
What happened
Anthropic released Claude Opus 5, its new flagship model, priced at $5 per million input tokens and $25 per million output tokens—half the cost of Fable 5 ($10 and $50 respectively). Opus 5 becomes the default model on Claude Max and the most capable model on Claude Pro, with a 1 million-token context window and a new Fast Mode that increases speed by 2.5x while doubling the price.
Why it matters
Opus 5 outperforms both Fable 5 and GPT-5.6 Sol on several key benchmarks: it scores 43.3% on agentic terminal coding (versus Fable 5's 33.7%), reaches an Elo score of 1,861 on knowledge work (versus Fable 5's 1,747), and achieves 30.2% on ARC-AGI-3 problem-solving—roughly four times higher than GPT-5.6 Sol's 7.8%. The move directly addresses pricing pressure from competitors while offering comparable or better performance at significantly lower cost.
What to watch
Token rates alone understate the full cost picture; token efficiency varies by effort level, and users can trade performance against token use through five effort settings (low, medium, high, xhigh, max). Anthropic says Opus 5 offers better value than its predecessor at every effort level, and recommends the low and medium settings for general tasks, while xhigh remains recommended for coding and agentic work.
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Anthropic's release of Claude Opus 5 represents a direct response to competitive and pricing pressure in the large language model market. The body notes that Anthropic is specifically responding to pricing pressure from GPT-5.6 Sol and Chinese competitors, and designed Opus 5 to close the price-performance gap with Fable 5, which commands a significantly higher cost per token. By pricing Opus 5 at exactly half the input and output token rates of Fable 5 while delivering equal or superior performance on multiple benchmarks, Anthropic is repositioning its product lineup to offer better value without sacrificing capability.
However, the article emphasizes that raw token rates mask a more complex cost story. Token efficiency—the actual number of tokens consumed to complete a task—varies substantially across models and effort levels. The body notes that Opus 4.7 ended up costing 30 to 40 percent more per task than Opus 4.6 despite identical base rates, and a similar pattern appeared with Claude Sonnet 5. This means users cannot assume that Opus 5's lower per-token price automatically translates to lower per-task cost. The five effort levels (low, medium, high, xhigh, max) allow users to make explicit performance-versus-efficiency trade-offs; Anthropic reports that Opus 5 achieves better value at every effort level compared to its predecessor, and recommends users start with low or medium for general work and xhigh for specialized coding tasks.
Opus 5's benchmark performance tells a nuanced story: it leads decisively on coding and novel problem-solving (the ARC-AGI-3 result of 30.2%—nearly 4× higher than GPT-5.6 Sol—is flagged by the body as a notable outlier with unclear real-world implications), and surpasses Fable 5 on knowledge work. However, the article makes clear that Opus 5 does not win everywhere—Fable 5 and Mythos 5 retain advantages in health and legal domains, and GPT-5.6 Sol leads on the DeepSWE v1.1 coding benchmark. The release also includes technical improvements: Opus 5 can now build its own tools to solve problems (the body describes it writing a computer vision pipeline when direct image viewing was blocked), and its safety classifiers trigger roughly 85 percent less often than Fable 5's, a response to Fable 5's heavy criticism for excessive interventions.
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