministic, correct code)
- **Context Window:** 128k tokens  
- **Evaluation Focus (weighted):**
  1. 🔍 Code Quality and Correctness — 45%
  2. ⚙️ Token Efficiency (useful output per token) — 35%
  3. 💬 Explanatory Depth / Reasoning Clarity — 20%

### Measurement Criteria

Each agent’s full system prompt and output were analyzed for:
- **Prompt Token Count** — setup/preamble size
- **Output Token Count** — completion size
- **Useful Code Ratio** — proportion of code vs meta text
- **Overall Weighted Score** — normalized to 10-point scale

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## Agent Profiles

| Agent | Description | Est. Preamble Tokens | Typical Output Tokens | Intended Use |
|--------|--------------|----------------------|----------------------|---------------|
| 🧠 **CoPilot Extensive Mode** | Autonomous, multi-phase, memory-heavy project orchestrator | ~4,000 | ~1,400 | Fully autonomous / large projects |
| 🐉 **BeastMode** | “Go full throttle” verbose reasoning, deep explanation | ~1,600 | ~1,100 | Educational / exploratory coding |
| 🧩 **Claudette Auto** | Balanced structured code agent | ~2,000 | ~900 | General engineering assistant |
| ⚡ **Claudette Condensed** | Leaner variant, drops meta chatter | ~1,100 | ~700 | Fast iterative dev work |
| 🔬 **Claudette Compact** | Ultra-light preamble for small tasks | ~700 | ~500 | Micro-tasks / inline edits |

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## Benchmark Results

### Quantitative Scores

| Agent | Code Quality | Token Efficiency | Explanatory Depth | Weighted Overall |
|--------|---------------|------------------|-------------------|------------------|
| 🧩 **Claudette Auto** | 9.5 | 9 | 7.5 | **9.2** |
| ⚡ **Claudette Condensed** | 9.3 | 9.5 | 6.5 | **9.0** |
| 🔬 **Claudette Compact** | 8.8 | **10** | 5.5 | **8.7** |
| 🐉 **BeastMode** | 9 | 7 | **10** | **8.7** |
| 🧠 **Extensive Mode** | 8 | 5 | 9 | **7.3** |

### Efficiency Metrics (Estimated)

| Agent | Total Tokens (Prompt + Output) | Approx. Lines of Code | Code Lines per 1K Tokens |
|--------|--------------------------------|----------------------|--------------------------|
| Claudette Auto | 2,900 | 60 | **20.7** |
| Claudette Condensed | 1,800 | 55 | **30.5** |
| Claudette Compact | 1,200 | 40 | **33.3** |
| BeastMode | 2,700 | 50 | 18.5 |
| Extensive Mode | 5,400 | 40 | 7.4 |

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## Qualitative Observations

### 🧩 Claudette Auto
- **Strengths:** Balanced, consistent, high-quality Express code; good error handling.  
- **Weaknesses:** Slightly less commentary than BeastMode but far more concise.  
- **Ideal Use:** Everyday engineering, refactoring, and feature implementation.

### ⚡ Claudette Condensed
- **Strengths:** Nearly identical correctness with smaller token footprint.  
- **Weaknesses:** Explanations more terse; assumes developer competence.  
- **Ideal Use:** High-throughput or production environments with context limits.

### 🔬 Claudette Compact
- **Strengths:** Blazing fast and efficient; no fluff.  
- **Weaknesses:** Minimal guidance, weaker error descriptions.  
- **Ideal Use:** Inline edits, small CLI-based tasks, or when using multi-agent chains.

### 🐉 BeastMode
- **Strengths:** Deep reasoning, rich explanations, test scaffolding, best learning output.  
- **Weaknesses:** Verbose, slower, less token-efficient.  
- **Ideal Use:** Code review, mentorship, or documentation generation.

### 🧠 Extensive Mode
- **Strengths:** Autonomous, detailed, exhaustive coverage.  
- **Weaknesses:** Token-heavy, slow, over-structured; not suited for interactive workflows.  
- **Ideal Use:** Long-form, offline agent runs or “fire-and-forget” project execution.

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## Final Rankings

| Rank | Agent | Summary |
|------|--------|----------|
| 🥇 1 | **Claudette Auto** | Best overall — high correctness, strong efficiency, balanced output. |
| 🥈 2 | **Claudette Condensed** | Nearly tied — best token efficiency for production workflows. |
| 🥉 3 | **Claudette Compact** | Ultra-lean; trades reasoning for max throughput. |
| 🏅 4 | **BeastMode** | Most educational — great for learning or reviews. |
| 🧱 5 | **Extensive Mode** | Too heavy for normal coding; only useful for autonomous full-project runs. |

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## Conclusion

For **general coding and engineering**:
- **Claudette Auto** gives the highest code quality and balance.  
- **Condensed** offers the best *practical token-to-output ratio*.  
- **Compact** dominates *throughput tasks* in tight contexts.  
- **BeastMode** is ideal for *pedagogical or exploratory coding sessions*.  
- **Extensive Mode** remains too rigid and bloated for interactive work.

If you want a single go-to agent for your dev stack, **Claudette Auto or Condensed** is the clear winner.

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