Definition
Claude Opus 4.6 is Anthropic's flagship AI model released February 5, 2026. It is the first Opus-class model with a 1 million token context window, leads every frontier model on GDPval-AA knowledge work (1606 Elo, 144 points ahead of GPT-5.2), Terminal-Bench 2.0 agentic coding (65.4%), BrowseComp agentic search (84.0%), and ARC AGI 2 abstract reasoning (68.8%). It introduces adaptive thinking, context compaction, agent teams, and four effort levels for cost control, all while maintaining the lowest over-refusal rate of any recent Claude model.
Anthropic released Claude Opus 4.6 on February 5, 2026. It is the most capable model in the Claude family, leading every major frontier benchmark in agentic coding, complex reasoning, financial analysis, and long-context retrieval. For businesses building AI into operations, marketing, and product development, this release changes the calculus on what is possible with a single foundation model.
At Conversion System, we work with businesses across SaaS, e-commerce, financial services, and cannabis to implement AI systems that drive measurable revenue. Claude Opus 4.6 represents a significant leap forward for the agentic workflows and automation strategies we deploy. This guide covers every major feature, benchmark result, pricing detail, and strategic takeaway you need to evaluate this model for your business.
Claude Opus 4.6 at a Glance
token context window (first Opus-class model)
max output tokens per request
Terminal-Bench 2.0 (top agentic coding score)
GDPval-AA Elo (144 pts ahead of GPT-5.2)
Source: Anthropic Official Announcement, Artificial Analysis GDPval-AA
What Is Claude Opus 4.6
Claude Opus 4.6 is Anthropic's flagship AI model, released February 5, 2026. It succeeds Claude Opus 4.5 and is designed for the most demanding enterprise tasks: long-running agentic workflows, multi-step coding, financial analysis, legal reasoning, and research across large document sets. The model is accessible via claude.ai, the Claude API (model name: claude-opus-4-6), Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry on Azure.
What separates Opus 4.6 from its predecessor is not incremental improvement. It is a qualitative shift in how much work an AI model can sustain autonomously. According to Anthropic, the model "plans more carefully, sustains agentic tasks for longer, can operate more reliably in larger codebases, and has better code review and debugging skills to catch its own mistakes." For teams already using AI agents or evaluating AI strategy, this changes the boundary of what you can delegate to a model.
Key Features and Capabilities
Opus 4.6 introduces several architectural and product-level improvements that directly affect how businesses can deploy AI. Here is what matters most:
1M Token Context Window (Beta)
Opus 4.6 is the first Opus-class model with a 1 million token context window. This lets teams feed entire codebases, legal contracts, financial filings, or research papers into a single prompt. On the MRCR v2 benchmark (8-needle, 1M variant), Opus 4.6 scores 76% versus Sonnet 4.5's 18.5%. That is a 4x improvement in retrieval accuracy at extreme context lengths.
Adaptive Thinking
Previously, developers chose between enabling or disabling extended thinking. Now the model decides when deeper reasoning is useful based on contextual signals. At the default effort level (high), Claude uses extended thinking when appropriate but skips it for straightforward queries. This reduces cost and latency on simple tasks while preserving deep analysis when needed.
Effort Controls
Four effort levels (low, medium, high, max) give developers precise control over the tradeoff between intelligence, speed, and cost. For batch processing or simple classification tasks, low effort reduces API spend. For complex analysis or financial modeling, max effort unlocks the model's full reasoning depth.
Context Compaction (Beta)
Long-running agents often hit context limits. Context compaction automatically summarizes and replaces older context when the conversation approaches a configurable threshold. This lets Claude complete longer agentic sessions without losing the thread of work.
Agent Teams (Claude Code)
In Claude Code, you can now spin up multiple agents that work in parallel and coordinate autonomously. This is built for tasks that split into independent, read-heavy work like codebase reviews, documentation audits, or multi-file refactoring. You can take over any subagent directly using Shift+Up/Down or tmux.
128K Output Tokens
Opus 4.6 supports outputs of up to 128K tokens per request. This eliminates the need to break large generation tasks into multiple calls. Teams generating long reports, detailed documentation, or comprehensive analysis can get complete outputs in a single request.
