Claude Opus 5 の単価総覧と、Claude Code で旧モデル「Opus 4.8」を指定する方法This article provides a comprehensive breakdown of Claude Opus 5 pricing and…

Anthropic's release of Claude Opus 5 has renewed a familiar question for developers: how much does the flagship model actually cost to run at scale, and can teams still fall back to a previous generation when they need it? This article walks through the pricing structure reported for Opus 5 and outlines how to explicitly pin Claude Code to the earlier Opus 4.8 model, a workflow that matters for anyone balancing budgets against consistent, predictable model behavior.

Like earlier Claude releases, Opus 5 is billed on a per-token basis, with separate rates for input and output tokens typically quoted per million tokens. Output tokens usually carry a higher rate than input tokens, reflecting the greater compute cost of generation. Flagship Opus-tier models have historically sat at the top of Anthropic's price list, above the mid-range Sonnet and the lightweight Haiku tiers, so teams evaluating Opus 5 should expect a premium relative to those alternatives. Exact figures depend on Anthropic's published rate card at the time of use, and readers should confirm current numbers against the official pricing page before committing to production estimates.

Beyond the headline input and output rates, several mechanisms can materially change the effective cost. Prompt caching lets repeated context, such as long system prompts or reference documents, be stored and reused at a reduced rate, which appears especially valuable for agentic workloads that resend similar context across many turns. Batch processing, where requests are submitted asynchronously and returned within a longer window, has generally been offered at a discount compared with real-time calls. Long-context requests may also be priced differently once they exceed certain token thresholds. Taken together, these factors mean the sticker price of a model is only a starting point; real spend is shaped heavily by how an application structures its prompts and traffic.

The second focus of the article is practical model selection inside Claude Code, Anthropic's command-line and IDE-integrated coding agent. When a new default model ships, Claude Code will typically route requests to it automatically. Developers who prefer the older Opus 4.8, whether for cost reasons, for reproducibility, or because they have validated a workflow against its specific behavior, can override this. Model selection is generally handled either through the in-session model command, through a configuration setting or environment variable, or by passing an explicit model identifier when invoking the tool. Pinning to a known model string helps ensure that automated pipelines and evaluation suites do not silently shift behavior when a new default rolls out.

There are sound reasons to hold onto a previous generation. Newer models can change formatting tendencies, refusal boundaries, or reasoning styles in ways that ripple through downstream parsing and testing. Teams with regression suites tuned to a particular version often prefer to migrate deliberately rather than automatically. Cost is another driver: if Opus 4.8 delivers acceptable quality for a given task at a lower or more familiar price, defaulting to it can reduce spend. That said, Anthropic periodically deprecates and retires older snapshots, so any dependency on a legacy model should be treated as temporary and monitored against the company's deprecation notices.

It helps to understand the surrounding context. Anthropic uses dated or versioned model identifiers so that a specific snapshot can be targeted precisely, a convention shared by other providers such as OpenAI and Google, both of which also expose model-pinning and tiered pricing. The broader industry pattern is a ladder of models at different price and capability points, paired with cost-control features like caching and batching, so that developers can match each task to an appropriate tier rather than routing everything to the most expensive option. Tools such as usage dashboards, token counters, and spending limits within the Anthropic console are the practical companions to this strategy.

For teams adopting Opus 5, the sensible approach is likely to benchmark it against Opus 4.8 and the Sonnet tier on representative tasks, measure both quality and token consumption, and then decide where the flagship is justified. Keeping the ability to pin a specific model gives that process a safety valve, and understanding the full pricing structure, not just the per-token headline, is what ultimately determines the bill.