fai-model-comparison-benchmark — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited fai-model-comparison-benchmark (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Compare AI models on cost, latency, and quality to select the best fit.
import time, json
def benchmark_model(model: str, client, test_prompts: list[str],
max_tokens: int = 500) -> dict:
latencies, costs, qualities = [], [], []
for prompt in test_prompts:
start = time.monotonic()
resp = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
max_tokens=max_tokens,
)
elapsed = (time.monotonic() - start) * 1000
latencies.append(elapsed)
costs.append(estimate_cost(resp.usage, model))
return {
"model": model,
"latency_p50_ms": sorted(latencies)[len(latencies)//2],
"latency_p95_ms": sorted(latencies)[int(len(latencies)*0.95)],
"avg_cost_per_call": sum(costs) / len(costs),
"total_cost": sum(costs),
"n": len(test_prompts),
}def compare_models(models: list[str], client, prompts, judge) -> list[dict]:
results = []
for model in models:
metrics = benchmark_model(model, client, prompts)
# Add quality scoring
quality_scores = []
for prompt in prompts[:20]: # Sample for quality
resp = client.chat.completions.create(
model=model, messages=[{"role": "user", "content": prompt}])
score = judge_quality(resp.choices[0].message.content, prompt, judge)
quality_scores.append(score)
metrics["avg_quality"] = sum(quality_scores) / len(quality_scores)
results.append(metrics)
return sorted(results, key=lambda x: x["avg_quality"], reverse=True)| Factor | GPT-4o | GPT-4o-mini | When to Choose |
|---|---|---|---|
| Quality | Highest | Good | 4o for complex reasoning |
| Latency | Higher | Lower | mini for real-time chat |
| Cost | $2.50/M in | $0.15/M in | mini for high-volume |
| Context | 128K | 128K | Equal |
| Best for | Analysis, code gen | Classification, extraction | Match to task complexity |
def print_report(results: list[dict]):
print(f"{'Model':<20} {'P50 ms':<10} {'P95 ms':<10} {'Quality':<10} {'Cost/call':<12}")
for r in results:
print(f"{r['model']:<20} {r['latency_p50_ms']:<10.0f} "
f"{r['latency_p95_ms']:<10.0f} {r['avg_quality']:<10.2f} "
f"${r['avg_cost_per_call']:<11.4f}")| Issue | Cause | Fix |
|---|---|---|
| Inconsistent quality scores | High temperature | Set temperature=0 for benchmarks |
| Latency varies wildly | Cold start or throttling | Warm up, run 50+ samples |
| Cost estimate wrong | Not counting system prompt | Include all message tokens |
| Mini beats 4o on task | Task is simple extraction | Use mini — save 94% cost |
| Practice | Rationale |
|---|---|
| Start simple, add complexity when needed | Avoid over-engineering |
| Automate repetitive tasks | Consistency and speed |
| Document decisions and tradeoffs | Future reference for the team |
| Validate with real data | Don't rely on synthetic tests alone |
| Review with peers | Fresh eyes catch blind spots |
| Iterate based on feedback | First version is never perfect |
fai-implementation-plan-generator — Planning and milestonesfai-review-and-refactor — Code review patternsfai-quality-playbook — Engineering quality standards~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.