ParrotRouter vs Anthropic

Use Claude models alongside GPT-4, Gemini, and 100+ others through a single, unified API

Quick Comparison

Feature	ParrotRouter	Anthropic Direct
Claude 3 Models (Opus, Sonnet, Haiku)
GPT-4 & GPT-3.5
Google Gemini Models
Open Source Models
OpenAI SDK Compatible
Single API for All Models
Free Credits	$5	$5 (limited trial)
Automatic Fallbacks
Usage Analytics
Rate Limits (Free Tier)	10 req/min	5 req/min

Key Advantages of ParrotRouter

Unified API
One SDK for Everything

Use the familiar OpenAI SDK to access Claude, GPT-4, Gemini, and 100+ other models. No need to learn multiple APIs.

Best of Both Worlds
Claude + GPT-4 Together

Use Claude for nuanced tasks and GPT-4 for others. Switch models with a single parameter change.

Cost Efficiency
Smart Model Routing

Automatically use Haiku for simple tasks and Opus for complex ones. Reduce costs by up to 70%.

High Availability
Never Go Down

If Claude is unavailable, automatically fallback to GPT-4 or other providers. Keep your app running 24/7.

Pricing Comparison

Claude Model Pricing

Claude 3 Opus

ParrotRouter: $15/M input, $75/M output + 5% fee
Anthropic Direct: $15/M input, $75/M output

Claude 3 Sonnet

ParrotRouter: $3/M input, $15/M output + 5% fee
Anthropic Direct: $3/M input, $15/M output

Claude 3 Haiku

ParrotRouter: $0.25/M input, $1.25/M output + 5% fee
Anthropic Direct: $0.25/M input, $1.25/M output

Additional Models

ParrotRouter: 100+ models available
Anthropic Direct: Only Claude models

* While ParrotRouter adds 5%, the ability to mix models optimally often results in 30-70% cost savings.

Migration Guide

From Anthropic SDK to ParrotRouter

Switch to ParrotRouter and keep using Claude while gaining access to 100+ additional models:

Option 1: Use OpenAI SDK (Recommended)

// Install OpenAI SDK
npm install openai

// Configure for ParrotRouter
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.parrotrouter.com/v1',
  apiKey: process.env.PARROTROUTER_API_KEY,
});

// Use Claude through OpenAI SDK format
const completion = await client.chat.completions.create({
  model: 'anthropic/claude-3-opus',
  messages: [
    { role: 'user', content: 'Hello Claude!' }
  ],
  max_tokens: 1000,
});

Option 2: Direct API Migration

// Before - Anthropic SDK
import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const message = await anthropic.messages.create({
  model: 'claude-3-opus-20240229',
  messages: [{ role: 'user', content: 'Hello!' }],
  max_tokens: 1000,
});

// After - ParrotRouter with OpenAI SDK
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.parrotrouter.com/v1',
  apiKey: process.env.PARROTROUTER_API_KEY,
});

const message = await client.chat.completions.create({
  model: 'anthropic/claude-3-opus',
  messages: [{ role: 'user', content: 'Hello!' }],
  max_tokens: 1000,
});

Code Examples

Basic Usage Comparison

Anthropic Direct

import anthropic

client = anthropic.Anthropic(
    api_key="your-api-key"
)

message = client.messages.create(
    model="claude-3-opus-20240229",
    messages=[
        {
            "role": "user",
            "content": "Write a haiku about AI"
        }
    ],
    max_tokens=100
)

print(message.content)

ParrotRouter

import openai

client = openai.OpenAI(
    base_url="https://api.parrotrouter.com/v1",
    api_key="your-parrotrouter-key"
)

# Use Claude
response = client.chat.completions.create(
    model="anthropic/claude-3-opus",
    messages=[
        {
            "role": "user",
            "content": "Write a haiku about AI"
        }
    ],
    max_tokens=100
)

# Or instantly switch to GPT-4
response = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[...]  # Same format!
)

Advanced: Model Selection Strategy

// Smart model selection based on task requirements
async function intelligentCompletion(task, prompt) {
  const client = new OpenAI({
    baseURL: 'https://api.parrotrouter.com/v1',
    apiKey: process.env.PARROTROUTER_API_KEY,
  });

  // Choose the best model for each task
  const modelConfig = {
    'code_review': {
      model: 'anthropic/claude-3-opus',  // Claude excels at code analysis
      temperature: 0.2
    },
    'creative_writing': {
      model: 'anthropic/claude-3-sonnet',  // Good balance for creative tasks
      temperature: 0.8
    },
    'data_extraction': {
      model: 'openai/gpt-3.5-turbo',  // Fast and cheap for structured data
      temperature: 0
    },
    'complex_reasoning': {
      model: 'openai/gpt-4-turbo',  // Strong reasoning capabilities
      temperature: 0.3
    },
    'quick_summary': {
      model: 'anthropic/claude-3-haiku',  // Fast and efficient
      temperature: 0.3
    }
  };

  const config = modelConfig[task] || { model: 'anthropic/claude-3-sonnet', temperature: 0.5 };

  return await client.chat.completions.create({
    model: config.model,
    messages: [{ role: 'user', content: prompt }],
    temperature: config.temperature,
    max_tokens: 1000,
  });
}

// Example usage
const codeReview = await intelligentCompletion('code_review', 'Review this Python function...');
const story = await intelligentCompletion('creative_writing', 'Write a short story about...');

Streaming with Multiple Models

// Stream responses from different models
async function streamWithFallback(messages) {
  const client = new OpenAI({
    baseURL: 'https://api.parrotrouter.com/v1',
    apiKey: process.env.PARROTROUTER_API_KEY,
  });

  const models = [
    'anthropic/claude-3-opus',     // Primary choice
    'openai/gpt-4-turbo',          // Fallback 1
    'anthropic/claude-3-sonnet',   // Fallback 2
  ];

  for (const model of models) {
    try {
      const stream = await client.chat.completions.create({
        model,
        messages,
        stream: true,
      });

      console.log(`Streaming from ${model}...`);
      
      for await (const chunk of stream) {
        process.stdout.write(chunk.choices[0]?.delta?.content || '');
      }
      
      return; // Success, exit
    } catch (error) {
      console.error(`${model} failed, trying next...`);
    }
  }
  
  throw new Error('All models failed');
}

System Prompts Across Models

# ParrotRouter handles system prompts consistently across all models
import openai

client = openai.OpenAI(
    base_url="https://api.parrotrouter.com/v1",
    api_key="your-key"
)

system_prompt = """You are an expert software engineer. 
Provide concise, accurate technical advice."""

models = [
    "anthropic/claude-3-opus",
    "openai/gpt-4-turbo", 
    "google/gemini-pro"
]

# Same format works for all models
for model in models:
    response = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": "Explain microservices"}
        ],
        max_tokens=200
    )
    print(f"\n{model}: {response.choices[0].message.content[:100]}...")

When to Use Each

Use ParrotRouter When You:

Want to use Claude alongside other models
Need a unified API for all AI providers
Want automatic failovers for high availability
Need to optimize costs across different tasks
Prefer the OpenAI SDK format
Want to A/B test different models

Use Anthropic Direct When You:

Only need Claude models exclusively
Have enterprise agreements with Anthropic
Prefer Anthropic's native SDK format
Don't need other AI providers
Don't need automatic failovers
Don't plan to compare models

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