Claude 4: Advancing Multi-Step Reasoning and AI Innovation | Anthropic AI

In a significant advancement for artificial intelligence, Anthropic has unveiled its next generation of AI models: Claude Opus 4 and Claude Sonnet 4. Released on May 22, 2025, these models represent a substantial leap forward in AI capabilities, particularly in the realm of multi-step reasoning, coding proficiency, and agentic behaviors. The Claude 4 family establishes new benchmarks across multiple domains, outperforming previous models and competing offerings in several critical areas.

Introduction to Claude 4 Models

Anthropic's Claude 4 release introduces two distinct models designed to address different use cases while sharing core architectural improvements:

Claude Opus 4 stands as Anthropic's most powerful model to date and claims the title of the world's best coding model. It excels in sustained performance on complex, long-running tasks that require focused effort across thousands of steps. With the ability to work continuously for several hours, Opus 4 dramatically outperforms all previous Sonnet models and significantly expands the horizons of what AI agents can accomplish.

Claude Sonnet 4 represents a significant upgrade to the previous Sonnet 3.7, delivering superior coding and reasoning capabilities while responding more precisely to user instructions. While not matching Opus 4's capabilities in most domains, Sonnet 4 strikes an optimal balance between performance and efficiency, making it ideal for everyday use cases.

Both models operate as hybrid systems offering two distinct modes of operation:

1. Near-instant responses for routine queries
2. Extended thinking capabilities for deeper reasoning on complex problems

This dual-mode functionality allows the models to adapt to different task requirements, providing quick answers when appropriate while reserving deeper computational resources for problems that demand more thorough analysis and multi-step reasoning.

The release of Claude 4 models comes at a pivotal moment in AI development, as organizations increasingly seek systems capable of handling complex workflows, understanding nuanced instructions, and maintaining coherence across extended interactions. With these new models, Anthropic has addressed several key limitations of previous AI systems, particularly in areas requiring sustained reasoning, precise instruction following, and complex problem-solving across multiple domains.

Technical Capabilities and Benchmarks

Claude 4 models demonstrate exceptional performance across a wide range of technical benchmarks, establishing new standards in several key domains. The most notable achievements are in software engineering, coding, and complex reasoning tasks.

Software Engineering Excellence

Claude Opus 4 and Sonnet 4 lead the industry on SWE-bench Verified, a rigorous benchmark for performance on real software engineering tasks. Opus 4 achieves an impressive 72.5% accuracy (79.4% with parallel test-time compute), while Sonnet 4 follows closely at 72.7% (80.2% with parallel test-time compute). These scores significantly outpace previous models like Sonnet 3.7 (62.3%/70.3%) and competing offerings from OpenAI and Google.

The models' performance on Terminal-bench further demonstrates their coding capabilities, with Opus 4 achieving 43.2%/50.0% and Sonnet 4 reaching 35.5%/41.3%. This benchmark evaluates the models' ability to navigate and execute commands in terminal environments, a critical skill for software development and system administration tasks.

Advanced Reasoning Capabilities

Beyond coding, Claude 4 models excel in graduate-level reasoning tasks. On the GPQA Diamond benchmark, which evaluates complex problem-solving abilities, Opus 4 scores 79.6%/83.3%, while Sonnet 4 achieves 75.4%/83.8%. These results demonstrate the models' ability to handle sophisticated reasoning challenges that require deep domain knowledge and multi-step logical processes.

In high school math competitions (AIME 2025), Opus 4 reaches 75.5%/90.0% accuracy, showcasing its mathematical reasoning abilities. This performance illustrates the model's capacity to work through complex mathematical problems that require multiple steps of calculation, theorem application, and creative problem-solving approaches.

Agentic Tool Use

The Claude 4 models demonstrate significant advancements in agentic tool use, a critical capability for AI systems that need to interact with external resources and services. On TAU-bench, which measures effective tool utilization, Opus 4 achieves 81.4% accuracy on retail tasks and 59.6% on airline tasks, while Sonnet 4 scores 80.5% and 60.0% respectively.

