Anthropic
Claude Sonnet 5.5
claude-sonnet-5.5
Total Context
1M
Max Output
128K
Released
N/A
Input$2 / M tokens
Output$10 / M tokens
Claude 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.
Context Window
1M tokens
Maximum Output
128K tokens
Release Date
Apr 16, 2026
Modalities
Claude Opus 4.7 Pricing
| Input Price | Output Price | Cache Read | Cache Create 5m |
|---|---|---|---|
| $5/M | $25/M | $0.5/M | $6.25/M |
Claude Opus 4.7 API Capabilities
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Not supported
Attachments
Supported
Knowledge Base
2026-01-31
Endpoint Protocols
Completions API
Claude Opus 4.7 Model Highlights
Claude Opus 4.7 combines advanced software engineering, sustained multi-step execution and higher-fidelity vision for complex professional work.
Advanced Software Engineering
Tackles difficult coding tasks with stronger planning, implementation and follow-through than its predecessor.
Sustained Verified Execution
Maintains rigor on long-running tasks, follows instructions closely and devises checks before reporting completion.
High-Fidelity Vision
Processes images at higher resolution, improving interpretation of technical diagrams and visually detailed materials.
Claude Opus 4.7 Use Cases
Claude Opus 4.7 is suited to difficult coding, repository review and visual professional deliverables that benefit from careful verification.
Long-Running Coding Tasks
Work through difficult implementation plans over many steps, verify intermediate results and deliver tested changes.
Repository Review and Debugging
Inspect pull requests, logs and traces to identify subtle defects, explain causes and propose validated fixes.
Visual Professional Deliverables
Interpret detailed visual references and create polished interfaces, slides or documents aligned with the source material.
How to Use Claude Opus 4.7 via the TokenHub API
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)Claude Opus 4.7 Benchmarks
| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 42.7 |
| Artificial Analysis Coding Index | Artificial Analysis software task aggregate | 53.1 |
| 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
Claude Opus 4.7 Comparisons
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.
Ready to use Claude Opus 4.7?
Use one API key to access Claude Opus 4.7 and more AI models through TokenHub.
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