Claude Opus 4.5 Integration Guide (2026)

Claude Opus 4.5 Integration Guide (2026)

Claude Opus 4.5 is Anthropic's most capable model for complex reasoning, nuanced writing, and advanced code generation. While Sonnet 4 handles most tasks efficiently, Opus 4.5 excels at tasks requiring deep analysis, multi-step reasoning, and creative synthesis.

This guide covers how to integrate Opus 4.5 into your applications using the Anthropic API, with practical code examples in Python, TypeScript, and cURL.

Prerequisites

Before you start, you need:

1. An Anthropic API account at console.anthropic.com
2. An API key (Settings > API Keys > Create Key)
3. A funded account or active billing (Opus 4.5 is not available on free credits for some tiers)

Pricing Overview

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context Window
Opus 4.5	$15.00	$75.00	200K tokens
Opus 4	$15.00	$75.00	200K tokens
Sonnet 4	$3.00	$15.00	200K tokens
Haiku 3.5	$0.80	$4.00	200K tokens

Opus 4.5 is 5x more expensive than Sonnet 4 per token. Use it selectively for tasks where quality justifies the cost.

Basic Integration

Python

Install the official SDK:

pip install anthropic

Basic usage:

import anthropic

client = anthropic.Anthropic(api_key="sk-ant-your-key-here")

message = client.messages.create(
    model="claude-opus-4-5-20250520",
    max_tokens=4096,
    messages=[
        {
            "role": "user",
            "content": "Analyze the trade-offs between microservices and monolithic architecture for a team of 5 developers building a B2B SaaS product."
        }
    ]
)

print(message.content[0].text)
print(f"Tokens used: {message.usage.input_tokens} in, {message.usage.output_tokens} out")

TypeScript / Node.js

npm install @anthropic-ai/sdk

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

const client = new Anthropic({ apiKey: "sk-ant-your-key-here" });

async function main() {
  const message = await client.messages.create({
    model: "claude-opus-4-5-20250520",
    max_tokens: 4096,
    messages: [
      {
        role: "user",
        content: "Design a database schema for a multi-tenant project management tool with RBAC.",
      },
    ],
  });

  if (message.content[0].type === "text") {
    console.log(message.content[0].text);
  }

  console.log(`Tokens: ${message.usage.input_tokens} in, ${message.usage.output_tokens} out`);
}

main();

cURL

curl https://api.anthropic.com/v1/messages \
  -H "content-type: application/json" \
  -H "x-api-key: sk-ant-your-key-here" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-5-20250520",
    "max_tokens": 4096,
    "messages": [
      {"role": "user", "content": "Explain quantum computing to a software engineer"}
    ]
  }'

Streaming Responses

For better user experience, stream responses token-by-token:

Python Streaming

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-5-20250520",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "Write a comprehensive analysis of WebAssembly adoption in 2026"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

print()  # New line after stream completes

TypeScript Streaming

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

const client = new Anthropic();

async function streamResponse() {
  const stream = client.messages.stream({
    model: "claude-opus-4-5-20250520",
    max_tokens: 4096,
    messages: [
      { role: "user", content: "Write a technical RFC for implementing event sourcing" },
    ],
  });

  for await (const event of stream) {
    if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
      process.stdout.write(event.delta.text);
    }
  }

  const finalMessage = await stream.finalMessage();
  console.log(`\nTotal tokens: ${finalMessage.usage.input_tokens + finalMessage.usage.output_tokens}`);
}

streamResponse();

System Prompts and Multi-Turn Conversations

System Prompt

Use system prompts to set Opus 4.5's behavior and expertise:

message = client.messages.create(
    model="claude-opus-4-5-20250520",
    max_tokens=4096,
    system="You are a senior staff engineer at a FAANG company. You provide technically rigorous advice with concrete code examples. You consider scalability, maintainability, and team velocity in every recommendation. Be direct and opinionated.",
    messages=[
        {"role": "user", "content": "How should I design the caching layer for our API that serves 50K RPM?"}
    ]
)

Multi-Turn Conversation

messages = []

def chat(user_message: str) -> str:
    messages.append({"role": "user", "content": user_message})

    response = client.messages.create(
        model="claude-opus-4-5-20250520",
        max_tokens=4096,
        system="You are a code architecture advisor.",
        messages=messages
    )

    assistant_message = response.content[0].text
    messages.append({"role": "assistant", "content": assistant_message})

    return assistant_message

# Multi-turn usage
print(chat("I'm building a real-time collaboration tool like Figma. Where do I start?"))
print(chat("What about conflict resolution? CRDTs vs OT?"))
print(chat("Show me a basic CRDT implementation in TypeScript."))

Advanced Features

Prompt Caching (Cost Optimization)

For repeated calls with the same system prompt, use prompt caching to reduce costs by up to 90%:

message = client.messages.create(
    model="claude-opus-4-5-20250520",
    max_tokens=4096,
    system=[
        {
            "type": "text",
            "text": """You are an expert code reviewer. Review code for:
            1. Security vulnerabilities (OWASP Top 10)
            2. Performance issues (N+1 queries, memory leaks)
            3. Type safety and error handling
            4. Architecture violations
            5. Test coverage gaps
            Provide severity ratings: critical, warning, info.""",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "Review this code:\n```python\n# ... code here\n```"}
    ]
)

# Check cache performance
print(f"Cache creation tokens: {message.usage.cache_creation_input_tokens}")
print(f"Cache read tokens: {message.usage.cache_read_input_tokens}")