This capability enables the models to effectively leverage external tools during problem-solving, significantly expanding their functional range beyond what's possible with a standalone language model. The ability to alternate between reasoning and tool use represents a major step forward in creating AI systems that can operate more autonomously in complex environments.

Multilingual and Visual Capabilities

Claude 4 models maintain strong performance across linguistic boundaries, with Opus 4 achieving 88.8% accuracy on multilingual Q&A tasks (MMMLU). This demonstrates the models' ability to understand and generate content across multiple languages with high fidelity.

In visual reasoning (MMMU validation), Opus 4 scores 76.5%, showcasing its ability to interpret and reason about visual information. While not the highest score in this category (OpenAI o3 leads at 82.9%), the performance remains competitive and enables a wide range of multimodal applications.

New Features Released Today

Alongside the Claude 4 models, Anthropic has announced several groundbreaking features and capabilities that significantly enhance the models' utility and expand their potential applications.

Extended Thinking with Tool Use (Beta)

One of the most significant innovations in the Claude 4 release is the introduction of extended thinking with tool use, currently available in beta. This capability allows both models to use tools—such as web search—during their extended thinking processes, enabling them to alternate between reasoning and tool use to improve response quality.

This represents a fundamental shift in how AI systems approach complex problems. Rather than being limited to information contained within their parameters, Claude 4 models can now actively seek out additional information when needed, verify facts, and incorporate external data into their reasoning processes. This creates a more dynamic and adaptive problem-solving approach that more closely resembles human cognitive processes.

The ability to seamlessly integrate tool use with reasoning allows Claude 4 to tackle problems that would be impossible for a standalone language model, regardless of its size or training. For example, when answering questions about current events, Claude can now search for the latest information, ensuring responses remain accurate and up-to-date.

Parallel Tool Execution

Both Claude 4 models can now use tools in parallel, a significant advancement that dramatically improves efficiency when multiple external resources need to be consulted. This capability allows the models to orchestrate complex workflows involving multiple tools, gathering information from various sources simultaneously rather than sequentially.

For developers building AI agents, this parallel execution capability enables more sophisticated applications that can coordinate multiple services and APIs, significantly expanding what's possible with AI-powered automation.

Improved Memory Capabilities

When given access to local files by developers, Claude 4 models demonstrate significantly improved memory capabilities. They can extract and save key facts to maintain continuity and build tacit knowledge over time, addressing one of the fundamental limitations of previous AI systems.

Claude Opus 4 particularly excels in this area, becoming skilled at creating and maintaining "memory files" to store key information. This unlocks better long-term task awareness, coherence, and performance on agent tasks—as demonstrated by Opus 4 creating a "Navigation Guide" while playing Pokémon to track its progress and strategy.

This memory enhancement enables Claude 4 to maintain context across extended interactions, reducing the need for users to repeatedly provide the same information and creating more natural, continuous experiences.

Claude Code General Availability

After receiving extensive positive feedback during its research preview, Claude Code is now generally available. This specialized application of Claude's capabilities supports background tasks via GitHub Actions and offers native integrations with VS Code and JetBrains, displaying edits directly in users' files for seamless pair programming.

The release includes an extensible Claude Code SDK, allowing developers to build their own agents and applications using the same core agent technology. This opens up new possibilities for AI-assisted software development across various environments and workflows.

New API Capabilities

Anthropic has released four new capabilities on the Anthropic API that enable developers to build more powerful AI agents:

1. Code Execution Tool: Allows Claude to run code and use the results in its reasoning
2. MCP Connector: Facilitates connections to external services and APIs
3. Files API: Provides improved file handling capabilities
4. Prompt Caching: Enables caching prompts for up to one hour, improving efficiency for repetitive tasks

These API enhancements give developers more flexibility and power when building applications on top of Claude 4, supporting more sophisticated AI agents that can interact with a wider range of services and maintain state more effectively.

