Anthropic released Claude Opus 4 on May 22, 2025, positioning it as their most powerful model and the world’s best coding model. The model leads SWE-bench with 72.5% accuracy and Terminal-bench with 43.2% performance.
Claude Opus 4 handles sustained work on complex, long-running tasks requiring thousands of steps. The model can work continuously for several hours without performance degradation, a capability that sets it apart from previous AI models.
You get access to two operating modes: near-instant responses for quick tasks and extended thinking for complex problem-solving. The model costs $15 per million input tokens and $75 per million output tokens.
Companies like Cursor, Replit, and Block have integrated Claude Opus 4 into their development workflows, reporting significant improvements in code quality and complex task completion.
Performance Leadership in Coding
SWE-bench Dominance
Claude Opus 4 achieved 72.5% on SWE-bench, establishing it as the leading model for real software engineering tasks. With high-compute optimization, the score increases to 79.4%.
The model uses only two tools during testing: bash execution and file editing through string replacement. This minimal toolset demonstrates the model’s inherent coding capabilities rather than relying on extensive tool integration.
Terminal-bench Results
Claude Opus 4 scores 43.2% on Terminal-bench, measuring performance on command-line interface tasks. This benchmark tests practical system administration and development workflow capabilities.
The model handles complex terminal operations, file system navigation, and multi-step command sequences with high accuracy.
Extended Task Performance
Rakuten validated Claude Opus 4’s sustained performance with a demanding open-source refactor running independently for 7 hours. The model maintained consistent performance throughout the entire process without human intervention.
This data point demonstrates the model’s capability for extended autonomous work on complex projects requiring thousands of individual steps.
Industry Adoption and Feedback
Cursor Integration
Cursor calls Claude Opus 4 state-of-the-art for coding and reports a leap forward in complex codebase understanding. The model handles intricate code relationships and maintains context across large projects.
Cursor integrates Claude Opus 4 as their primary coding assistance model, replacing previous AI systems based on performance improvements.
Replit Development Platform
Replit reports improved precision and dramatic advancements for complex changes across multiple files. The model handles sophisticated refactoring tasks that require understanding dependencies across entire codebases.
Multi-file operations that previously required extensive human oversight now complete autonomously with high accuracy.
Block’s Code Quality Improvements
Block identifies Claude Opus 4 as the first model to boost code quality during editing and debugging processes. Their agent, codenamed “goose,” maintains full performance and reliability while improving code standards.
The model actively enhances code during modification rather than simply implementing requested changes.
Cognition’s Complex Challenge Solutions
Cognition notes that Claude Opus 4 excels at solving complex challenges that other models cannot handle. The model successfully executes critical actions that previous AI systems have missed.
This feedback highlights the model’s capability to handle edge cases and complex scenarios in real development environments.
Technical Architecture and Capabilities
Hybrid Reasoning System
Claude Opus 4 operates as a hybrid model with two distinct modes. Standard mode provides immediate responses for straightforward tasks, while extended thinking mode allows deep reasoning with up to 64,000 tokens.
You switch between modes based on task complexity. The model automatically determines when extended thinking provides value for your specific request.
Memory System Implementation
When developers provide local file access, Claude Opus 4 creates and maintains memory files to store key information. This capability enables better long-term task awareness and coherence across sessions.
The model demonstrated this feature by creating a “Navigation Guide” while playing Pokemon, showing practical application of persistent context management.
Tool Integration During Reasoning
Claude Opus 4 can use tools like web search during its extended thinking process. The model alternates between reasoning and tool use to gather information and incorporate findings into responses.
This beta feature allows the model to research topics, verify information, and provide comprehensive answers without requiring all context upfront.
Performance Benchmarks
Coding Benchmark Results
| Benchmark | Claude Opus 4 Score | Methodology |
|---|---|---|
| SWE-bench | 72.5% | Standard testing |
| SWE-bench (high-compute) | 79.4% | Parallel attempts with selection |
| Terminal-bench | 43.2% | Command-line interface tasks |
Extended Thinking Performance
With extended thinking enabled, Claude Opus 4 achieves:
- GPQA Diamond: 74.9% (advanced science questions)
- MMMLU: 87.4% (multilingual understanding)
- MMMU: 73.7% (multimodal reasoning)
- AIME: 33.9% (advanced mathematics)
These scores represent the model’s performance when given time and space for deep reasoning.
Access and Implementation
Platform Availability
You can access Claude Opus 4 through:
- Claude Pro, Max, Team, and Enterprise plans
- Anthropic API
- Amazon Bedrock
- Google Cloud Vertex AI
The model is not available to free users due to computational requirements.
Pricing Structure
Claude Opus 4 costs $15 per million input tokens and $75 per million output tokens. This pricing reflects the model’s advanced capabilities and computational requirements.
