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Anthropic: Claude Opus 4.5Frontier reasoning. Autonomous agents.

Try Anthropic: Claude Opus 4.5API Documentation

Think First. Execute Better.

Extended Thinking

Hybrid Reasoning Mode

Toggle between fast execution and deep reasoning for complex logical challenges.

Autonomous Operation

Self-Improving Agents

Spawn subagents, manage context, and refine capabilities autonomously across iterations.

Token Efficiency

Effort Control Parameter

Match top performance with 76% fewer tokens at medium effort settings.

Examples

See what Anthropic: Claude Opus 4.5 can create

Copy any prompt below and try it yourself in the playground.

Data Pipeline

“Build a Python data processing pipeline that reads CSV files, performs statistical analysis, and generates visualizations. Include error handling and logging.”

API Integration

“Create a REST API client that connects to multiple third-party services, handles authentication, rate limiting, and caches responses efficiently.”

System Architecture

“Design a scalable microservices architecture for an e-commerce platform with load balancing, database sharding, and message queuing.”

Workflow Automation

“Develop an autonomous workflow that monitors email, extracts structured data, updates spreadsheets, and sends notifications based on conditions.”

For Developers

A few lines of code.
Complex tasks. Fewer iterations.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Anthropic: Claude Opus 4.5

Read the docs

Claude Opus 4.5 excels at coding, agentic tasks, and autonomous workflows with hybrid reasoning that toggles between fast and deep thinking. It demonstrates self-improving capabilities, learning from experience and refining its own performance across iterations.

The effort parameter controls the trade-off between response quality and token usage. At medium effort, Opus 4.5 matches top performance while using 76% fewer tokens; at high effort, it exceeds competitors by 4.3% while still using 48% fewer tokens.

Yes. Opus 4.5 supports sustained reasoning through 30-minute autonomous coding sessions with context management, memory tools, and context compaction to handle extended operations without intervention.

Claude Opus 4.5 supports a 200,000-token context window, allowing it to process roughly 150,000 words or several hundred pages in a single interaction, with up to 64,000 tokens per response.

Opus 4.5 offers improved pricing with the effort parameter enabling cost optimization. At medium effort, you get top-tier performance with significantly reduced token consumption compared to other frontier models.

Yes. Opus 4.5 excels at managing teams of subagents, enabling construction of complex, well-coordinated multi-agent systems with improved performance on deep research tasks.

Ready to create?

Start generating with Anthropic: Claude Opus 4.5 on ModelsLab.

Try Anthropic: Claude Opus 4.5API Documentation