Multi-Step Reasoning Capabilities

One of the most significant advancements in Claude 4 is its enhanced ability to reason over many steps, maintaining coherence and accuracy throughout extended chains of thought. This capability represents a fundamental shift in how AI systems approach complex problems that require breaking down challenges into smaller, sequential steps.

The Evolution of Multi-Step Reasoning

Traditional language models often struggle with problems requiring multiple logical steps, frequently losing track of intermediate results or making errors that compound throughout the reasoning process. Claude 4 models address this limitation through architectural improvements and training techniques specifically designed to maintain coherence across extended reasoning chains.

The models can now decompose complex problems into manageable sub-problems, solve each component systematically, and integrate these solutions into a comprehensive answer. This approach mirrors human problem-solving strategies and enables Claude 4 to tackle challenges that would be intractable through single-step reasoning.

Sustained Performance Over Thousands of Steps

Claude Opus 4 particularly excels at maintaining focus and accuracy over thousands of reasoning steps. As highlighted in Anthropic's announcement, it can work continuously for several hours on complex tasks, dramatically outperforming previous models. This sustained performance enables entirely new categories of applications where long-term coherence is essential.

For example, when analyzing complex codebases, Opus 4 can maintain an accurate mental model of the entire system architecture while making targeted modifications across multiple files. Similarly, when solving intricate mathematical or logical problems, it can track numerous variables and constraints without losing context or making contradictory assumptions.

Self-Monitoring and Error Correction

Claude 4 models demonstrate improved self-monitoring capabilities, allowing them to catch and correct errors during extended reasoning processes. This self-correction mechanism is crucial for multi-step reasoning, as it prevents small errors from cascading into significant failures.

The models can recognize when their reasoning has gone astray, backtrack to identify the source of the error, and adjust their approach accordingly. This metacognitive ability significantly enhances reliability in complex reasoning tasks and reduces the need for human intervention.

Memory Management Through File Access

When given access to local files, Claude 4 models can externalize their thinking process, creating structured notes that serve as external memory. This capability is particularly valuable for multi-step reasoning, as it allows the models to offload intermediate results and refer back to them when needed.

Anthropic provides the example of Opus 4 creating a "Navigation Guide" while playing Pokémon, demonstrating how the model can maintain awareness of its progress and strategy across an extended task. This external memory approach enables more sophisticated reasoning than would be possible with the model's internal context window alone.

Thinking Summaries for Transparency

To make extended reasoning processes more accessible to users, Claude 4 introduces thinking summaries that condense lengthy thought processes using a smaller model. This feature maintains transparency while improving the user experience, providing insight into how Claude arrived at its conclusions without overwhelming users with excessive detail.

For most cases, the full thought process is displayed, with summarization only needed about 5% of the time. For users requiring complete access to raw chains of thought, Anthropic offers a Developer Mode through their sales team.

Real-World Applications and Partner Testimonials

The capabilities of Claude 4 models are already being leveraged by numerous organizations across various industries, demonstrating their practical impact on real-world applications. Partner testimonials highlight the models' strengths in different domains and provide concrete examples of how these advancements translate to tangible benefits.

Coding and Software Development

Several leading technology companies have integrated Claude 4 models into their development workflows, reporting significant improvements in productivity and code quality.

Cursor describes Claude Opus 4 as "state-of-the-art for coding and a leap forward in complex codebase understanding." This assessment highlights the model's ability to comprehend large, complex codebases holistically, enabling more contextually appropriate modifications and suggestions.

Replit reports "improved precision and dramatic advancements for complex changes across multiple files." This capability is particularly valuable for refactoring tasks that span multiple components of a software system, where maintaining consistency across changes is critical.

Block calls Opus 4 "the first model to boost code quality during editing and debugging in its agent, codename goose, while maintaining full performance and reliability." This suggests that the model not only helps developers write code faster but actually improves the quality of the resulting software.