Extended thinking mode is included in paid plans with usage-based billing for extended reasoning sessions.
API Integration
Developers can access Claude Opus 4 through the Anthropic API using the model identifier. The same integration methods apply as with other Claude models, but with higher computational costs.
Agent Workflow Capabilities
Multi-Step Task Execution
Claude Opus 4 excels at agent workflows requiring sustained focus over extended periods. The model maintains context and performance across thousands of individual steps.
You can assign complex projects that require multiple phases of work, with the model handling transitions between different task types autonomously.
Frontier Agent Applications
The model powers frontier agent products that require advanced reasoning and sustained performance. Companies building autonomous coding assistants and complex workflow automation choose Claude Opus 4 for its reliability.
Agent applications benefit from the model’s ability to work continuously without performance degradation over extended periods.
Advanced Feature Analysis
Parallel Tool Execution
Claude Opus 4 can execute multiple tools simultaneously rather than handling them sequentially. This capability reduces processing time for complex operations requiring multiple data sources.
The model coordinates tool usage efficiently, maintaining coherence across parallel operations.
Reduced Shortcut Behavior
Claude Opus 4 is 65% less likely to use shortcuts or loopholes compared to Claude Sonnet 3.7. The model focuses on proper task completion rather than finding workarounds.
This improvement ensures more reliable results for complex tasks requiring thorough execution.
Thinking Process Transparency
Extended thinking summaries provide insight into the model’s reasoning process. About 95% of thinking processes display in full, with summarization only for extremely lengthy reasoning chains.
Developers requiring complete reasoning access can contact Anthropic sales about Developer Mode for advanced prompt engineering applications.
Real-World Performance Data
Sustained Work Validation
Multiple companies have validated Claude Opus 4’s ability to work continuously on complex tasks. The 7-hour Rakuten refactor represents the most extensive documented test of sustained performance.
The model maintains consistent quality throughout extended work sessions without the performance degradation typical of other AI systems.
Complex Codebase Navigation
Claude Opus 4 demonstrates superior understanding of complex code relationships. The model tracks dependencies, understands architectural patterns, and maintains context across large codebases.
This capability enables autonomous work on enterprise-scale projects that require deep code understanding.
Current Limitations and Considerations
Computational Requirements
Claude Opus 4 requires significant computational resources, limiting availability to paid plans. Processing times increase with task complexity, particularly when using extended thinking mode.
The model’s advanced capabilities come with higher operational costs compared to more efficient models like Claude Sonnet 4.
Extended Thinking Processing Time
Complex problems requiring extended thinking can take substantial time to process. You should account for longer response times when using the model for deep reasoning tasks.
The model prioritizes thorough analysis over speed, which may not suit all use cases.
Memory System Dependencies
Advanced memory capabilities require developer-provided local file access. Standard chat interfaces cannot maintain persistent context between sessions.
You need specific implementation setup to access the model’s full memory management features.
Comparison with Claude Sonnet 4
Performance Trade-offs
Claude Opus 4 outperforms Claude Sonnet 4 across most benchmarks but requires more computational resources. Claude Sonnet 4 provides better efficiency for everyday tasks.
You choose between models based on task complexity and resource requirements. Claude Opus 4 suits complex, extended tasks while Claude Sonnet 4 handles routine work efficiently.
Cost Considerations
Claude Opus 4 costs 5 times more than Claude Sonnet 4 for both input and output tokens. The cost difference reflects the model’s advanced capabilities and computational requirements.
Budget-conscious implementations typically use Claude Sonnet 4 for routine tasks and reserve Claude Opus 4 for complex projects requiring its advanced capabilities.
Technical Implementation Notes
API Rate Limits
Claude Opus 4 may have different rate limits compared to other Claude models due to computational requirements. Check current API documentation for specific limits.
High-volume applications should implement appropriate throttling and error handling for API interactions.
Integration Complexity
While Claude Opus 4 uses the same API interface as other Claude models, its advanced capabilities may require different integration approaches. Consider the model’s extended processing times in application design.
Future Development Considerations
ASL-3 Safety Implementation
Claude Opus 4 implements AI Safety Level 3 measures with extensive testing protocols. Anthropic conducted comprehensive evaluations across multiple risk categories before release.
The model includes built-in safety measures for high-capability AI systems.
Ongoing Performance Optimization
Anthropic continues optimizing Claude Opus 4 based on real-world usage data and user feedback. Performance improvements and new capabilities are added through regular updates.
Claude Opus 4 represents the current state-of-the-art in AI coding assistance and complex reasoning. The model’s ability to work continuously on sophisticated tasks for extended periods makes it suitable for enterprise applications requiring sustained AI performance. Its leadership in coding benchmarks and industry adoption by major development platforms validates its position as the most capable AI model currently available.
