Anthropic's near-frontier reasoning model with a per-request effort dial for agentic coding at controllable cost.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Opus 5 Wins: Agentic Coding Per Dollar
Benchmarks
Independent Benchmarks: Terminal-Bench, ARC-AGI 3 and Intelligence Index
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
Claude Opus 5 vs Claude Fable 5: Capability Ceiling or Cost Control?
Opus 5 and Fable 5 are natural alternatives: both are Anthropic frontier-tier models with a 1M-token context window, adaptive thinking, and image/file input, aimed at autonomous coding and knowledge work. Anthropic positions Opus 5 as coming close to Fable 5's intelligence at roughly half the per-task cost, and Fable 5's safety classifiers actually fall back to Opus 5 when a request is blocked. The practical decision is whether your workload needs Fable 5's absolute capability ceiling for long-running autonomous tasks, or whether Opus 5's near-frontier quality at lower cost and burn rate is enough.
Choose Claude Opus 5 when you want near-frontier reasoning and agentic-coding quality with materially lower per-task cost, a later May 2026 knowledge cutoff, fewer spurious refusals, and fine-grained control over spend via the effort dial—ideal for regular advanced coding and enterprise workflows. Choose Claude Fable 5 when you need the absolute capability ceiling for long-running, ambiguous, multi-day autonomous tasks and can absorb roughly double the cost and its higher token burn rate. For most teams, Opus 5 is the more economical default.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous agentic coding
Opus 5 is Anthropic's recommended starting model for complex agentic coding. It more than doubles Opus 4.8 on Frontier-Bench and reaches 89.1% on Terminal-Bench 2.1 (Artificial Analysis, max effort), with strong tool calling and beta mid-conversation tool changes that preserve the prompt cache. This suits multi-file repository agents in tools like Cursor or Devin. Tune effort per task to control token spend, and benchmark effort levels since max is not always optimal.
Difficult debugging and root-cause analysis
Anthropic and independent write-ups highlight Opus 5's strength on hard debugging and root-cause tasks, where it self-verifies and recovers from errors without intervention. Its adaptive thinking and later May 2026 knowledge cutoff help with recent libraries and APIs. This fits production incident triage and fixing subtle regressions across large codebases. For simple, mechanical fixes, a cheaper model is more economical.
Whole-codebase and long-document analysis
The 1M-token context window—default and maximum, with no input surcharge for long contexts—lets Opus 5 reason over an entire mid-sized codebase or large document corpus in a single request. Prompt caching reduces the cost of repeated stable context. This suits architecture review, cross-file refactor planning, and large-corpus knowledge work. A 1M window is capacity, not an instruction to fill it; irrelevant context still degrades accuracy, so structure prompts and consider RAG for very large retrieval needs.
Enterprise knowledge work
Opus 5 leads on knowledge-work evaluations like GDPval-AA and is positioned as a daily driver for serious business tasks, with the most aligned Opus behavior and roughly 85% fewer spurious refusals than prior models. This fits research synthesis, analysis, and document-heavy enterprise workflows where reliability and fewer refusals matter. The effort dial lets teams keep routine queries cheap while reserving deeper reasoning for high-stakes tasks.