Comprehensive benchmarking system for comparing Claude vs GPT-4 performance, with intelligent prompt caching achieving 25% cost reduction.
Choosing the right LLM for production involves complex trade-offs. Organizations struggle with:
A data-driven evaluation framework that quantifies performance:
Modular design separating evaluation, optimization, and visualization concerns.
Core framework built with modern Python 3.9+ features including dataclasses, type hints, and async support for efficient API calls.
Direct integration with Claude models (Opus, Sonnet, Haiku) for comprehensive benchmarking across the Claude family.
GPT-4 and GPT-3.5 Turbo integration for head-to-head comparison against Claude models on identical tasks.
Interactive dashboard for real-time benchmarking visualization, cost analysis, and stakeholder reporting.
@dataclass
class EvaluationResult:
"""Container for evaluation metrics"""
model: str
prompt: str
response: str
latency_ms: float
input_tokens: int
output_tokens: int
cost_usd: float
quality_score: Optional[float] = None
@property
def tokens_per_second(self) -> float:
"""Calculate throughput"""
return (self.output_tokens / self.latency_ms) * 1000class PromptCache:
"""LRU Cache reducing redundant API calls"""
def __init__(self, max_size=1000, ttl_seconds=3600):
self.cache: OrderedDict = OrderedDict()
self.stats = {"hits": 0, "misses": 0, "tokens_saved": 0def get(self, prompt: str, model: str) -> Optional[CacheEntry]:
prompt_hash = self._hash_prompt(prompt, model)
if prompt_hash in self.cache:
self.stats["hits"] += 1
self.stats["tokens_saved"] += entry.total_tokens
return self.cache[prompt_hash]
return Noneclass ContextWindowOptimizer:
# Compression patterns for token reduction
COMPRESSION_MAP = {
r'\bplease\s+could you\s+': '',
r'\bin order to\b': 'to',
r'\bdue to the fact that\b': 'because',
r'\bit is important to note that\b': 'note:',
}
def compress_prompt(self, prompt: str) -> Tuple[str, List]:
for pattern, replacement in self.COMPRESSION_MAP.items():
prompt = re.sub(pattern, replacement, prompt)
return prompt, techniques_appliedCombined savings from caching and prompt compression techniques
Avoided API calls through LRU cache with 1-hour TTL
Token reduction via verbose phrase elimination
Comprehensive task coverage across reasoning, coding, and creative domains.
Multi-step deduction puzzles testing inference capabilities.
Word problems requiring step-by-step calculation.
Algorithm implementation with edge case handling.
Bug identification and explanation tasks.
Concise extraction from long-form content.
Constrained generation like haiku composition.
Structured JSON output from unstructured text.
Knowledge recall and accuracy testing.
Track latency (avg, P50, P95), throughput (tokens/sec), cost (per-request and aggregate), and quality scores with custom evaluators. Export results to JSON for further analysis.
Built-in pricing data for Claude (Opus, Sonnet, Haiku) and GPT (4, 4-Turbo, 3.5-Turbo). Project daily, monthly, and yearly costs based on expected usage patterns.
Streamlit-based UI with Plotly charts for real-time visualization. Compare models side-by-side, analyze task-level performance, and generate stakeholder-ready reports.
Full framework functionality without API keys. Simulated latency and responses allow testing the evaluation pipeline before committing to API costs.
Full source code with documentation available on GitHub.