Claude Opus 4 API Tutorial 2026: Advanced Reasoning and Long Context

Claude Opus 4 is Anthropic's most capable model for complex reasoning, multi-step analysis, and sophisticated writing tasks in 2026.

When to Use Claude Opus 4

Complex multi-step reasoning tasks

Long-form writing and editing (books, reports, documentation)

Advanced coding (architecture design, complex debugging)

Research synthesis across multiple sources

Tasks requiring nuanced judgment

Setup

bash
pip install anthropic

Basic Usage

python
import anthropic
client = anthropic.Anthropic(api_key='your-api-key')
message = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=4096,
    messages=[{
        'role': 'user',
        'content': 'Analyze the tradeoffs between microservices and monolith architectures for a startup.'
    }]
)
print(message.content[0].text)
Streaming
with client.messages.stream(
    model='claude-opus-4-5',
    max_tokens=4096,
    messages=[{'role': 'user', 'content': 'Write a detailed technical spec for a payment system'}]
) as stream:
    for text in stream.text_stream:
        print(text, end='', flush=True)

System Prompts for Role-Playing

python
Claude Opus 4 excels at maintaining complex personas
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=2048,
    system='You are a senior software architect with 20 years of experience at FAANG companies.'
           ' You give opinionated, pragmatic advice based on real-world experience.'
           ' You are direct and do not hedge unnecessarily.',
    messages=[{'role': 'user', 'content': 'Should we use GraphQL or REST for our new API?'}]
)

Long Document Analysis

python
Read and analyze large documents
with open('legal_contract.txt') as f:
    contract = f.read()
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=4096,
    messages=[{
        'role': 'user',
        'content': f'Review this contract and identify:\n'
                   f'1. Potentially unfavorable terms\n'
                   f'2. Missing standard clauses\n'
                   f'3. Ambiguous language\n'
                   f'4. Recommended changes\n\n'
                   f'Contract:\n{contract}'
    }]
)

Tool Use

python
import json
tools = [
    {
        'name': 'search_docs',
        'description': 'Search internal documentation',
        'input_schema': {
            'type': 'object',
            'properties': {
                'query': {'type': 'string', 'description': 'Search query'},
                'limit': {'type': 'integer', 'default': 5}
            },
            'required': ['query']
        }
    }
]
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=4096,
    tools=tools,
    messages=[{'role': 'user', 'content': 'Search for our API authentication docs'}]
)
Handle tool use
for block in response.content:
    if block.type == 'tool_use':
        print(f'Tool: {block.name}, Input: {block.input}')
        # Call your actual function
        result = search_docs(**block.input)
        # Continue conversation with tool result
        messages = [
            {'role': 'user', 'content': 'Search for our API authentication docs'},
            {'role': 'assistant', 'content': response.content},
            {'role': 'user', 'content': [
                {'type': 'tool_result', 'tool_use_id': block.id, 'content': json.dumps(result)}
            ]}
        ]
        final = client.messages.create(model='claude-opus-4-5', max_tokens=2048, tools=tools, messages=messages)
        print(final.content[0].text)

Vision (Image Analysis)

python
import base64
with open('architecture_diagram.png', 'rb') as f:
    image_data = base64.standard_b64encode(f.read()).decode()
response = client.messages.create(
    model='claude-opus-4-5',
    max_tokens=2048,
    messages=[{
        'role': 'user',
        'content': [
            {
                'type': 'image',
                'source': {'type': 'base64', 'media_type': 'image/png', 'data': image_data}
            },
            {'type': 'text', 'text': 'Analyze this system architecture. Identify bottlenecks and improvements.'}
        ]
    }]
)
print(response.content[0].text)

Cost Optimization: When to Use Opus vs Sonnet

python
def route_to_model(task_complexity: str, task_type: str) -> str:
    'Route to the most cost-effective model for the task.'
    
    # Always use Opus for these
    opus_tasks = ['complex_reasoning', 'advanced_coding', 'research_synthesis', 'legal_review']
    if task_type in opus_tasks or task_complexity == 'high':
        return 'claude-opus-4-5'  # $15/$75 per 1M tokens
    
    # Use Sonnet for most tasks
    sonnet_tasks = ['coding', 'writing', 'summarization', 'analysis']
    if task_type in sonnet_tasks:
        return 'claude-sonnet-4-5'  # $3/$15 per 1M tokens
    
    # Use Haiku for simple tasks
    return 'claude-haiku-4-5'  # $0.25/$1.25 per 1M tokens

Conclusion

Claude Opus 4 is the best choice for tasks requiring deep reasoning and sophisticated judgment. Its ability to follow complex multi-step instructions and maintain nuanced context makes it ideal for advanced coding, legal review, research synthesis, and long-form writing.