Anthropic
Claude Sonnet 5.5
Total Context
1M
Max Output
128K
Released
N/A
claude-opus-4.6Claude 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.
Context Window
1M tokens
Maximum Output
128K tokens
Release Date
Feb 5, 2026
Modalities
| Input Price | Output Price | Cache Read | Cache Create 5m |
|---|---|---|---|
| $5/M | $25/M | $0.5/M | $6.25/M |
Reasoning
Tool calling
Temperature parameter
Attachments
Knowledge Base
Endpoint Protocols
Claude Opus 4.6 combines repository-scale software engineering, long-context analysis and adaptive reasoning for demanding agentic and professional work.
Navigates large codebases, plans coordinated changes and catches mistakes through stronger review and debugging.
Tracks and retrieves information across very large inputs with less context drift, then reasons over the recovered evidence.
Adjusts reasoning effort to task difficulty while sustaining planning and execution over extended agent workflows.
Claude Opus 4.6 is suited to complex codebase repair, multi-source research and professional document or data workflows.
Explore unfamiliar services, isolate the root cause of a failure and implement coordinated fixes with tests.
Search across extensive material, reconcile conflicting evidence and produce a structured, traceable analysis.
Analyze financial or operational data and turn findings into organized documents, spreadsheets or presentation content.
curl 'https://us-api.tokenhub.com/v1/messages' \
-X 'POST' \
-H "Authorization: Bearer $TOKENHUB_API_KEY"| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 37.8 |
| Artificial Analysis Coding Index | Artificial Analysis software task aggregate | 47.6 |
| 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
Understand what Claude Opus 4.6 is, its best uses, distinguishing strengths, practical tradeoffs, and safe TokenHub integration guidance.
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.
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.
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.
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.
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.
Use one API key to access Claude Opus 4.6 and more AI models through TokenHub.
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