Benchmark Performance: How Opus 4.6 Compares
Benchmarks have limitations, but they remain the most reliable way to compare frontier models. Opus 4.6 leads or matches the best available model across nearly every major evaluation. Here is how it stacks up against OpenAI's GPT-5.2, Google's Gemini 3 Pro, and its predecessor Opus 4.5.
| Benchmark | Opus 4.6 | Opus 4.5 | GPT-5.2 | Gemini 3 Pro |
|---|---|---|---|---|
| Terminal-Bench 2.0 (Agentic Coding) | 65.4% | 59.8% | 64.7% | 56.2% |
| SWE-bench Verified (Software Engineering) | 80.8% | 80.9% | 80.0% | 76.2% |
| BrowseComp (Agentic Search) | 84.0% | 67.8% | 77.9% | 59.2% |
| Humanity's Last Exam (w/ tools) | 53.1% | 43.4% | 50.0% | 45.8% |
| ARC AGI 2 (Novel Problem-Solving) | 68.8% | 37.6% | 54.2% | 45.1% |
| GDPval-AA Elo (Knowledge Work) | 1606 | 1416 | 1462 | 1195 |
| Finance Agent (Financial Analysis) | 60.7% | 55.9% | 56.6% | 44.1% |
| GPQA Diamond (Graduate-Level Reasoning) | 91.3% | 87.0% | 93.2% | 91.9% |
| OSWorld (Computer Use) | 72.7% | 66.3% | N/R | N/R |
| MRCR v2 8-needle 1M (Long-Context) | 76.0% | N/A | N/R | N/R |
| BigLaw Bench (Legal Reasoning) | 90.2% | N/R | N/R | N/R |
Sources: Anthropic, Vellum AI Benchmarks, Artificial Analysis. N/R = Not Reported.
Standout Results Explained
Three results deserve special attention because they signal where the industry is heading:
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ARC AGI 2: 68.8% (nearly double Opus 4.5's 37.6%)
This measures abstract reasoning on problems the model has never seen before. A 31.2 percentage point leap in a single release suggests fundamental improvements in how the model generalizes to novel situations. For businesses, this translates to better handling of ambiguous, first-time problems.
-
BrowseComp: 84.0% (crushing GPT-5.2's 77.9%)
BrowseComp measures a model's ability to find hard-to-locate information online. This makes Opus 4.6 the strongest model for agentic research workflows where AI systems independently search, synthesize, and reason over web content.
-
GDPval-AA: 1606 Elo (144 points ahead of GPT-5.2)
This evaluation measures performance on economically valuable knowledge work across finance, legal, and enterprise domains. Opus 4.6 outperforms GPT-5.2 approximately 70% of the time on these tasks. For teams using AI to produce business deliverables, this is the most actionable benchmark.
Pricing and Access
Opus 4.6 pricing remains consistent with Opus 4.5, making the performance gains essentially free for existing users:
| Tier | Input (per 1M tokens) | Output (per 1M tokens) | Notes |
|---|---|---|---|
| Standard (up to 200K context) | $5.00 | $25.00 | Same as Opus 4.5 |
| Premium (200K-1M context) | $10.00 | $37.50 | New tier for long-context |
| US-Only Inference | 1.1x standard | 1.1x standard | Data residency option |
Source: Anthropic Pricing Page
Access is available through claude.ai (consumer and team plans), the Claude API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry on Azure. The model identifier for API usage is claude-opus-4-6.
Safety and Alignment
Intelligence gains in Opus 4.6 do not come at the cost of safety. Anthropic ran what they describe as "the most comprehensive set of safety evaluations of any model," including new evaluations for user wellbeing and more complex tests of refusal behavior. Key safety findings include:
- Overall alignment matches Opus 4.5, which was Anthropic's most-aligned model to date
- Lowest over-refusal rate of any recent Claude model, meaning fewer false positives on safe queries
- Low misaligned behavior rates across deception, sycophancy, encouragement of delusions, and cooperation with misuse
- Six new cybersecurity probes deployed to detect potential misuse of the model's enhanced capabilities
- Interpretability methods applied for the first time to understand why the model behaves in certain ways
Full details are available in the Claude Opus 4.6 System Card.
For businesses evaluating AI models for regulated industries like banking and finance or healthcare, this safety profile matters. A model that maintains alignment while delivering stronger performance reduces compliance risk in production deployments.