GitHub has announced that Claude Sonnet 4 will serve as the base model for the new coding agent in GitHub Copilot, noting that it "soars in agentic scenarios." This integration will bring Claude 4's capabilities to millions of developers worldwide, potentially transforming how software is created.

Extended Autonomous Operations

Claude 4's ability to maintain performance over extended periods enables new categories of autonomous operations that were previously impractical.

Rakuten validated Opus 4's capabilities with "a demanding open-source refactor running independently for 7 hours with sustained performance." This demonstration highlights the model's ability to work on complex tasks without human intervention for extended periods, maintaining focus and quality throughout.

iGent reports that "Sonnet 4 excels at autonomous multi-feature app development, as well as substantially improved problem-solving and codebase navigation—reducing navigation errors from 20% to near zero." This dramatic reduction in errors illustrates how Claude 4's improved reasoning and memory capabilities translate to more reliable autonomous operations.

Research and Complex Problem-Solving

Claude 4 models demonstrate exceptional capabilities in research contexts and complex problem-solving scenarios.

Cognition notes that "Opus 4 excels at solving complex challenges that other models can't, successfully handling critical actions that previous models have missed." This suggests that the model can address previously intractable problems, potentially opening new frontiers in AI-assisted research.

The models' strong performance on graduate-level reasoning tasks (GPQA Diamond) and high school math competitions (AIME 2025) further demonstrates their potential for educational applications and scientific research support.

Design and Creative Work

Manus highlights Claude Sonnet 4's "improvements in following complex instructions, clear reasoning, and aesthetic outputs." This testimonial suggests that the model excels not only at technical tasks but also at creative and design-oriented work where aesthetic considerations are important.

The ability to follow complex instructions precisely while maintaining aesthetic quality makes Claude 4 models valuable tools for creative professionals who need technical assistance without sacrificing artistic vision.

Enterprise Integration

Claude 4 models are designed to integrate seamlessly into enterprise workflows, with both models available on the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI. This multi-platform availability ensures that organizations can leverage Claude 4's capabilities within their existing cloud infrastructure.

Sourcegraph says the model "shows promise as a substantial leap in software development—staying on track longer, understanding problems more deeply, and providing more elegant code quality." This assessment highlights how Claude 4 can enhance enterprise software development processes, potentially reducing development time and improving code quality.

Augment Code reports "higher success rates, more surgical code edits, and more careful work through complex tasks," making Sonnet 4 their "top choice for their primary model." This endorsement demonstrates the model's readiness for production enterprise environments where reliability and precision are paramount.

Comparison with Other AI Models

Claude 4 models represent a significant advancement in AI capabilities, establishing new benchmarks across multiple domains. To fully appreciate their impact, it's valuable to compare them with other leading AI models in the market.

Claude 4 vs. Previous Claude Models

The most immediate comparison is between Claude 4 models and their predecessors, particularly Claude Sonnet 3.7. The improvements are substantial across all measured dimensions:

• Coding Performance: Claude Sonnet 4 achieves 72.7% on SWE-bench, compared to 62.3% for Sonnet 3.7, representing a 10.4 percentage point improvement. Claude Opus 4 pushes this even further to 72.5% (79.4% with parallel test-time compute).
• Reasoning Capabilities: On graduate-level reasoning (GPQA Diamond), Opus 4 scores 79.6%/83.3% compared to Sonnet 3.7's 78.2%, while on high school math competitions (AIME 2025), Opus 4 achieves 75.5%/90.0% compared to Sonnet 3.7's 54.8%.
• Instruction Following: Both Claude 4 models demonstrate significantly improved precision in following complex instructions, with a 65% reduction in shortcut behaviors compared to Sonnet 3.7 on agentic tasks.
• Memory and Continuity: Claude 4 models, particularly Opus 4, show dramatically improved memory capabilities when given access to local files, enabling better long-term task awareness and coherence.
• Tool Use: Both models can now use tools during extended thinking and execute tools in parallel, capabilities not present in previous Claude models.

