Claude Opus 4.6: The Complete Guide to Features, Benchmarks, and Real-World Applications

Anthropic released Claude Opus 4.6 on February 5, 2026. This new model brings major improvements for developers, researchers, and enterprise teams.

Claude Opus 4.6 is the most advanced model in Anthropic's lineup. It offers a 1 million token context window in beta. The model excels at coding tasks, long-horizon work, and professional applications.

The biggest upgrade is how Opus 4.6 handles complex tasks over extended sessions. It plans better, catches its own mistakes, and works through large codebases without losing focus. These improvements show up across finance, legal work, research, and software development.

This guide explains what makes Claude Opus 4.6 different, how to use it effectively, and where it outperforms other AI models.

What Is Claude Opus 4.6?

Claude Opus 4.6 is Anthropic's flagship AI model released on February 5, 2026. It sits at the top of the Claude 4 model family, above Sonnet 4.5 and Haiku 4.5.

The model builds on Opus 4.5 from November 2025. It keeps the same pricing but adds substantial new capabilities. The main focus areas are agentic coding, long-context reasoning, and knowledge work tasks.

Opus 4.6 uses the model ID claude-opus-4-6 in API calls. It's available through claude.ai, the Claude API, and major cloud platforms including Amazon Web Services, Google Cloud, Microsoft Azure, and Snowflake.

Key Features and Capabilities

1 Million Token Context Window (Beta)

The context window expansion is one of the biggest changes. Opus 4.6 supports up to 1 million input tokens in beta.

This equals roughly 750,000 words or 10-15 full research papers. The model can process entire codebases, multiple documents, or long conversation histories without performance drops.

Previous models suffered from "context rot" - performance degraded as conversations grew longer. Opus 4.6 fixes this problem. On the MRCR v2 benchmark, it scores 76% at finding information buried in 1 million tokens. Claude Sonnet 4.5 scored just 18.5% on the same test.

The beta context window requires using the context-1m-2025-08-07 header in API calls. Standard context is 200,000 tokens. Premium pricing applies above 200,000 tokens at $10 input and $37.50 output per million tokens.

Adaptive Thinking

Opus 4.6 introduces adaptive thinking mode. The model decides when and how much to reason based on task complexity.

Previous models used extended thinking with a fixed token budget. You had to guess how many thinking tokens a task needed. Now Claude evaluates each request and adjusts automatically.

At high effort (the default), the model uses extended thinking when helpful. For simple tasks, it skips thinking entirely. This saves tokens and reduces latency on straightforward requests.

Adaptive thinking works especially well for agentic workflows. The model can think between tool calls, making better decisions about next steps.

Effort Controls

Anthropic added four effort levels: low, medium, high (default), and max.

The effort parameter controls how many tokens Claude spends on responses. Lower effort means faster, cheaper responses with slightly reduced capability. Higher effort produces more thorough reasoning.

You set effort through the API:

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=4096,
    output_config={"effort": "medium"},
    messages=[{"role": "user", "content": "Your prompt"}]
)

The effort parameter works with or without thinking enabled. It affects all token spending including tool calls.

Context Compaction

Long-running tasks often hit the context window limit. Context compaction solves this problem.

When a conversation approaches the maximum context, Opus 4.6 automatically summarizes earlier parts. It replaces old messages with condensed versions. This extends task duration without manual truncation.

Compaction happens server-side. You configure a threshold. When context reaches that point, Claude summarizes and continues working.

This feature enables effectively infinite conversations for agentic workflows. The model can work through multi-hour tasks without hitting limits.

128K Output Tokens

Opus 4.6 can generate up to 128,000 tokens in a single response. This doubles the previous 64,000 token limit.

Longer outputs help with:

• Generating complete codebases
• Writing extensive documentation
• Creating detailed reports
• Building comprehensive spreadsheets

The extended output capacity pairs well with the 1M token context. You can analyze large inputs and produce large outputs in one API call.

Agent Teams in Claude Code

Claude Code now supports agent teams in research preview. You can spin up multiple independent Claude instances that work in parallel.

Previous versions used sequential subagents. One agent completed its work before the next started. Agent teams coordinate simultaneously on different components.

Scott White, Head of Product at Anthropic, compared this to a talented human team. Each agent owns its piece and coordinates with others. Tasks complete faster through parallel work.