Enterprise Use Cases and Early Results
Anthropic shared testimonials from early access partners that reveal how Opus 4.6 performs in real production environments. These are not synthetic benchmarks. They are results from teams deploying the model on actual business problems.
Software Engineering
SentinelOne reported that 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." Cursor confirmed it as "the new frontier on long-running tasks" and noted it is "highly effective at reviewing code." GitHub highlighted Opus 4.6 for "complex, multi-step coding work developers face every day, especially agentic workflows that demand planning and tool calling."
Legal and Financial Services
Harvey reported that Opus 4.6 achieved a 90.2% BigLaw Bench score with 40% perfect scores and 84% above 0.8, making it "remarkably capable for legal reasoning." Box saw a 10% lift in performance on multi-source analysis across legal, financial, and technical content, reaching 68% versus a 58% baseline. Thomson Reuters highlighted "a meaningful leap in long-context performance" for complex research workflows.
Product and Design
Figma reported that Opus 4.6 "generates complex, interactive apps and prototypes in Figma Make with an impressive creative range" and "translates detailed designs and multi-layered tasks into code on the first try." Lovable described "an uplift in design quality" and noted it "works beautifully with design systems." Shopify called it "the best Anthropic model we've tested" and said it "went above and beyond, exploring and creating details I didn't even know I wanted."
Cybersecurity
NBIM (Norges Bank Investment Management) reported that across 40 cybersecurity investigations, Opus 4.6 "produced the best results 38 of 40 times in a blind ranking against Claude 4.5 models." Each model ran end to end on the same agentic harness with up to 9 subagents and 100+ tool calls per investigation.
Agentic Operations
Rakuten reported that 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." The model "handled both product and organizational decisions while synthesizing context across multiple domains, and it knew when to escalate to a human."
Long-Context Performance: A Qualitative Shift
One of the most significant improvements is long-context retrieval. A common problem with AI models is context rot, where performance degrades as conversations grow longer. Opus 4.6 addresses this directly.
On the MRCR v2 benchmark (8-needle, 1M variant), which tests a model's ability to retrieve information hidden in massive amounts of text, Opus 4.6 scores 76% versus Sonnet 4.5's 18.5%. At 256K context, it scores 93%. This means teams can feed entire legal contracts, annual reports, or research paper collections into a single prompt and expect reliable retrieval.
What 1M Context Means in Practice
- Legal review: Analyze an entire 500-page merger agreement in a single pass, with cross-referencing of clauses and definitions
- Codebase analysis: Load hundreds of source files and understand architectural patterns, dependencies, and technical debt simultaneously
- Financial analysis: Process multiple 10-K filings, earnings transcripts, and analyst reports in one conversation
- Research synthesis: Combine 50+ academic papers and produce comprehensive literature reviews with accurate citations
For context, 1 million tokens is roughly equivalent to 750,000 words or about 3,000 pages of text. This is enough to hold entire textbooks, regulatory frameworks, or full-year financial reporting packages within a single conversation.
Developer and API Features
Beyond the model itself, Anthropic released several platform features designed to help Opus 4.6 perform at its best in production environments:
New API Capabilities
Adaptive Thinking
Claude decides when deeper reasoning is helpful rather than requiring a binary on/off choice. At default (high) effort, the model uses extended thinking selectively. Developers can adjust effort to control this behavior.
Effort Parameter
Four levels: low, medium, high (default), max. The /effort parameter controls how much reasoning the model applies. Low effort is ideal for classification and extraction. Max effort is for financial modeling, legal analysis, and complex coding.
Context Compaction
Automatically summarizes older context when conversations approach a configurable threshold. This lets long-running agents continue working without hitting context limits or losing critical information.
Agent Teams
In Claude Code, multiple agents can now work in parallel as a coordinated team. Best for independent, read-heavy tasks like codebase reviews where different agents can examine different parts simultaneously.
US-Only Inference
For workloads requiring data residency within the United States, this option ensures all inference runs on US-based infrastructure at 1.1x standard token pricing.
Office Integrations: Claude in Excel and PowerPoint
For knowledge workers, Opus 4.6 powers upgraded integrations with the tools teams already use. Claude in Excel now handles long-running and harder tasks with improved performance. It can plan before acting, ingest unstructured data and infer the right structure without guidance, and handle multi-step changes in one pass.
