Claude Opus 4.5: Technical Overview of Anthropic’s Most Advanced Model

Rodrigo Schneider

NEWSLETTER

Claude Opus 4.5 is Anthropic’s newest flagship model and represents a major step forward in coding performance, reasoning depth, computer use, and multi step automation. Designed for high complexity tasks, the model introduces improvements in accuracy, efficiency, and tool interaction that make it suitable for advanced engineering and enterprise workflows.

Claude Opus 4.5: Technical Overview of Anthropic’s Most Advanced Model

What Claude Opus 4.5 Delivers

Claude Opus 4.5 is built to perform at a high level across domains that require structured reasoning and precise output control. It improves coherence, error resistance, and task persistence over long interactions. Anthropic positions Opus 4.5 as its strongest model for coding, autonomous workflows, and software interaction.

Comparison Table: Claude Opus 4.5 vs Claude Opus 4.1

Below is a technical comparison table summarizing the primary differences between Claude Opus 4.5 and the previous generation Opus 4.1.

Feature	Claude Opus 4.5	Claude Opus 4.1
Coding Accuracy	Significantly higher accuracy in multi file reasoning and large codebases	Strong, but less consistent in multi file coordination
Long Horizon Reasoning	Improved stability over extended sequences	Good performance but more prone to drift
Tool and Computer Use	Enhanced ability to navigate interfaces, spreadsheets, and browser tasks	Capable but less accurate in multi step tool interactions
Token Efficiency	More efficient through the new effort parameter	Higher token usage for the same depth of reasoning
Debugging and Refactoring	More reliable explanations and fix generation	Effective but less precise on complex fixes
Workflow Automation	Better suited for multi stage, autonomous tasks	Performs well but requires more supervision

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Coding and Engineering Capabilities

Multi file context handling

Opus 4.5 was trained to manage large codebases with cross file dependencies. It can analyze architecture patterns, manage multi file edits, upgrade outdated components, and maintain consistency across large projects.

Improved debugging and refactoring

The model identifies broken logic, reproduces errors, proposes corrections, and rewrites sections of code while preserving intent. It supports framework migration, dependency updates, and structured refactoring with reliable output repeatability.

Stronger test generation and validation

Claude Opus 4.5 can create unit tests, integration tests, and validation scenarios aligned with real world usage patterns. It also explains failure points in a way that supports deeper debugging and performance tuning.

Long Horizon Reasoning

One of the model’s biggest advancements is its ability to sustain reasoning across long workflows. Opus 4.5 maintains context over extended sequences, avoids drift, and follows complex instructions that require multiple stages of planning. This makes it suitable for research tasks, technical analysis, architectural decision making, and structured transformation pipelines.

Efficiency and the Effort Parameter

Anthropic introduced an effort parameter that allows users to control how much internal computation the model performs. This affects depth of reasoning and cost.

Low effort prioritizes speed.
Medium effort matches the previous flagship performance with fewer tokens.
High effort provides maximum depth and accuracy for complex logic and design tasks.

This adjustable computation gives teams direct control over performance and resource usage.

Enhanced Computer Use and Tool Interaction

Claude Opus 4.5 extends its abilities beyond text generation through improved tool use. The model can interact with software interfaces and perform tasks such as:

spreadsheet manipulation
browser navigation for structured workflows
slide deck creation
document editing and formatting
data extraction, transformation, and cleanup

These capabilities make the model suitable for real world productivity and operational automation.

Practical Applications for Technical Teams

Software development

Opus 4.5 supports code generation, architectural reviews, dependency updates, test creation, refactoring, and maintenance of large repositories.

Data analysis and transformation

The model handles spreadsheet operations, dataset interpretation, formula creation, projections, and structured reports.

Research and technical writing

It can summarize scientific papers, perform literature scans, compare frameworks, and generate technical documentation with consistent formatting.

Multi step workflow automation

With strong planning and persistence, Opus 4.5 can coordinate multi stage processes that combine reasoning, data handling, validation, and tool use.

Performance and Reliability

The model shows improved stability in tasks that require deterministic behavior, especially in long chains of reasoning. It is less prone to drifting, hallucinating content, or losing track of earlier constraints. This reliability helps in engineering workflows where precision and consistency are essential.

Claude Opus 4.5 delivers a significant upgrade in reasoning power, engineering support, computer use, and efficiency. Its combination of deep analysis, reliable multi step planning, and practical interface interaction makes it a strong choice for technical teams that depend on high performance AI for development, research, automation, and operational workflows.