These improvements represent not just incremental progress but fundamental advancements in AI capabilities, particularly in areas requiring sustained performance, precise instruction following, and complex reasoning.

Claude 4 vs. OpenAI Models

When compared to OpenAI's models, Claude 4 demonstrates several distinct advantages:

• Software Engineering: Claude Opus 4 (72.5%) and Sonnet 4 (72.7%) outperform OpenAI's models on SWE-bench, with GPT-4.1 scoring 54.6% and o3 scoring 69.1%.
• Terminal Coding: Claude Opus 4 (43.2%) significantly outperforms OpenAI's models in terminal coding tasks, with o3 scoring 30.2% and GPT-4.1 scoring 30.3%.
• Agentic Tool Use: On retail tasks in TAU-bench, Claude Opus 4 (81.4%) and Sonnet 4 (80.5%) outperform OpenAI o3 (70.4%) and GPT-4.1 (68.0%).
• Sustained Performance: Claude Opus 4's ability to work continuously for several hours on complex tasks represents a capability not matched by OpenAI's current models.

OpenAI's models do maintain advantages in certain areas:

• Visual Reasoning: OpenAI o3 scores 82.9% on MMMU validation, compared to Claude Opus 4's 76.5%.
• Graduate-level Reasoning: OpenAI o3 scores 83.3% on GPQA Diamond, matching Claude Opus 4's best performance (83.3%).
• High School Math: OpenAI o3 scores 88.9% on AIME 2025, compared to Claude Opus 4's 75.5% (though Opus 4 reaches 90.0% with parallel test-time compute).

Claude 4 vs. Google's Gemini Models

Compared to Google's Gemini 2.5 Pro (Preview 05-06), Claude 4 models show several strengths:

• Software Engineering: Claude Opus 4 (72.5%) and Sonnet 4 (72.7%) significantly outperform Gemini 2.5 Pro (63.2%) on SWE-bench.
• Terminal Coding: Claude Opus 4 (43.2%) and Sonnet 4 (35.5%) outperform Gemini 2.5 Pro (25.3%) on Terminal-bench.
• Visual Reasoning: Gemini 2.5 Pro (79.6%) outperforms both Claude 4 models in visual reasoning tasks.
• High School Math: Gemini 2.5 Pro (83.0%) outperforms Claude Sonnet 4 (70.5%) but falls short of Claude Opus 4's best performance (90.0% with parallel test-time compute).

Unique Differentiators

Beyond benchmark comparisons, Claude 4 models offer several unique differentiators:

1. Extended Thinking with Tool Use: The ability to alternate between reasoning and tool use during extended thinking represents a novel approach to complex problem-solving.
2. Memory File Creation: Claude Opus 4's ability to create and maintain memory files when given local file access enables new forms of long-term coherence.
3. Sustained Performance: Claude Opus 4's ability to work continuously for several hours on complex tasks, maintaining focus and quality throughout, enables entirely new categories of applications.
4. Reduced Shortcut Behaviors: The 65% reduction in shortcut behaviors compared to previous models makes Claude 4 more reliable for agentic tasks where precision and thoroughness are critical.

These differentiators position Claude 4 models as particularly well-suited for complex, long-running tasks that require sustained reasoning, precise instruction following, and effective tool use—capabilities that align well with the needs of developers building sophisticated AI agents and applications.

Conclusion and Future Implications

The release of Claude 4 models represents a significant milestone in AI development, establishing new benchmarks across multiple domains and introducing capabilities that fundamentally expand what's possible with AI systems. As we look toward the future, several key implications emerge from these advancements.

The Path to Virtual Collaborators

Claude 4 models, particularly Opus 4, take substantial steps toward becoming true virtual collaborators capable of sustained, coherent interaction over extended periods. The ability to maintain full context, sustain focus on longer projects, and effectively use tools positions these models as increasingly capable partners for complex knowledge work.