Claude in PowerPoint (research preview for Max, Team, and Enterprise plans) lets teams process data in Excel and then create presentations directly. Claude reads your layouts, fonts, and slide masters to stay on brand, whether building from a template or generating a full deck from a description.
For marketing teams building pitch decks, quarterly reports, or client deliverables, this combination of Excel + PowerPoint automation can cut hours of manual work. At Conversion System, we help teams build custom AI workflows around these integrations to maximize productivity across departments.
What This Means for Your Business AI Strategy
If you are evaluating AI tools for your organization or already running AI workflows, Opus 4.6 shifts the conversation in several important ways:
Agent Reliability Is No Longer Theoretical
The benchmark results and enterprise testimonials show that Opus 4.6 can sustain complex, multi-step agentic tasks across hours and hundreds of tool calls. This moves agentic AI from experimental to production-grade for many enterprise use cases.
Context Window Changes Document Workflows
The 1M token context with 76% retrieval accuracy at that scale means teams in banking, legal, and research can eliminate the "chunk and summarize" workaround that previously limited AI document analysis.
Cost Control Is Built In
The four-level effort parameter means you no longer pay for deep reasoning on simple tasks. A classification job at low effort costs a fraction of a financial analysis at max effort, even though both use the same model.
Safety Is a Business Feature
The lowest over-refusal rate of any Claude model combined with maintained alignment means teams can deploy Opus 4.6 in customer-facing applications with confidence that it will be both helpful and safe.
How to Get Started with Claude Opus 4.6
Here is a practical roadmap for evaluating and deploying Opus 4.6 in your organization:
Identify High-Value Use Cases
Start with tasks where the benchmark improvements matter most: agentic coding, long-document analysis, financial modeling, or research synthesis. Use our free AI audit to identify the best starting points.
Test Effort Levels
Run the same prompts at different effort levels (low, medium, high, max) to find the optimal cost-quality tradeoff for each workflow. Many tasks perform well at medium effort, saving significant API spend.
Enable Context Compaction for Agents
If you are building AI agents that run multi-step workflows, enable context compaction to prevent context window limits from interrupting long-running tasks.
Benchmark Against Your Current Model
Run a controlled A/B test comparing Opus 4.6 to your current model on production tasks. Measure accuracy, latency, cost, and user satisfaction. The GDPval-AA and Finance Agent results suggest Opus 4.6 will outperform on knowledge work tasks.
Scale Gradually
Move from pilot to production one workflow at a time. Monitor cost, quality, and user feedback. Use the effort parameter to control spend as you scale. Talk to our team to build a deployment roadmap.
Known Limitations and Trade-Offs
No model is perfect. Here are the trade-offs to be aware of when evaluating Opus 4.6:
- Overthinking on simple tasks: At default (high) effort, Opus 4.6 sometimes reasons more deeply than needed on straightforward requests. Anthropic recommends dialing effort to medium for routine tasks.
- MCP Atlas regression: Scaled tool use dropped from 62.3% (Opus 4.5) to 59.5%. Teams building agents that coordinate many tools simultaneously may need additional orchestration logic.
- Premium pricing for long context: Prompts exceeding 200K tokens trigger premium pricing ($10/$37.50 per million tokens vs. $5/$25). Budget accordingly for document-heavy workflows.
- SWE-bench near-parity: The 80.8% SWE-bench score is essentially flat versus Opus 4.5's 80.9%. Coding improvements appear concentrated in agentic terminal operations rather than isolated GitHub issue resolution.
- Visual reasoning gap: At 73.9% on MMMU Pro (without tools), Opus 4.6 trails Gemini 3 Pro's 81.0% and GPT-5.2's 79.5%. For vision-heavy workflows, consider multimodal alternatives.
Next Steps
Claude Opus 4.6 is a significant release that moves the frontier of what AI can do in production business environments. The combination of stronger reasoning, reliable long-context retrieval, agentic persistence, and built-in cost controls makes it the most practical foundation model for enterprise AI deployment as of February 2026.
If you are evaluating AI for your business, the best time to start is now. Use our free AI audit tool to benchmark your readiness, or schedule a strategy call with our team to identify where Claude Opus 4.6 can drive the most value for your organization.
Ready to Implement AI That Drives Revenue?
Conversion System helps businesses across SaaS, e-commerce, finance, and cannabis deploy AI systems that deliver measurable ROI. Claude Opus 4.6 is one of the many tools in our stack.
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