How to Use Claude Opus 4.5: Complete Developer Guide

Introduction

Claude Opus 4.5 is Anthropic's most powerful model for coding tasks. But raw power alone doesn't guarantee results - you need to know how to use it effectively.

This guide walks through everything you need to start building with Opus 4.5: from basic API setup to advanced techniques like leveraging the effort parameter and building long-running agents with context compaction.

Getting Started

Prerequisites

Before you begin, you'll need:

1. An Anthropic API key: Sign up at console.anthropic.com
2. Python 3.8+ or Node.js 18+ (depending on your preferred SDK)
3. Basic familiarity with REST APIs

Installation

Python:

pip install anthropic

Node.js/TypeScript:

npm install @anthropic-ai/sdk

Basic API Call

Python:

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-opus-4-5-20251101",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Write a Python function to merge two sorted arrays."
        }
    ]
)

print(message.content[0].text)

TypeScript:

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

const client = new Anthropic();

const message = await client.messages.create({
  model: 'claude-opus-4-5-20251101',
  max_tokens: 1024,
  messages: [
    {
      role: 'user',
      content: 'Write a Python function to merge two sorted arrays.'
    }
  ]
});

console.log(message.content[0].text);

Model ID Reference

| Model | ID | Best For | |-------|-----|----------| | Claude Opus 4.5 | claude-opus-4-5-20251101 | Complex coding, long-running tasks | | Sonnet 4.5 | claude-sonnet-4-5-20250929 | Fast iterations, cost efficiency | | Claude 3 Haiku | claude-3-haiku-20240307 | High-volume, simple tasks |

Understanding the Effort Parameter

The effort parameter is one of Opus 4.5's most important features. It lets you control the tradeoff between response speed and reasoning depth.

Effort Levels

| Level | Use Case | Token Usage | Response Time | |-------|----------|-------------|---------------| | Low | Simple tasks, quick answers | Minimal | Fast | | Medium | Everyday coding tasks | Moderate | Balanced | | High | Complex reasoning, difficult bugs | Maximum | Slower |

Using the Effort Parameter

import anthropic

client = anthropic.Anthropic()

# High effort for complex debugging
message = client.messages.create(
    model="claude-opus-4-5-20251101",
    max_tokens=4096,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000  # Higher budget for more reasoning
    },
    messages=[
        {
            "role": "user",
            "content": """
            Debug this race condition in our async Python code:

            [Your complex code here]

            The bug only appears under high load. Find the root cause.
            """
        }
    ]
)

Strategy: Escalating Effort

A cost-effective approach is to start with medium effort and escalate only when needed:

def smart_query(prompt, client):
    # Try medium effort first
    response = client.messages.create(
        model="claude-opus-4-5-20251101",
        max_tokens=2048,
        thinking={"type": "enabled", "budget_tokens": 5000},
        messages=[{"role": "user", "content": prompt}]
    )

    # Check if response needs more depth
    if needs_deeper_analysis(response):
        # Escalate to high effort
        response = client.messages.create(
            model="claude-opus-4-5-20251101",
            max_tokens=4096,
            thinking={"type": "enabled", "budget_tokens": 15000},
            messages=[{"role": "user", "content": prompt}]
        )

    return response

Context Compaction for Agents

Context compaction is what makes Opus 4.5 exceptional for long-running agentic tasks. It allows the model to maintain coherent understanding across extended sessions without context window bloat.

How Context Compaction Works

Traditional AI conversations accumulate tokens as the conversation grows. Eventually, you hit the context limit and must either truncate history or start fresh.

Context compaction intelligently summarizes and compresses previous context, allowing Opus 4.5 to:

• Work on tasks for 30+ minutes without degradation
• Handle multi-file refactoring across large codebases
• Maintain consistent understanding of project architecture

Building a Long-Running Agent

Here's a pattern for building agents that leverage context compaction:

import anthropic
from typing import List, Dict

class CodingAgent:
    def __init__(self):
        self.client = anthropic.Anthropic()
        self.conversation: List[Dict] = []
        self.system_prompt = """
        You are a senior software engineer working on a codebase.
        You can read files, write files, and run commands.
        Always explain your reasoning before taking action.
        """

    def add_context(self, role: str, content: str):
        self.conversation.append({"role": role, "content": content})

    def query(self, user_input: str) -> str:
        self.add_context("user", user_input)

        response = self.client.messages.create(
            model="claude-opus-4-5-20251101",
            max_tokens=4096,
            system=self.system_prompt,
            messages=self.conversation
        )

        assistant_response = response.content[0].text
        self.add_context("assistant", assistant_response)

        return assistant_response

    def run_task(self, task_description: str):
        """Run a multi-step task with context compaction."""
        print(f"Starting task: {task_description}")

        # Initial planning
        plan = self.query(f"""
        Task: {task_description}

        First, analyze the task and create a step-by-step plan.
        List the files you'll need to examine and modify.
        """)
        print(f"Plan:\n{plan}\n")

        # Execute steps (simplified - real implementation would parse and execute)
        while not self.is_task_complete():
            next_step = self.query("Execute the next step in your plan. Show your work.")
            print(f"Step result:\n{next_step}\n")

        return self.query("Summarize what was accomplished and any remaining issues.")