Agent teams excel at:

• Large codebase migrations
• Multi-component development
• Complex research projects
• Organizational task management

One early access partner reported Opus 4.6 "autonomously closed 13 issues and assigned 12 issues to the right team members in a single day, managing a ~50-person organization across 6 repositories."

Office Integration Upgrades

Anthropic upgraded Claude in Excel and released Claude in PowerPoint as a research preview.

Excel improvements: The model now interprets messy spreadsheets without explicit explanations. It understands context from headers, formulas, and data patterns.

PowerPoint integration: Claude works directly inside PowerPoint through a side panel. You can create and edit presentations without switching applications. The model automatically matches existing colors, fonts, and layouts.

Previously, you asked Claude to create a deck, then manually transferred it to PowerPoint. Now the entire workflow happens in one place.

Benchmark Performance

Opus 4.6 sets new records across multiple industry evaluations.

Coding and Agentic Tasks

Terminal-Bench 2.0 measures ability to navigate terminals, execute commands, and perform development operations. Opus 4.6 achieves the highest score in Anthropic's lineup and outperforms most competitors.

On SWE-bench Verified, which tests real-world software engineering, Opus 4.6 scores 80.8%. This is a slight decrease from Opus 4.5's 80.9%, suggesting optimization focused on other areas. With prompt modifications, the score reaches 81.42%.

Knowledge Work and Professional Tasks

GDPval-AA evaluates economically valuable knowledge work. Opus 4.6's 144-point Elo lead over GPT-5.2 translates to winning roughly 70% of head-to-head comparisons.

BrowseComp was developed by OpenAI to showcase their models' search capabilities. Anthropic's 84% score represents the current industry high. This makes Opus 4.6 particularly effective for research agents and information retrieval.

Reasoning and Problem Solving

The 68.8% ARC AGI 2 score represents nearly double Opus 4.5's performance. This suggests genuine advances in novel problem-solving beyond benchmark optimization.

Context and Retrieval

On long-context retrieval benchmarks, Opus 4.6 demonstrates usable performance across its full 1M token window.

The 8-needle MRCR v2 test hides eight pieces of information across 1 million tokens. Opus 4.6 finds 76% of them. This is a qualitative shift from the 18.5% score of Sonnet 4.5.

GPT-5.2 achieves similar performance on comparable tests. Gemini 3 Pro trails at 77% on the 8-needle variant despite having a 2M native context window.

Real-World Use Cases

Software Development

Opus 4.6 excels at long-running coding tasks. It plans upfront, adapts strategies as it learns, and catches its own mistakes during code review.

Use it for:

• Large codebase migrations
• Refactoring complex systems
• Bug detection and debugging
• Code review and quality checks
• Multi-file implementations

Michael Truell, co-founder of Cursor, notes: "Claude Opus 4.6 excels on the hardest problems. It shows greater persistence, stronger code review, and the ability to stay on long tasks where other models tend to give up."

One developer reported Opus 4.6 "handled a multi-million-line codebase migration like a senior engineer. It planned up front, adapted its strategy as it learned, and finished in half the time."

The model works particularly well in Claude Code, where agent teams can tackle different components simultaneously.

Research and Analysis

The 1M token context window transforms research workflows. You can process entire literature reviews, patent portfolios, or regulatory submissions in single passes.

Research applications:

• Literature analysis across multiple papers
• Competitive intelligence gathering
• Scientific data interpretation
• Market research synthesis
• Regulatory document analysis

Justin Reppert from Elicit reported: "Claude Opus 4.6 achieved 85% recall on our biopharma competitive intelligence benchmark—a 12-point lift over baseline through autonomous 15-minute discovery loops with zero prompt tuning."

The model performs almost twice as well as Opus 4.5 on computational biology, structural biology, organic chemistry, and phylogenetics benchmarks.

Legal and Finance Work

Opus 4.6 understands professional domain conventions. It produces documents, spreadsheets, and presentations that match expert-created work.

Professional use cases:

• Contract review and drafting
• Financial modeling and analysis
• Due diligence research
• Regulatory compliance checks
• Risk assessment reports

The 90.2% BigLaw Bench score shows strong legal reasoning capability. The 1606 GDPval-AA Elo rating demonstrates effectiveness across finance and legal tasks.

