Claude 4.8

Positioning Overview

claude-opus-4-8 is the Opus model in the Claude 4.8 family, with text output, positioned for long-horizon Agents, coding, and deep reasoning tasks in enterprise workflows. Compared with previous generations, its improvements are concentrated in task consistency across long-running tasks and multi-turn interactions: workflows with complex state where single-step errors tend to accumulate show its value most clearly. It is suitable for developer evaluation when building automated Agents, repository-level code engineering, legal and financial document review, or multi-document analysis products. It is called as text through the unified HiAPI.ai interface.

Core Highlights

• Maintains consistency across long tasks — reduces accumulation of small errors in multi-turn, long-state workflows, making it suitable for automated processes where failure cost grows with the number of steps.
• Improved agent and browser operation capabilities — computer use and browser agent performance are markedly improved over previous generations, scoring 84% on Online-Mind2Web.
• End-to-end completion of complex Agent tasks — on the Super-Agent benchmark, it is the only model to complete all test cases end-to-end, reducing the need for manual intervention.
• Outstanding performance in substantive legal document review — it achieves the highest score on the Legal Agent Benchmark and breaks past 10% under the all-pass criterion for the first time.

Capability Description

Long-horizon Agent execution: maintains coherent execution across multi-turn, long-state task flows, suitable for products that need to chain dozens of operations through a state machine or orchestration framework, reducing mid-course deviation and rework.

Code engineering and refactoring: supports repository-level code refactoring and long coding sessions, and can be used to turn requirement descriptions and defect reports into committable code changes and review comments.

Multi-document analysis and review: can cross-compare and analyze multiple long documents such as financial reports, regulatory filings, and legal materials, outputting structured conclusions for downstream processes.

Computer use and browser agents: possesses browser-operation agent capabilities and can handle automation tasks involving information retrieval and page interaction as the execution-layer component of Agent products.

Enterprise workflow automation: oriented toward translation, deep research, slide generation, and analytical Agent scenarios, integrating with existing business systems through text output.

Applicable Scenarios

• Repository-level refactoring Agent for software teams — input a code repository and refactoring goals, produce reviewable change plans and explanations for automated code cleanup before continuous integration.
• Multi-document cross-analysis for researchers — input multiple reports and materials, produce comparison conclusions and key point summaries for drafting deep research reports.
• Legal document review for legal teams — input contracts or legal documents, produce clause issues and review comments for preliminary screening before manual review.
• Financial and compliance material review — input financial reports and compliance documents, produce summaries of key metrics and risk points for daily review by investment research and compliance teams.
• Enterprise Agent product integration — input user instructions and business context, produce text results for product features such as translation, slide generation, and research assistants.

Limitations and Selection Advice

The main limitation is that it is a moderate improvement over previous generations, with no significant gain in general reasoning ability; if the task is mainly single-turn Q&A or lightweight classification, a lighter model is more suitable. Its advantages are concentrated in long-task consistency, Agent reliability, and document review. Most benchmarks come from official or partner sources, so cross-comparability is limited; selection should be validated against your own task set. Billing is per token, and actual cost should be evaluated based on usage; version lifecycle and available regions also require ongoing attention.