Best Practices for Agentic Tasks

1. Structured prompts: Give clear instructions with explicit steps
2. Progress checkpoints: Ask for summaries at key points
3. Error recovery: Include instructions for handling failures
4. Tool definitions: Clearly define what tools/actions are available

AGENTIC_SYSTEM_PROMPT = """
You are a coding agent with access to the following tools:

1. read_file(path) - Read a file's contents
2. write_file(path, content) - Write content to a file
3. run_command(cmd) - Run a shell command
4. search_codebase(query) - Search for code patterns

For each step:
1. State what you're trying to accomplish
2. Choose the appropriate tool
3. Execute and analyze the result
4. Decide if you need additional steps

If you encounter an error, explain it and propose a solution.
"""

Best Practices for Coding Tasks

Multi-File Refactoring

Opus 4.5 excels at coordinated changes across many files. Here's how to structure these requests:

refactoring_prompt = """
I need to rename the `UserService` class to `AccountService` across my codebase.

Current structure:
- src/services/UserService.ts (main class)
- src/controllers/UserController.ts (imports UserService)
- src/routes/user.routes.ts (uses UserController)
- tests/UserService.test.ts (tests)

Please:
1. List all files that need changes
2. For each file, show the exact changes needed
3. Flag any potential breaking changes
4. Suggest any additional updates (types, interfaces, etc.)
"""

Debugging Complex Issues

For difficult bugs, provide maximum context:

debugging_prompt = """
Bug: API returns 500 error intermittently under load

Environment:
- Node.js 20, Express 4.18
- PostgreSQL 15 with connection pooling
- Running in Kubernetes (3 pods)

Symptoms:
- Error: "Connection terminated unexpectedly"
- Only happens when >100 concurrent requests
- Database shows connections not being released

Relevant code:
[Include your connection handling code]

Error logs:
[Include relevant log snippets]

Please:
1. Identify possible root causes
2. Rank them by likelihood
3. Provide specific fixes for each
4. Suggest monitoring to prevent recurrence
"""

Code Review Workflows

Opus 4.5 can provide thorough code reviews:

code_review_prompt = """
Review this pull request for:
1. Correctness - Logic errors, edge cases
2. Security - OWASP top 10 vulnerabilities
3. Performance - N+1 queries, memory leaks
4. Maintainability - Code clarity, naming
5. Testing - Missing test cases

PR Description: Add user authentication with JWT

Files changed:
[Include diff or full file contents]

Be specific about issues and suggest exact fixes.
"""

Cost Optimization

Token Usage Tips

1. Use streaming for long responses: Reduces perceived latency
2. Batch similar requests: Combine related questions
3. Cache common patterns: Don't re-ask for boilerplate
4. Right-size effort: Use high effort only when needed

Monitoring Usage

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-5-20251101",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Your prompt"}]
)

# Track token usage
input_tokens = response.usage.input_tokens
output_tokens = response.usage.output_tokens

# Calculate cost (Opus 4.5 pricing)
cost = (input_tokens * 5 / 1_000_000) + (output_tokens * 25 / 1_000_000)
print(f"Request cost: ${cost:.4f}")

When to Use Sonnet Instead

Despite Opus 4.5's power, Sonnet 4.5 is often the better choice:

| Task | Recommended Model | |------|-------------------| | Quick code completion | Sonnet 4.5 | | Simple refactoring | Sonnet 4.5 | | Explaining code | Sonnet 4.5 | | Complex debugging | Opus 4.5 | | Multi-file changes | Opus 4.5 | | Architecture decisions | Opus 4.5 |

Integration Patterns

VS Code Extension

If using Claude in VS Code, configure the model in settings:

{
  "claude.model": "claude-opus-4-5-20251101",
  "claude.maxTokens": 4096
}

Claude Code CLI

For terminal-based workflows:

# Use Opus 4.5 explicitly
claude --model opus "Refactor this function for better performance"

# Enable high effort mode
claude --model opus --effort high "Debug this race condition"

CI/CD Integration

Add AI code review to your pipeline:

# .github/workflows/ai-review.yml
name: AI Code Review
on: [pull_request]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Claude Review
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
        run: |
          python scripts/ai_review.py --model claude-opus-4-5-20251101

Troubleshooting

Common Issues

Rate limits:

import time
from anthropic import RateLimitError

def query_with_retry(client, **kwargs):
    for attempt in range(3):
        try:
            return client.messages.create(**kwargs)
        except RateLimitError:
            time.sleep(2 ** attempt)
    raise Exception("Max retries exceeded")

Context too long:

• Use context compaction features
• Summarize previous conversation
• Focus prompts on specific files/issues

Inconsistent outputs:

• Lower temperature for deterministic tasks
• Provide more specific instructions
• Use examples in your prompt

Conclusion

Claude Opus 4.5 is a powerful tool for serious development work. The key to getting value from it is understanding when and how to use its unique features:

• Effort parameter: Match compute to task complexity
• Context compaction: Enable long-running agentic workflows
• Token efficiency: More capability with less cost

Start with the examples in this guide, then adapt them to your specific workflows. The combination of state-of-the-art coding performance and flexible configuration makes Opus 4.5 a worthy addition to any developer's toolkit.

Next steps:

• Read the Opus 4.5 release announcement
• gpt-5-1-codex-max-vs-gemini-3-pro">Compare Claude vs competitors
• Explore Claude Code