Matej Jambrich, CTO at Dentons Europe, said: "Claude in Microsoft Foundry brings the frontier reasoning strength we need for legal work, backed by the governance and operational controls required in an enterprise environment."

Content and Document Creation

The model excels at creating professional work products. It understands formatting conventions, maintains consistency, and produces polished outputs.

Content applications:

• Report generation
• Presentation creation
• Spreadsheet building
• Documentation writing
• Data visualization

With Claude in PowerPoint, you can build presentations that automatically match brand guidelines. In Excel, the model interprets complex spreadsheets and suggests improvements.

How to Use Claude Opus 4.6 Effectively

Choose the Right Effort Level

Start with the default high effort for most tasks. Adjust based on results:

• Use low effort for simple extractions or classifications where speed matters
• Use medium effort for moderate tasks with cost constraints
• Keep high effort as default for quality work
• Use max effort only for the hardest problems requiring deepest reasoning

If you notice the model overthinking simple requests, dial down to medium. This saves tokens and reduces latency.

Enable Adaptive Thinking

For API users, set thinking to adaptive mode:

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=16000,
    thinking={"type": "adaptive"},
    messages=[{"role": "user", "content": "Your prompt"}]
)

Adaptive thinking works best for varied workloads where some requests are simple and others complex. The model optimizes automatically.

Leverage the Full Context Window

Take advantage of the 1M token context for:

• Processing multiple documents simultaneously
• Working with large codebases
• Maintaining long conversation histories
• Analyzing comprehensive datasets

Remember to use the beta header for contexts above 200K tokens. Premium pricing applies, but the capability often justifies the cost.

Structure Agentic Workflows

For complex multi-step tasks, give Opus 4.6 room to work:

1. Clearly define the end goal
2. Provide necessary context upfront
3. Let the model plan its approach
4. Allow use of tools and thinking
5. Review outputs and iterate

The model performs better when it can think through problems and correct its own mistakes.

Use Agent Teams for Parallel Work

In Claude Code, leverage agent teams for:

• Multi-component projects
• Tasks with independent workstreams
• Large-scale code migrations
• Organizational coordination

Assign clear responsibilities to each agent. Let them coordinate on shared goals.

Common Mistakes to Avoid

Overusing Max Effort

Max effort provides the highest capability but adds cost and latency. Many tasks work fine at high or medium effort.

Test different effort levels on representative samples. Find the minimum that produces acceptable results. Save max effort for genuinely hard problems.

Ignoring Context Limits

While Opus 4.6 supports 1M tokens, standard context is 200K. Enable the beta header when needed. Budget for premium pricing above 200K tokens.

Also remember that large contexts increase API call costs even at standard rates. Only include necessary information.

Forcing Thinking on Simple Tasks

Adaptive thinking automatically skips thinking for simple requests. Don't manually enable thinking for basic operations like classification or extraction.

Trust the model to evaluate task complexity. It uses thinking when helpful and skips it when unnecessary.

Comparing Models Without Proper Testing

Benchmark scores don't tell the complete story. Test Opus 4.6 on your actual use cases.

What works best depends on your specific requirements. Consider factors like:

• Task complexity
• Required output format
• Latency tolerance
• Budget constraints
• Integration needs

Run side-by-side comparisons with real workflows before making decisions.

How Opus 4.6 Compares to Competitors

vs GPT-5.2

Opus 4.6 leads on:

• Enterprise knowledge work (GDPval-AA)
• Agentic search (BrowseComp)
• Legal reasoning (BigLaw Bench)
• Long-context retrieval (MRCR v2)

GPT-5.2 leads on:

• Some coding benchmarks (SWE-bench)
• Graduate-level reasoning (GPQA Diamond)
• Tool coordination at scale (MCP Atlas)

Pricing: Opus 4.6 costs $5/$25 input/output vs GPT-5.2's $5/$15. Opus 4.6 charges more for outputs but often requires fewer API calls for complex tasks.

vs Gemini 3 Pro

Opus 4.6 leads on:

• Usable long-context performance
• Coding and terminal operations
• Enterprise task execution
• Agentic workflows

Gemini 3 Pro leads on:

• Raw context window size (2M native)
• Some visual reasoning tasks
• Multilingual capabilities

Key difference: Gemini advertises a larger context but shows performance degradation. Opus 4.6 maintains quality across its full 1M window.

vs Claude Opus 4.5

Opus 4.6 improves on its predecessor across nearly every benchmark:

• 190-point GDPval-AA Elo gain
• 76% vs 18.5% on MRCR long-context retrieval
• 65.4% vs 59.8% on Terminal-Bench
• 68.8% vs ~35% on ARC AGI 2

The biggest gains come in long-context handling, agentic coding, and sustained task performance. Pricing remains identical.

