Claude Opus 4.6 is Anthropic’s latest Opus-class model, released February 5, 2026. It focuses on sustained, agentic intelligence for real knowledge work: improved planning and multi-step execution, stronger code review and debugging across large repositories, and a first-in-class Opus‑variant with a 1M‑token context window in beta. Anthropic pairs these capabilities with new API controls—effort, adaptive thinking, and context compaction—plus product integrations that extend the model into everyday work tools. The result is a model intended to run longer, reason deeper, and coordinate multi-agent workflows with predictable trade-offs for latency, cost, and safety.
What’s new in Claude Opus 4.6
Opus 4.6 combines algorithmic and product-level changes designed to make long-horizon reasoning and autonomous agent work practical for teams. At the model level, Anthropic improved planning and sustained reasoning: Opus 4.6 more reliably breaks ambitious tasks into concrete steps, stays productive over extended sessions, and revisits its chain-of-thought to reduce errors on complex problems. For developers this manifests as improved code review, debugging, and the ability to navigate and edit larger codebases than prior models handled comfortably. The release also introduces a beta 1M‑token context variant—an important milestone for workflows that need to hold massive document sets or entire code repositories in scope—and increases maximum single-response length to 128k output tokens, enabling long, single-request reports or diffs.
On the platform side, Anthropic exposed behavioral controls that let teams tune the model’s thinking. The new effort parameter (low, medium, high, max) gives a simple cost/latency vs. depth trade-off; adaptive thinking lets the model decide when deeper, costlier reasoning is warranted rather than forcing a global thinking mode; and context compaction (beta) summarizes and replaces older context automatically when a conversation approaches a configured threshold so agents can continue running without hitting token limits. There are also residency and pricing options: US-only inference at 1.1× token pricing for domestic workloads, and premium pricing for very large prompt sizes in the 1M-token tier (Anthropic states premium rates apply for inputs above 200k tokens). Taken together these capabilities and controls move Opus 4.6 from being an exceptional single-turn assistant toward a configurable, long-running collaborator for engineers and knowledge workers.
Performance and benchmark evidence
Anthropic presents Opus 4.6 as a frontier leader across benchmarks that matter for real work. On agentic coding evaluations—where models must plan, call tools, and execute—Opus 4.6 achieved the top score on Terminal‑Bench 2.0. It also leads in complex multidisciplinary reasoning tests such as Humanity’s Last Exam and ranks highly on BrowseComp, a metric of a model’s ability to locate hard-to-find information online. In economically oriented evaluations, Anthropic reports Opus 4.6 outperforming the next-best industry model in their comparison (OpenAI’s GPT‑5.2) on GDPval‑AA by roughly 144 Elo points, and exceeding its immediate predecessor, Opus 4.5, by about 190 Elo points—differences that translate into substantial practical gains on finance, legal, and other knowledge tasks.
Long-context retrieval is highlighted as a qualitative improvement: on the 8‑needle 1M variant of MRCR v2 (a needle-in-a-haystack retrieval benchmark), Anthropic reports Opus 4.6 scoring 76% versus 18.5% for Sonnet 4.5 in their runs, indicating much better ability to identify scarce signals inside vast text. Across partner trials and internal evaluations, Opus 4.6 shows consistent uplifts in multilingual coding, root-cause analysis for software failures, cybersecurity tasks, and life-science reasoning. Anthropic also cites hands-on partner experiments—such as agentic cybersecurity investigations where Opus 4.6 produced the best outcome in 38 of 40 cases—underscoring the model’s practical effectiveness when tasked with long-running, multi-step workflows rather than isolated prompts. For teams, those benchmark improvements imply a larger set of work that can be automated or accelerated reliably.
