Claude API vs OpenAI GPT: Ultimate Performance Guide

// Claude API Implementation

import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});
const claudeAnalysis = async (propertyData: string) => {
  const message = await anthropic.messages.create({
    model: "claude-3-5-sonnet-20241022",
    max_tokens: 1024,
    messages: [{
      role: "user",
      content: Analyze this property data for investment potential: ${propertyData}
    }]
  });
  
  return message.content[0].text;
};

// OpenAI GPT Implementation

import OpenAI from 'openai';
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});
const gptAnalysis = async (propertyData: string) => {
  const completion = await openai.chat.completions.create({
    model: "gpt-4-turbo-preview",
    messages: [{
      role: "user",
      content: Analyze this property data for investment potential: ${propertyData}
    }],
    max_tokens: 1024,
    temperature: 0.1
  });
  
  return completion.choices[0].message.content;
};

class LLMService {

  private claudeClient: Anthropic;
  private openaiClient: OpenAI;
  private rateLimiter: RateLimiter;
  
  constructor() {
    this.claudeClient = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
    this.openaiClient = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
    this.rateLimiter = new RateLimiter({ tokensPerMinute: 10000 });
  }
  
  async processWithFallback(prompt: string, preferredModel: 'claude' | 'gpt' = 'claude') {
    await this.rateLimiter.acquire();
    
    try {
      if (preferredModel === 'claude') {
        return await this.callClaude(prompt);
      } else {
        return await this.callGPT(prompt);
      }
    } catch (error) {
      console.warn(${preferredModel} failed, trying fallback:, error.message);
      
      // Implement fallback logic
      if (preferredModel === 'claude') {
        return await this.callGPT(prompt);
      } else {
        return await this.callClaude(prompt);
      }
    }
  }
  
  private async callClaude(prompt: string) {
    const response = await this.claudeClient.messages.create({
      model: "claude-3-5-sonnet-20241022",
      max_tokens: 2048,
      messages: [{ role: "user", content: prompt }]
    });
    
    return this.validateResponse(response.content[0].text);
  }
  
  private async callGPT(prompt: string) {
    const response = await this.openaiClient.chat.completions.create({
      model: "gpt-4-turbo-preview",
      messages: [{ role: "user", content: prompt }],
      max_tokens: 2048,
      temperature: 0.1
    });
    
    return this.validateResponse(response.choices[0].message.content);
  }
  
  private validateResponse(content: string): string {
    if (!content || content.length < 10) {
      throw new Error('Invalid response received');
    }
    return content;
  }
}

interface CachedResponse {

  content: string;
  timestamp: number;
  model: string;
}
class OptimizedLLMService extends LLMService {
  private responseCache: Map<string, CachedResponse> = new Map();
  private readonly CACHE_TTL = 3600000; // 1 hour
  
  async getOptimizedResponse(prompt: string, task: 'code' | 'analysis' | 'creative') {
    const cacheKey = this.generateCacheKey(prompt, task);
    const cached = this.responseCache.get(cacheKey);
    
    if (cached && Date.now() - cached.timestamp < this.CACHE_TTL) {
      return cached.content;
    }
    
    // Route to optimal model based on task type
    const preferredModel = this.selectOptimalModel(task);
    const response = await this.processWithFallback(prompt, preferredModel);
    
    this.responseCache.set(cacheKey, {
      content: response,
      timestamp: Date.now(),
      model: preferredModel
    });
    
    return response;
  }
  
  private selectOptimalModel(task: string): 'claude' | 'gpt' {
    const modelPreferences = {
      'code': 'claude',      // Better at code analysis
      'analysis': 'claude',   // Superior reasoning
      'creative': 'gpt'       // More creative output
    };
    
    return modelPreferences[task] || 'claude';
  }
}

const claudePrompt = 

You are a real estate analysis expert. Please analyze the following property data systematically:
1. First, examine the financial metrics (price, rent, expenses)
2. Then, evaluate the location factors (neighborhood, schools, transportation)
3. Finally, assess the investment potential with specific recommendations
Property Data:
${propertyData}
Please structure your response with clear sections and bullet points for each analysis area.
;

const gptPrompt = 

As a seasoned real estate investor, analyze this property and provide insights that would help a client make an informed investment decision. Consider all relevant factors and be creative in identifying opportunities or risks that might not be immediately obvious.
Property Data:
${propertyData}
;

