Anthropic's flagship for long-horizon agents that flag their own mistakes and skip fewer required tool calls.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Opus 4.8 Separates Itself: Honesty in Long-Horizon Agentic Work
Benchmarks
Opus 4.8 on Coding, Terminal, and Knowledge-Work Benchmarks
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
Claude Opus 4.8 vs Claude Opus 4.7: What Actually Changes?
Opus 4.8 and Opus 4.7 are natural comparison points: they share the same 1M-token context window, 128K max output, January 2026 knowledge cutoff, and tool surface, and Anthropic priced 4.8 identically to its predecessor. The decision is not about new capabilities—Anthropic itself called 4.8 "a modest but tangible improvement." The real change is reliability. Opus 4.8 introduces adaptive thinking, fixes tool-calling and comment-verbosity issues reported on 4.7, and is roughly four times less likely to let flaws in its own code pass unremarked.
Choose Opus 4.8 when you run long, unattended agentic sessions, code migrations, or multi-step debugging where silent failures and skipped tool calls are expensive, and when honest self-flagging reduces human review. It is also the drop-in upgrade at the same token price. Choose Opus 4.7 only if you have already validated and pinned it in production and cannot spare the time to re-test adaptive thinking and the new default effort behavior, since migrating may shift per-call token consumption unless you set effort explicitly.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous long-horizon coding agents
Opus 4.8's core strength is sustaining agentic engineering unattended. It triggers required tool calls more reliably than Opus 4.7, handles compaction better so long traces stay on task, and is roughly four times less likely to let its own code flaws pass unremarked. For products running multi-hour agent sessions—codebase migrations, multi-stage debugging, asynchronous pipelines—this reduces silent failures and the human oversight burden. The 1M-token context lets large codebases fit in a single session.
Large-scale codebase migrations and refactors
With a 1M-token context window and 128K max output, Opus 4.8 can hold substantial portions of a codebase in context and emit long artifacts without truncation. Anthropic's dynamic workflows feature (in Claude Code) fans a hard problem across parallel subagents to handle migrations spanning hundreds of thousands of lines. Its 88.6% SWE-bench Verified and 69.2% SWE-bench Pro scores support multi-file changes that respect architecture rather than making local, brittle edits.
Professional knowledge work and analysis
Opus 4.8 maintains quality across very long outputs for drafting documents, building presentations, and analyzing data. Enterprise testers reported higher-quality, more information-dense analysis than prior Opus models, with a notable tendency to proactively flag issues in inputs and outputs that other models leave for users to catch. On GDPval-AA, which measures economically valuable knowledge work, Anthropic reports Opus 4.8 leading the frontier cluster—useful for high-value professional deliverables where errors are costly.
Agentic computer use
Opus 4.8 scores 83.4% on OSWorld-Verified, ahead of GPT-5.5 (78.7%) and Gemini 3.1 Pro (76.2%) as reported, making it a practical fit for agents that operate a computer interface across many steps. This capability underpins running parallel subagents that verify their own work before reporting back. As with all agentic deployments, treat honesty and self-flagging as a safety layer rather than a substitute for verification on high-stakes actions.