Pricing and Access

API Pricing

Standard context (up to 200K tokens):

• Input: $5 per million tokens
• Output: $25 per million tokens

Premium context (200K to 1M tokens, beta):

• Input: $10 per million tokens
• Output: $37.50 per million tokens

US-only inference adds 1.1x multiplier to these rates.

Claude.ai Access

Opus 4.6 requires a paid subscription:

• Pro plan: $20/month
• Team plan: $30/user/month
• Enterprise: Custom pricing

Free tier users cannot access Opus models.

Cloud Platforms

Opus 4.6 is available on:

• Amazon Bedrock
• Google Cloud Vertex AI
• Microsoft Azure (Foundry)
• Snowflake Cortex AI

Pricing varies by platform. Check with your cloud provider for specific rates.

Safety and Alignment

Anthropic conducted extensive safety evaluations before releasing Opus 4.6. The model underwent the most comprehensive testing pipeline in the company's history.

Key safety improvements:

• Low rates of harmful behavior across evaluations
• Strong performance on user wellbeing assessments
• Six novel cybersecurity stress tests
• Enhanced alignment compared to previous models

The company claims Opus 4.6 shows the strongest safety profile of any frontier model. Independent testing supports this claim.

Anthropic also uses Opus 4.6 for cyberdefensive work. The model helps find and patch vulnerabilities in open-source software. This supports the goal of keeping cyberdefenders ahead of potential threats.

Tips for Getting the Best Results

Provide Clear Context

Give Opus 4.6 the information it needs upfront. Include:

• Task objectives
• Success criteria
• Relevant background
• Format requirements
• Constraints or limitations

The model works better with complete context than when forced to guess.

Let It Think

For complex problems, enable adaptive thinking and trust the model to reason. Don't rush to conclusions.

Thinking adds tokens but often saves overall cost by reducing errors and iterations.

Iterate and Refine

Opus 4.6 can review and improve its own work. Ask it to:

• Check for errors
• Suggest improvements
• Consider alternatives
• Validate assumptions

This self-correction capability is one of the model's strengths.

Use Tools Appropriately

When working through the API, provide tools that help the model:

• Search for current information
• Execute code
• Access external data
• Verify calculations

Tool use extends capabilities beyond the base model's knowledge.

Set Appropriate Expectations

Opus 4.6 is powerful but not perfect. It still makes mistakes, especially on:

• Very recent events (beyond training cutoff)
• Highly specialized domains
• Tasks requiring real-time data
• Problems outside its training distribution

Verify critical outputs. Use the model as a capable assistant, not an infallible oracle.

Future Developments

Anthropic positions Opus 4.6 as part of an ongoing evolution toward more capable agentic systems.

Expected areas of improvement:

• Extended context windows beyond 1M tokens
• Better tool coordination
• More sophisticated reasoning
• Improved efficiency and speed
• Stronger domain-specific performance

The company continues testing new features in research previews. Agent teams and context compaction may become standard features in future releases.

Claude Code and office integrations will likely expand. Anthropic is pushing deeper into professional workflows where AI can handle substantial portions of knowledge work.

Conclusion

Claude Opus 4.6 represents a significant step forward in AI capability. The 1 million token context window, adaptive thinking, and record benchmark scores make it one of the most capable models available.

The key strengths are:

• Sustained performance on long-running tasks
• Strong coding and debugging abilities
• Effective knowledge work across professional domains
• Improved self-correction and planning
• Usable long-context without performance degradation

For developers, researchers, and enterprise teams working with complex problems, Opus 4.6 delivers meaningful improvements over previous models. The combination of intelligence, context, and agentic capabilities opens new possibilities for AI-assisted work.

Pricing remains unchanged from Opus 4.5, making the upgrade straightforward for existing users. New users should weigh the higher output costs against reduced need for multiple API calls and iterations.

Try Opus 4.6 on your actual use cases. Start with high effort and adaptive thinking. Adjust based on results. The model excels when given room to think, plan, and work through complex problems systematically.