Agentic workflows, coding, and product integrations
Opus 4.6 is built to support agentic patterns: multi-step tasks that require planning, tool calls, and stateful tracking across many turns. Claude Code introduces agent teams (research preview): multiple subagents that can run in parallel, coordinate autonomously, and be directly managed by humans where necessary. This design pattern suits large, read-heavy tasks—codebase review, multi-repo audits, or distributed data extraction—where independent subtasks can proceed concurrently while a coordinator agent synthesizes results and escalates blockers.
For API-based agent deployments, context compaction is pivotal: it automatically summarizes and replaces older content when the session nears configured capacity, allowing an agent to continue operating on fresh inputs without manual pruning. Adaptive thinking reduces wasted compute by letting the model choose when extended internal reasoning is worthwhile; the effort parameter remains an accessible manual control to reduce latency and cost on simpler tasks. These controls help balance autonomy and predictability: use high or max effort with adaptive thinking for deep planning or high‑stakes problems, and lower effort for routine, latency‑sensitive interactions.
Anthropic has also extended Opus 4.6 into common productivity tools. Claude in Excel now handles longer, more complex spreadsheet workflows—inferring structure from unstructured data, planning before acting, and making multi-step modifications in a single pass. Claude in PowerPoint (research preview) reads slide masters and layouts to generate brand-consistent decks from structured inputs or transformed spreadsheet outputs. Together these integrations enable end-to-end flows—data ingest and structuring in Excel, multi-document reasoning in Opus 4.6, and final deliverable generation in PowerPoint—reducing friction for teams that must go from raw information to polished artifacts.
Safety practices and cybersecurity considerations
Anthropic emphasizes that Opus 4.6’s capability advances were pursued alongside extensive safety work. According to the published system card and announcement, automated behavioral audits show Opus 4.6 has low rates of misaligned behaviors—deception, sycophancy, cooperation with misuse—and a lower rate of over‑refusals than prior Claude releases. Anthropic reports running their most comprehensive safety evaluation suite to date, adding tests for user wellbeing, more stringent refusal challenges, and new probes that examine surreptitious or covert harmful behavior. Complementing evaluation, the team applied interpretability methods to better understand internal failure modes and to help identify subtle behaviors that standard tests might miss.
Because Opus 4.6 demonstrates enhanced cybersecurity capabilities, Anthropic took a two-pronged approach: they developed six new cybersecurity probes to detect potentially harmful outputs, and they accelerated defensive uses of the model, employing it to find and patch vulnerabilities in open-source software. The stated rationale is practical: defenders should have access to strong AI tools in order to level the field against adversaries. Anthropic also notes that safeguards will keep evolving and that near-term measures could include real-time intervention to block abuse. For adopters this means coupling model capabilities with layered operational safeguards: human-in-the-loop review, privilege separation for agents touching production systems, monitoring and anomaly detection on agent actions, and the use of the system card and safety guidance when designing agent behaviors.
Practical guidance for adoption and rollout
Opus 4.6 is available on claude.ai, via the Claude API (model name claude-opus-4-6), and across major cloud platforms. Anthropic’s stated pricing keeps the standard tiers at $5/$25 per million tokens for typical use, with premium rates applied for very large prompts inside the 1M‑token beta tier (notably for inputs above 200k tokens). There is also a US-only inference option at 1.1× token pricing for organizations that require domestic residency.
If you plan a pilot, begin with high-value, bounded tasks that genuinely need longer context or agentic planning: large codebase refactors, legal or financial multi-document summaries, long-running spreadsheet automation, and multi-step research projects. Instrument agents so you can audit tool calls and intermediate outputs; add human escalation gates before material actions or deployments; and use the effort parameter and adaptive thinking to tune cost and latency. For long sessions, enable context compaction to maintain coherence without manual pruning. Combine model-based checks and unit tests with established engineering practices—code review, CI/CD gating, and access controls—before allowing agents to perform privileged operations. Finally, use Anthropic’s system card and safety documentation as a reference for expected failure modes and mitigation strategies; continue monitoring and iterate on guardrails as you scale adoption.
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