Claude Opus 4.6

claude-opus-4.6

Claude Opus 4.6 is described as a very strong Anthropic model for complicated requests that require concrete planning and polished execution. Official materials emphasize following through: breaking work into steps, executing, and delivering refined results. It is best framed as a high-autonomy model for complex professional work.

Total Context

1Mtokens

Max Output

128Ktokens

Released

Feb 5, 2026

Modalities

Claude Opus 4.6 Price

Input PriceOutput PriceCache ReadCache Create 5m
$5/M$25/M$0.5/M$6.25/M

Claude Opus 4.6 API

anthropicPOST /v1/messages

Claude Opus 4.6 Benchmark

37.8

/100

Artificial Analysis Intelligence Index

Artificial Analysis broad capability aggregate

Index score

47.6

/100

Artificial Analysis Coding Index

Artificial Analysis software task aggregate

Index score

Knowledge & Reasoning

GPQA

Advanced science problem solving

84%

HLE

Broad expert-level exam set

18.6%

Coding & Engineering

SciCode

Scientific coding challenges

45.7%

Terminal-Bench Hard

Hard terminal task execution

48.5%

Instruction Following & Agent Tasks

IFBench

Prompt constraint adherence

44.6%

AA-LCR

Long-context reasoning

58.3%

τ²-Bench

Agent workflow tasks

84.8%

Metrics sourced from Artificial Analysis

Media and Discussions

Selected public videos and posts related to this model.

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Frequently asked questions about Claude Opus 4.6

Understand what Claude Opus 4.6 is, its best uses, distinguishing strengths, practical tradeoffs, and safe TokenHub integration guidance.

What is the intended positioning of Claude Opus 4.6?+

Claude Opus 4.6 is a powerful Opus model for complex coding, research, and agentic professional workflows. It remains a defined model generation, but newer models in the same family may be preferable for new evaluations.

Is Claude Opus 4.6 a good choice for difficult software-engineering tasks?+

Best-fit scenarios include difficult software-engineering tasks, deep research and evidence synthesis, and professional document and decision analysis. Test representative inputs and define measurable acceptance criteria before production.

Which strengths distinguish Claude Opus 4.6 from nearby options?+

Key strengths include strong reasoning on difficult problems, reliable execution of multi-step agent workflows, and effective use of tools and function calls. This combination is especially useful for deep research and evidence synthesis.

Which workloads are a poor fit for Claude Opus 4.6?+

Consider another model when the project can benefit from a newer Opus generation, the workload is simple enough for a smaller model, or the workflow cannot include human review for important decisions. Run generated code through tests, security checks, and human review before merging or deployment.

Which TokenHub details matter when configuring Claude Opus 4.6?+

In TokenHub, select the exact model identifier displayed for Claude Opus 4.6, use the endpoint documented for your account, and authenticate with your TokenHub credentials. Check the TokenHub model page for the available Claude features, context limits, tool support, and current model status for your account.