openai
POST /v1/chat/completionsClaude Opus 4.7
claude-opus-4.7Claude Opus 4.7 is described by Anthropic as a strong upgrade for advanced software engineering and difficult long-running tasks. Official messaging highlights the model’s ability to handle complicated work and verify its own outputs. It should be described around sustained engineering judgment rather than generic writing quality.
Total Context
1Mtokens
Max Output
128Ktokens
Released
Apr 16, 2026
Modalities
Claude Opus 4.7 Price
| Input Price | Output Price | Cache Read | Cache Create 5m |
|---|---|---|---|
| $5/M | $25/M | $0.5/M | $6.25/M |
Claude Opus 4.7 API
Claude Opus 4.7 Benchmark
42.7
/100
Artificial Analysis Intelligence Index
Artificial Analysis broad capability aggregate
Index score
53.1
/100
Artificial Analysis Coding Index
Artificial Analysis software task aggregate
Index score
Knowledge & Reasoning
GPQA
Advanced science problem solving
88.5%
HLE
Broad expert-level exam set
31.2%
Coding & Engineering
SciCode
Scientific coding challenges
50.1%
Terminal-Bench Hard
Hard terminal task execution
54.5%
Instruction Following & Agent Tasks
IFBench
Prompt constraint adherence
43.6%
AA-LCR
Long-context reasoning
67%
τ²-Bench
Agent workflow tasks
74.0%
Metrics sourced from Artificial Analysis
Model Comparison
Frequently asked questions about Claude Opus 4.7
Understand what Claude Opus 4.7 is, its best uses, distinguishing strengths, practical tradeoffs, and safe TokenHub integration guidance.
Where does Claude Opus 4.7 sit within its provider’s model family?+
Claude Opus 4.7 is a previous-generation Opus model built for difficult coding, strict instruction following, and long-running workflows. It remains a defined model generation, but newer models in the same family may be preferable for new evaluations.
Which production scenarios suit Claude Opus 4.7?+
Best-fit scenarios include difficult software-engineering tasks, long-running multi-step workflows, and professional document and decision analysis. Test representative inputs and define measurable acceptance criteria before production.
What makes Claude Opus 4.7 stand out for long-running multi-step workflows?+
Key strengths include strong coding performance, strict instruction following, and strong handling of long context. This combination is especially useful for long-running multi-step workflows.
What tradeoffs should developers consider with Claude Opus 4.7?+
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.
How can a team safely start using Claude Opus 4.7 on TokenHub?+
In TokenHub, select the exact model identifier displayed for Claude Opus 4.7, 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.
Media and Discussions
Selected public videos and posts related to this model.
X (Twitter)
Reddit
YouTube