Tool Use (Function Calling)

Opus 4.5 excels at using tools for structured output and actions:

message = client.messages.create(
    model="claude-opus-4-5-20250520",
    max_tokens=4096,
    tools=[
        {
            "name": "create_jira_ticket",
            "description": "Creates a Jira ticket for a bug or feature request",
            "input_schema": {
                "type": "object",
                "properties": {
                    "title": {"type": "string", "description": "Ticket title"},
                    "description": {"type": "string", "description": "Detailed description"},
                    "priority": {"type": "string", "enum": ["critical", "high", "medium", "low"]},
                    "type": {"type": "string", "enum": ["bug", "feature", "task"]},
                    "labels": {"type": "array", "items": {"type": "string"}}
                },
                "required": ["title", "description", "priority", "type"]
            }
        },
        {
            "name": "query_database",
            "description": "Executes a read-only SQL query against the application database",
            "input_schema": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "SQL SELECT query"}
                },
                "required": ["query"]
            }
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "Users are reporting 500 errors on the /api/orders endpoint. Check the recent order records and create a bug ticket."
        }
    ]
)

# Process tool calls
for content_block in message.content:
    if content_block.type == "tool_use":
        print(f"Tool: {content_block.name}")
        print(f"Input: {content_block.input}")

Vision (Image Analysis)

Opus 4.5 can analyze images, making it useful for design review and screenshot-based debugging:

import base64

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

message = client.messages.create(
    model="claude-opus-4-5-20250520",
    max_tokens=4096,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "Review this UI screenshot for accessibility issues, visual hierarchy problems, and responsive design concerns."
                }
            ]
        }
    ]
)

Batch API (50% Cost Savings)

For non-urgent tasks, the Batches API processes requests at half price:

batch = client.batches.create(
    requests=[
        {
            "custom_id": f"review-{i}",
            "params": {
                "model": "claude-opus-4-5-20250520",
                "max_tokens": 4096,
                "messages": [{"role": "user", "content": f"Review this code: {code}"}]
            }
        }
        for i, code in enumerate(code_files)
    ]
)

# Check batch status
status = client.batches.retrieve(batch.id)
print(f"Status: {status.processing_status}")

When to Use Opus 4.5 vs. Sonnet 4

Task	Recommended Model	Reason
Complex architecture design	Opus 4.5	Better at multi-factor reasoning
Code review (security audit)	Opus 4.5	Catches subtle vulnerabilities
Standard code generation	Sonnet 4	Fast, accurate, 5x cheaper
API responses in production	Sonnet 4 or Haiku	Latency and cost matter
Research synthesis	Opus 4.5	Better at connecting disparate ideas
Documentation writing	Sonnet 4	More than adequate quality
Data extraction / parsing	Haiku	Fast and cheapest option

Smart Routing Pattern

Route requests to the optimal model based on complexity:

def route_to_model(task_type: str, complexity: str) -> str:
    if complexity == "high" or task_type in ["architecture", "security_audit", "research"]:
        return "claude-opus-4-5-20250520"
    elif complexity == "medium" or task_type in ["code_generation", "review", "writing"]:
        return "claude-sonnet-4-20250514"
    else:
        return "claude-haiku-3-5-20241022"

# Usage
model = route_to_model(task_type="code_generation", complexity="medium")
message = client.messages.create(model=model, max_tokens=4096, messages=[...])

Error Handling and Retry Logic

Production integrations need robust error handling:

import anthropic
import time

def call_opus(messages: list, max_retries: int = 3) -> str:
    for attempt in range(max_retries):
        try:
            response = client.messages.create(
                model="claude-opus-4-5-20250520",
                max_tokens=4096,
                messages=messages
            )
            return response.content[0].text

        except anthropic.RateLimitError:
            wait_time = 2 ** attempt * 10  # Exponential backoff
            print(f"Rate limited. Waiting {wait_time}s...")
            time.sleep(wait_time)

        except anthropic.APIStatusError as e:
            if e.status_code >= 500:
                # Server error, retry
                time.sleep(2 ** attempt)
                continue
            raise  # Client error, don't retry

        except anthropic.APIConnectionError:
            time.sleep(2 ** attempt)
            continue

    raise Exception("Max retries exceeded")

Cost Estimation

Estimate your monthly Opus 4.5 costs before committing:

Usage Pattern	Daily Messages	Avg Tokens/Message	Monthly Cost
Light	20	2,000 in / 1,000 out	~$55
Moderate	100	3,000 in / 2,000 out	~$450
Heavy	500	5,000 in / 3,000 out	~$3,500
Production API	10,000	2,000 in / 500 out	~$20,000

Cost optimization checklist:

1. Use prompt caching for repeated system prompts (saves up to 90%)
2. Route simple tasks to Sonnet 4 or Haiku (saves 70-95%)
3. Use the Batch API for non-urgent requests (saves 50%)
4. Set max_tokens conservatively to avoid over-generation
5. Implement response caching for identical queries

Conclusion

Claude Opus 4.5 is the right choice when you need the highest quality reasoning, analysis, or creative output. For most production applications, a smart routing strategy that uses Opus 4.5 for complex tasks and Sonnet 4 for everything else will give you the best balance of quality and cost.

Start with the basic integration, add streaming for user-facing applications, and implement prompt caching as soon as you have a stable system prompt.

If your application needs AI media generation in addition to language models -- like generating images, videos, or talking avatars -- Hypereal AI provides a complementary API. Pair Claude Opus 4.5 for reasoning and Hypereal for visual content generation.