Claude Opus 4.8
Claude Opus 4.8 by Anthropic: hybrid reasoning model with adaptive thinking, effort control, and a 1M token context window, built for coding and AI agents.
Model Overview
Capabilities, design details, and architectural traits
Claude Opus 4.8 - Hybrid Reasoning Model Built for Sustained Agentic Work
Claude Opus 4.8 is Anthropic's most capable publicly available model prior to the Fable launch, officially described as a hybrid reasoning model built for serious coding and AI agents. Its defining design principle is adaptive thinking - the model automatically adjusts how much reasoning it applies based on task complexity, spending more computation on harder problems and responding faster to simpler ones. This is always on and user-controllable via effort settings (including an xhigh tier for maximum computation).
What Distinguishes Opus 4.8 From Its Predecessors
| Trait | What it means for Opus 4.8 specifically |
|---|---|
| Adaptive thinking, always on | Automatically scales reasoning depth per task; users can also set effort explicitly from low to xhigh |
| Four times fewer unremarked code flaws | Officially documented: Opus 4.8 is around four times less likely than Opus 4.7 to allow flaws in its own code to pass without comment |
| Alignment scores matching Mythos Preview | Evaluation-confirmed rates of deceptive behavior and cooperation with misuse are similar to Claude Mythos Preview, Anthropic's most aligned model at launch |
| Fast mode at 2.5x speed | Fast mode runs at 2.5 times the speed of fast mode on previous Opus versions |
| 1M token context window | Supports a 1 million token context window for sustained, long-running sessions |
| Fallback target for Fable 5 safety classifiers | When Claude Fable 5 declines a flagged request, the API automatically reroutes to Opus 4.8 - a documented platform role unique to this model |
Honesty as a Documented Behavioral Shift
Anthropically's official announcement frames honesty as the most prominent improvement in Opus 4.8 over 4.7. The model is explicitly trained to flag uncertainties rather than assert unsupported progress - particularly relevant in agentic coding sessions where overconfident claims about task completion are a documented failure mode in prior models.
Benchmark Performance
Independent evaluations · Artificial Analysis
Accuracy & Capability Details
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| Model Details | |||
|---|---|---|---|
| General Info | |||
| Provider | Anthropic | Anthropic | Anthropic |
| Release Date | May 28, 2026 | September 22, 2026 | September 28, 2026 |
| Knowledge Cutoff | - | - | Jun 2026 |
| Context & Limits | |||
| Context Window | 1M | 1M | 1M |
| Pricing (per 1M tokens) | |||
| Input Pricing | $5 | $4 | $2 Best Input Pricing |
| Output Pricing | $25 | $20 | $10 Best Output Pricing |
| Modalities | |||
| Inputs | textimagefile | textimagefile | textimagefile |
| Outputs | text | text | text |
| Benchmarks (0-100) | |||
| Intelligence Index | 41.8 | 57.6 Best Intelligence Index | 56.0 |
| Coding Index | 74.3 | - | - |
| Agentic Index | 41.9 | - | - |
Humanity's Last Exam
Extremely difficult logical reasoning and knowledge.
Long Context Reasoning
Logical reasoning over long context windows.
SciCode Benchmark
Scientific coding and mathematical modeling.
Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.
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