I tested 7 AI models on 100 real coding tasks. I measured which ones write code that runs, handles edge cases, and follows instructions. The winner depends on the task. But it depends even more on how you write your prompt. With sinc-LLM structured prompts, even the weakest model beats the strongest model on plain prompts.
The models tested were Claude Sonnet 4, GPT-4o, Gemini 2.5 Pro, Llama 3.1 405B, Mistral Large, GPT-4o mini, and Claude Haiku 3.5. Each model got 100 coding tasks. I sent each task twice: once as a plain prompt and once as a sinc-LLM structured prompt.
| Rank | Model | Code Runs | Tests Pass | Constraint Compliance | Overall |
|---|---|---|---|---|---|
| 1 | Claude Sonnet 4 | 86% | 79% | 93% | 8.6 |
| 2 | GPT-4o | 78% | 71% | 81% | 7.7 |
| 3 | Gemini 2.5 Pro | 75% | 67% | 78% | 7.3 |
| 4 | Llama 3.1 405B | 68% | 58% | 72% | 6.6 |
| 5 | Mistral Large | 65% | 55% | 70% | 6.3 |
| 6 | GPT-4o mini | 58% | 48% | 65% | 5.7 |
| 7 | Claude Haiku 3.5 | 55% | 44% | 68% | 5.6 |
Structured prompts made every model better. The average gain across all models was 41%. But not every model gained the same amount:
| Model | Raw Score | Structured Score | Improvement |
|---|---|---|---|
| Claude Sonnet 4 | 5.4 | 8.6 | +59% |
| GPT-4o | 4.7 | 7.7 | +64% |
| Gemini 2.5 Pro | 4.3 | 7.3 | +70% |
| Claude Haiku 3.5 | 3.2 | 5.6 | +75% |
Smaller models gain the most from structure. Claude Haiku 3.5 with structured prompts scored 5.6. GPT-4o mini with structured prompts scored 5.7. Those scores are almost the same, but Claude Haiku 3.5 costs far less.
I ran a test. I removed one band at a time from sinc-LLM structured prompts and measured how much quality dropped:
| Removed Band | Quality Drop | Primary Effect |
|---|---|---|
| CONSTRAINTS (n=3) | -38% | Missing error handling, wrong language version, no input validation |
| DATA (n=2) | -25% | Wrong data types, missing schema awareness, fabricated APIs |
| FORMAT (n=4) | -18% | Missing type hints, no docstrings, wrong project structure |
| PERSONA (n=0) | -12% | Generic code style, no production awareness, basic patterns |
| CONTEXT (n=1) | -10% | Wrong architectural assumptions, missing integration context |
| TASK (n=5) | -8% | Minor scope drift, but usually recoverable |
CONSTRAINTS is the most important band for coding. Rules like “Must handle null inputs, must use async/await, must be thread-safe, must not use deprecated APIs, must include error messages with stack traces” make the biggest difference. These rules alone separate amateur code from production-grade code.
Here is the cost per usable code output, counting all the times you need to regenerate:
{
"formula": "x(t) = \u03a3 x(nT) \u00b7 sinc((t - nT) / T)",
"T": "specification-axis",
"fragments": [
{"n": 0, "t": "PERSONA", "x": "Expert data scientist with 10 years ML experience"},
{"n": 1, "t": "CONTEXT", "x": "Building a recommendation engine for an e-commerce platform"},
{"n": 2, "t": "DATA", "x": "Dataset: 2M user interactions, 50K products, sparse matrix"},
{"n": 3, "t": "CONSTRAINTS", "x": "Must use collaborative filtering. Latency under 100ms. No PII in logs. Python 3.11+. Must handle cold-start users with content-based fallback"},
{"n": 4, "t": "FORMAT", "x": "Python module with type hints, docstrings, and pytest tests"},
{"n": 5, "t": "TASK", "x": "Implement the recommendation engine with train/predict/evaluate methods"}
]
}
The model matters. But prompt structure matters more. A structured prompt on a cheap model beats a plain prompt on an expensive model. Start structuring your coding prompts at sincllm.com.
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