Claude models explained: choosing the best model for your use case
02:00 · July 24, 2026 · Claude Blog
Summary
This article offers practical guidance on choosing among Anthropic’s Claude model classes—Mythos, Fable, Opus, Sonnet, and Haiku—according to the demands of a given workload. It describes the capabilities and intended uses of each class, then outlines the main decision factors: task difficulty, latency requirements, access restrictions, and unit economics. The piece also presents an “advisor strategy” that pairs faster worker models with more capable reviewers and stresses the value of custom evaluations when standard benchmarks lose discriminative power at the frontier.
The default recommendation is to begin with the strongest generally available model and modulate performance and cost through effort level rather than by immediately selecting a smaller class. More capable models often complete tasks in fewer steps, which can reduce overall price-per-task even when the per-token rate is higher. Organizations facing strict latency or volume constraints can then test lower-tier models, while others may start at the lowest viable class and scale upward until quality thresholds are met.
Mythos represents the most advanced class, suited to complex coding, long-running agent workflows, and previously unsolved problems. It is offered in two variants of the same base model: Mythos itself, restricted to trusted organizations working with dual-use cybersecurity or biology content, and Fable, which adds safeguards for broader deployment. Opus targets reasoning-heavy enterprise work and performs strongly on benchmarks such as GDPval-AA and Terminal-Bench 2.1. In practice, Fable frequently shows advantages in creativity and writing despite comparable benchmark scores, so teams are advised to compare the two directly on their own tasks.
Sonnet serves as the versatile option for everyday and high-volume workloads, including sub-agents in multi-agent systems, while Haiku is positioned for high-frequency, latency-sensitive applications where cost is paramount. All classes are trained across similar domains; differences lie mainly in the difficulty of problems they can handle reliably and the associated speed and price trade-offs.
The advisor strategy improves efficiency by letting a lower-cost executor model consult a stronger model only when needed to review plans or results. On SWE-bench Pro, for example, Sonnet 5 paired with a Fable 5 advisor reaches within 10 percent of full Fable 5 performance at 63 percent of the cost. When benchmarks saturate, the article recommends custom evaluations drawn from real production tasks, complete with team-defined success criteria, to determine which model class meets requirements most effectively.
Why it matters
It offers highly actionable architectural strategies, such as the advisor pattern and custom evaluation frameworks, directly applicable to Dutch product teams building cost-effective and scalable AI solutions. The insights on data retention and model safety also align well with EU compliance standards.
