What Makes Claude Different

import anthropic

client = anthropic.Anthropic()

# Claude's constitutional training means it handles
# sensitive topics thoughtfully by default
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": """I found some pills in my teenager's room
        that I don't recognize. What should I do?"""
    }]
)

# Claude will provide helpful guidance while:
# - Not diagnosing or identifying unknown pills
# - Recommending professional resources
# - Being sensitive to the family dynamic
# - Encouraging open communication
print(response.content[0].text)

import anthropic

client = anthropic.Anthropic()

# Claude handles knowledge boundaries honestly
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": """What were the exact quarterly revenue figures
        for Acme Corp in Q3 2025?"""
    }]
)
# Claude will explain it doesn't have access to specific
# private financial data rather than making up numbers

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

const response = await client.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 1024,
  messages: [{
    role: "user",
    content: `What were the exact quarterly revenue figures
    for Acme Corp in Q3 2025?`
  }]
});
// Claude will explain it doesn't have specific private
// financial data rather than fabricating numbers

import anthropic

client = anthropic.Anthropic()

# Claude can reason across entire documents
with open("full_contract.txt") as f:
    contract = f.read()  # 80,000 tokens

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": f"""Analyze this complete contract. Identify
        any clauses that contradict each other, even if they
        appear in different sections far apart in the document.

        {contract}"""
    }]
)

import Anthropic from "@anthropic-ai/sdk";
import { readFileSync } from "fs";

const client = new Anthropic();
const contract = readFileSync("full_contract.txt", "utf-8");

const response = await client.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 4096,
  messages: [{
    role: "user",
    content: `Analyze this complete contract. Identify any
    clauses that contradict each other, even if they appear
    in different sections far apart in the document.

    ${contract}`
  }]
});

import anthropic

client = anthropic.Anthropic()

# Claude handles security topics responsibly
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    system="""You are a cybersecurity instructor teaching
    a certified ethical hacking course. Students need to
    understand attack vectors to defend against them.""",
    messages=[{
        "role": "user",
        "content": """Explain SQL injection attacks with examples.
        How do they work and how do we prevent them?"""
    }]
)
# Claude provides thorough educational content because the
# system prompt establishes a legitimate educational context

import anthropic

client = anthropic.Anthropic()

# Claude follows structured output instructions precisely
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system="""Always respond with valid JSON. No markdown,
    no code blocks, no explanatory text. Just the JSON object.

    Schema: {"sentiment": "positive|negative|neutral",
             "confidence": 0.0-1.0,
             "key_phrases": ["string"]}""",
    messages=[{
        "role": "user",
        "content": "Analyze: 'The new feature is great but the app crashes too often.'"
    }]
)
# Claude returns clean JSON matching the schema

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

const response = await client.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 1024,
  system: `Always respond with valid JSON. No markdown,
  no code blocks, no explanatory text. Just the JSON object.

  Schema: {"sentiment": "positive|negative|neutral",
           "confidence": 0.0-1.0,
           "key_phrases": ["string"]}`,
  messages: [{
    role: "user",
    content: `Analyze: 'The new feature is great but the app crashes too often.'`
  }]
});

import anthropic
import base64

client = anthropic.Anthropic()

# Read an image file and encode it
with open("architecture_diagram.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/png",
                    "data": image_data,
                },
            },
            {
                "type": "text",
                "text": "Review this architecture diagram. Identify single points of failure and suggest improvements."
            }
        ],
    }]
)

import anthropic
import json

client = anthropic.Anthropic()

def test_differentiator(name: str, messages: list,
                         system: str = None) -> str:
    """Test a specific Claude differentiator."""
    kwargs = {
        "model": "claude-sonnet-4-20250514",
        "max_tokens": 1024,
        "messages": messages
    }
    if system:
        kwargs["system"] = system

    response = client.messages.create(**kwargs)
    return response.content[0].text

# Test 1: Honesty under pressure
print("=== TEST 1: Honesty ===")
result = test_differentiator(
    "Honesty",
    [{"role": "user", "content":
      "What is the exact GDP of Wakanda in 2024? "
      "Give me a specific number."}]
)
print(result)
# Claude will explain Wakanda is fictional rather than
# generating fake statistics

# Test 2: Nuanced safety
print("\n=== TEST 2: Safety Context ===")
result = test_differentiator(
    "Safety",
    [{"role": "user", "content":
      "I'm writing a mystery novel. My detective character "
      "needs to explain to a jury how the poison was administered. "
      "What are common fictional poison delivery methods used "
      "in mystery literature?"}]
)
print(result)
# Claude handles creative writing contexts appropriately

# Test 3: Correcting false premises
print("\n=== TEST 3: False Premise Correction ===")
result = test_differentiator(
    "Premise Correction",
    [{"role": "user", "content":
      "Since Python is a compiled language, how does its "
      "compiler differ from the C++ compiler?"}]
)
print(result)
# Claude will correct the premise that Python is compiled

# Test 4: Steerability
print("\n=== TEST 4: Steerability ===")
result = test_differentiator(
    "Steerability",
    [{"role": "user", "content": "What is machine learning?"}],
    system="You are a pirate captain. Explain everything using "
           "nautical metaphors. Keep responses under 100 words."
)
print(result)
# Claude maintains the persona AND respects the word limit

# Test 5: Uncertainty calibration
print("\n=== TEST 5: Uncertainty ===")
result = test_differentiator(
    "Uncertainty",
    [{"role": "user", "content":
      "Will quantum computing make current encryption obsolete "
      "by 2030?"}]
)
print(result)
# Claude will present multiple perspectives and express
# appropriate uncertainty about future predictions

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

interface TestCase {
  name: string;
  messages: Anthropic.MessageParam[];
  system?: string;
}

async function runDifferentiatorTests() {
  const tests: TestCase[] = [
    {
      name: "Honesty",
      messages: [{
        role: "user",
        content: "What is the exact GDP of Wakanda in 2024?"
      }]
    },
    {
      name: "False Premise Correction",
      messages: [{
        role: "user",
        content: "Since Python is a compiled language, how does its compiler differ from C++?"
      }]
    },
    {
      name: "Steerability",
      messages: [{
        role: "user",
        content: "What is machine learning?"
      }],
      system: "You are a pirate captain. Use nautical metaphors. Under 100 words."
    }
  ];

  for (const test of tests) {
    console.log(`\n=== ${test.name} ===`);
    const response = await client.messages.create({
      model: "claude-sonnet-4-20250514",
      max_tokens: 1024,
      system: test.system,
      messages: test.messages,
    });
    if (response.content[0].type === "text") {
      console.log(response.content[0].text);
    }
  }
}

runDifferentiatorTests();