class CostOptimizedService {
  private tokenCounter: TokenCounter;
  
  async processWithBudget(prompt: string, maxCostCents: number = 10) {
    const estimatedTokens = this.tokenCounter.estimate(prompt);
    const estimatedCost = this.calculateCost(estimatedTokens);
    
    if (estimatedCost > maxCostCents) {
      // Use cheaper model or truncate prompt
      return await this.processWithGPT4o(this.truncatePrompt(prompt, maxCostCents));
    }
    
    return await this.processWithOptimalModel(prompt);
  }
  
  private calculateCost(tokens: number, model: string = 'claude'): number {
    const pricing = {
      'claude': { input: 0.0015, output: 0.0075 }, // per 1K tokens
      'gpt4turbo': { input: 0.001, output: 0.003 },
      'gpt4o': { input: 0.0005, output: 0.0015 }
    };
    
    const rates = pricing[model];
    return (tokens * rates.input / 1000) + (tokens * 0.5 * rates.output / 1000); // Estimate 50% output ratio
  }
}

class ResilientLLMService {

  private circuitBreaker: CircuitBreaker;
  private healthChecker: HealthChecker;
  
  constructor() {
    this.circuitBreaker = new CircuitBreaker({
      failureThreshold: 5,
      recoveryTime: 60000,
      monitoringPeriod: 10000
    });
    
    this.healthChecker = new HealthChecker({
      checkInterval: 30000,
      endpoints: ['claude', 'openai']
    });
  }
  
  async robustProcess(prompt: string): Promise<string> {
    const healthyServices = await this.healthChecker.getHealthyServices();
    
    if (healthyServices.length === 0) {
      throw new Error('No healthy LLM services available');
    }
    
    for (const service of healthyServices) {
      try {
        return await this.circuitBreaker.execute(() => 
          this.callService(service, prompt)
        );
      } catch (error) {
        console.warn(Service ${service} failed:, error.message);
        continue;
      }
    }
    
    throw new Error('All LLM services failed');
  }
}

interface APIEvaluationCriteria {
  responseTime: number;        // Weight: 1-10
  accuracy: number;           // Weight: 1-10
  costEfficiency: number;     // Weight: 1-10
  safetyRequirements: number; // Weight: 1-10
  scalabilityNeeds: number;   // Weight: 1-10
}
function calculateAPIScore(api: 'claude' | 'gpt', criteria: APIEvaluationCriteria): number {
  const benchmarks = {
    claude: { responseTime: 7, accuracy: 9, costEfficiency: 6, safety: 10, scalability: 8 },
    gpt: { responseTime: 8, accuracy: 8, costEfficiency: 8, safety: 7, scalability: 9 }
  };
  
  const scores = benchmarks[api];
  const weightedScore = 
    (scores.responseTime * criteria.responseTime) +
    (scores.accuracy * criteria.accuracy) +
    (scores.costEfficiency * criteria.costEfficiency) +
    (scores.safety * criteria.safetyRequirements) +
    (scores.scalability * criteria.scalabilityNeeds);
    
  return weightedScore / (criteria.responseTime + criteria.accuracy + criteria.costEfficiency + criteria.safetyRequirements + criteria.scalabilityNeeds);
}

class HybridLLMStrategy {

  async processRequest(prompt: string, context: RequestContext) {
    const strategy = this.determineOptimalStrategy(context);
    
    switch (strategy) {
      case 'claude-primary':
        return await this.processWithFallback(prompt, 'claude', 'gpt');
      case 'gpt-primary':
        return await this.processWithFallback(prompt, 'gpt', 'claude');
      case 'parallel':
        return await this.processParallel(prompt);
      default:
        return await this.processWithFallback(prompt, 'claude', 'gpt');
    }
  }
  
  private determineOptimalStrategy(context: RequestContext): string {
    if (context.requiresHighAccuracy && context.isSensitiveContent) {
      return 'claude-primary';
    }
    if (context.prioritizeSpeed && context.isCreativeTask) {
      return 'gpt-primary';
    }
    if (context.isCriticalDecision) {
      return 'parallel'; // Compare outputs for validation
    }
    return 'claude-primary'; // Default to safety-first approach
  }
}