The improvements in memory capabilities, when combined with extended thinking and tool use, enable more natural and productive human-AI collaboration. Rather than serving as simple question-answering systems, Claude 4 models can actively participate in extended problem-solving processes, maintaining awareness of context and building tacit knowledge over time.

This evolution toward virtual collaboration has profound implications for knowledge work across industries, potentially transforming how humans interact with AI systems and incorporate them into creative and analytical workflows.

Expanding the Frontier of AI Agents

Claude 4's advancements in sustained performance, tool use, and memory capabilities dramatically expand what's possible with AI agents. The ability to work continuously for several hours on complex tasks, maintaining focus and quality throughout, enables entirely new categories of autonomous applications.

The reduction in shortcut behaviors (65% less likely than previous models) makes Claude 4 more reliable for agentic tasks where precision and thoroughness are critical. This reliability, combined with improved instruction following, creates new possibilities for delegating complex workflows to AI systems with greater confidence in the results.

As these capabilities continue to evolve, we can expect to see increasingly sophisticated AI agents handling complex tasks with minimal human supervision, from software development and data analysis to research and content creation.

Implications for Software Development

The exceptional performance of Claude 4 models on software engineering benchmarks suggests a significant transformation in how software is developed. With Claude Opus 4 achieving 72.5% on SWE-bench and Claude Code now generally available, developers have access to increasingly capable AI assistants for coding tasks.

The integration of Claude Sonnet 4 as the base model for GitHub Copilot's new coding agent will bring these capabilities to millions of developers worldwide, potentially accelerating software development cycles and enabling more ambitious projects with smaller teams.

Beyond simple code completion, Claude 4's ability to understand complex codebases holistically and make coherent modifications across multiple files enables more sophisticated refactoring and architectural improvements, potentially enhancing software quality and maintainability.

Responsible Development and Safety Considerations

Anthropic emphasizes that Claude 4 models come with extensive testing and evaluation to minimize risk and maximize safety, including implementing measures for higher AI Safety Levels like ASL-3. This focus on responsible development is crucial as AI capabilities continue to advance.

The introduction of thinking summaries for extended reasoning processes represents a thoughtful approach to balancing transparency with usability, ensuring users can understand how Claude arrived at its conclusions without being overwhelmed by excessive detail.

As AI systems become more capable, this commitment to safety, transparency, and responsible development will remain essential for ensuring these technologies benefit humanity while minimizing potential risks.

The Competitive Landscape

The release of Claude 4 models intensifies competition in the frontier AI space, with Anthropic, OpenAI, Google, and others pushing the boundaries of what's possible with large language models. This competitive environment drives rapid innovation and improvement, benefiting users and accelerating the development of increasingly capable AI systems.

Claude 4's unique strengths in coding, sustained performance, and multi-step reasoning establish Anthropic as a leader in specific domains, while competition in areas like visual reasoning and mathematical problem-solving remains fierce.

This specialization may lead to an ecosystem where different AI systems excel in different domains, with users selecting the most appropriate tool for specific tasks rather than relying on a single general-purpose system.

Looking Forward

As we look to the future, the advancements embodied in Claude 4 suggest several trends that will likely shape AI development in the coming years:

1. Increased Focus on Sustained Performance: The ability to maintain quality and coherence over extended interactions will become increasingly important as AI systems tackle more complex tasks.
2. Deeper Integration of Tools and Reasoning: The combination of reasoning capabilities with effective tool use will continue to expand what AI systems can accomplish, creating more powerful and flexible assistants.
3. Enhanced Memory and Context Management: Improvements in how AI systems maintain and utilize context over time will enable more natural and productive human-AI collaboration.
4. Specialization and Differentiation: As the frontier advances, AI systems may increasingly differentiate based on specialized capabilities rather than general-purpose performance.

The release of Claude 4 represents not just an incremental improvement but a significant step toward more capable, reliable, and useful AI systems. As these technologies continue to evolve, they promise to transform how we work, create, and solve problems across virtually every domain of human endeavor.