Claude Opus 4.6

Claude Opus 4.6

Quick answer: Claude Opus 4.6 is Anthropic's frontier model, released 5 February 2026. It is a hybrid reasoning model tuned for agentic coding, computer use, and professional knowledge work, and is the first Opus-class model with a 1M-token context window (beta). Anthropic reports 81.42% on SWE-bench Verified and state-of-the-art results on Terminal-Bench 2.0 and Humanity's Last Exam. It is a closed, API-only model (claude-opus-4-6).

At a Glance

Where Claude Opus 4.6 leads

• Frontier agentic coding — 81.42% on SWE-bench Verified, plus state-of-the-art Terminal-Bench 2.0.
• A 1M-token context window (beta) with strong long-context fidelity (76% on MRCR v2 8-needle at 1M).
• Professional knowledge work — 90.2% on BigLaw Bench and 53.0% on Humanity's Last Exam (with tools).

Where it lags

• Premium pricing — $5 / $25 per 1M, rising to $10 / $37.50 above 200k-token prompts — several times GPT-5's rate.
• Closed and API-only with no open weights to self-host, and the 1M window is still in beta.

Best for: teams that need the most reliable agentic coding and long-context professional work and will pay a premium for it.

What Claude Opus 4.6 Is

Claude Opus 4.6 is the most capable tier of Anthropic's Claude family at its February 2026 release, positioned as a daily driver for serious coding, long-horizon agentic tasks, and enterprise workflows. It is a hybrid reasoning model: it can answer quickly or engage extended thinking, and with "adaptive thinking" it decides for itself when deeper reasoning is worthwhile. Developers tune this through four effort levels — low, medium, high (default), and max — trading intelligence against speed and cost.

Its headline platform feature is a 1M-token context window (in beta on the Claude Developer Platform), a first for an Opus-class model, paired with outputs up to 128k tokens. Anthropic emphasises that the model not only holds more context but uses it better — citing a 76% score on the 8-needle 1M variant of MRCR v2, a long-context retrieval test where the earlier Sonnet 4.5 scored 18.5%. Context compaction on the API lets long-running agents summarise older context and keep working past the window limit.

For Benchgen, Opus 4.6 is an API-benchmarked model with no open weights, so its platform value comes from running it through Benchgen's evaluations via API and setting the verified result against its strong public coding and agent scores. The interesting question is not whether Opus 4.6 is capable in the abstract — it clearly is — but how reliably that capability converts into completed, domain-specific agent tasks across repeated trials.

Specifications

Pricing

Prompts above 200k tokens are billed at $10.00 / $37.50 per 1M. Source: Anthropic — Introducing Claude Opus 4.6.

Context Window

Claude Opus 4.6 has a 1M-token context window (in beta on the Claude Developer Platform) — roughly 1,500 pages of text in a single request — with a maximum output of 128k tokens. That is enough to hold an entire codebase or a large contract set at once, and Anthropic reports 76% on the 8-needle 1M variant of MRCR v2, a long-context retrieval test, indicating the model uses that window rather than merely storing it.

Public Benchmark Scores

Scores above are reported by Anthropic (some via named partners) and shown for context. They are not Benchgen measurements. Several figures depend on harness, effort level, and tool settings — see the source for methodology.

Claude Opus 4.6 vs Alternatives

For a builder, Opus 4.6 is the most expensive of the three but posts the highest SWE-bench Verified score and pairs a 1M-token window with the strongest long-context fidelity. The choice comes down to whether top-end coding reliability and professional-grade long-context work justify paying several times GPT-5's per-token rate. (Rival scores attributed to OpenAI and Google; not Benchgen measurements.)

How Claude Opus 4.6 Performs on Real Agent Tasks

Opus 4.6's public results lean heavily toward agentic and real-world signals rather than saturated multiple-choice tests: state of the art on Terminal-Bench 2.0, a leading score on Humanity's Last Exam, and partner testimonials describing it autonomously triaging issues, migrating multi-million-line codebases, and topping legal and finance evals. The 81.42% on SWE-bench Verified is a genuine real-world coding signal, and the 90.2% BigLaw Bench result points to strength on dense professional documents.

What those benchmarks cannot tell you is how Opus 4.6 behaves on a specific domain workflow, run many times, under a consistent rubric. A leaderboard number measures general capability; what agent builders actually need is reliable, repeated completion of the task in front of them — including how often the model fails, and why. That reliability-under-repetition view is what Benchgen is built to surface, and it is the dimension public benchmarks leave open.

Use Claude Opus 4.6 via API

from anthropic import Anthropic

client = Anthropic(api_key="YOUR_API_KEY")

message = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Summarize this contract clause..."}],
)
print(message.content[0].text)

Related Models & Benchmarks

• Compare: GPT-5 · Gemini 3 Pro · Grok 4 · Claude Sonnet 4.6
• Benchmarks: SWE-bench Verified

Frequently Asked Questions

Specs and scores sourced from Anthropic's Claude Opus 4.6 announcement. Third-party benchmark scores attributed inline to Anthropic. Benchgen scores, where shown, are produced on Benchgen infrastructure. Last updated 2026-06-17.