claude mythos 5 Key Model Features

claude mythos 5 is designed for ambitious, long-running projects that previous models couldn't sustain. It can operate in an agent harness like claude code, planning across multiple stages and checking its own work for days at a time. This class of model is proactive, thorough, and capable of delegating to sub-agents, making it ideal for the most difficult knowledge work that requires minimal human oversight during multi-stage execution.

The claude mythos 5 model is priced at $10 per million input tokens and $50 per million output tokens. This reflects a significant cost reduction compared to previous preview versions. Additionally, users can benefit from a 90% discount on input tokens through prompt caching. For specific workloads requiring residency in the United States, US-only inference is available at 1.1x pricing for both input and output token consumption.

Yes, vision is a core strength of the claude mythos 5 class. It can interpret complex diagrams, charts, and nested tables within files like PDFs. In technical benchmarks, it demonstrated the ability to rebuild web apps from screenshots alone and played games like Pokemon FireRed using only raw visual input without any navigation aids. This makes it highly effective for document-heavy fields like finance, legal, and architectural analytics.

To ensure safety, the claude mythos 5 generation includes robust classifiers for biology, chemistry, and cybersecurity. If a request is flagged in these domains, it automatically routes to the opus 4.8 model to prevent potential misuse in creating cyberattacks or bioweapons. This fallback system ensures a safe experience while maintaining high performance. API users only pay for the specific model used, avoiding higher costs for rerouted tasks.

Absolutely. claude mythos 5 is the most capable model for large-scale software engineering. It excels at codebase-wide migrations, such as the 50-million-line Ruby migration performed for Stripe. The model writes its own tests, implements high-fidelity designs, and uses its vision capabilities to verify visual outputs against original project goals. It handles complex, multi-day autonomous sessions with a high degree of reliability and focus.

Using the mythos class models requires a 30-day data retention policy for safety monitoring. This data is not used to train new models or for any non-safety purposes. It helps defend against novel attacks and allows developers to identify and reduce false positives in safety classifiers. GPTProto.com adheres to these standards to ensure that usage remains compliant with frontier safety regulations while protecting sensitive information.