PERFORMANCE Signal 517
Claude reports degraded performance across several models and services
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Illustration only Photo by Agê Barros on Unsplash
Users of Claude API and related tools may experience higher error rates while Anthropic investigates the degraded performance.
Elevated errors on requests to multiple Claude models can disrupt applications that depend on the service for generative AI tasks. Until the investigation concludes, developers may need to implement retry logic or consider alternative providers to maintain reliability.
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Degraded performance affects Claude Mythos 5, Claude Fable 5, Claude Opus 5, Claude Sonnet 5, Claude Haiku 4.5, and other Claude models.
The incident impacts claude.ai, Claude API (api.anthropic.com), Claude Code, and Claude Cowork.
Anthropic is investigating elevated errors on requests to these models and will provide an update as soon as possible.
THE READ
What the cluster adds up to.
Claude status indicates degraded performance for several of its models, including Mythos 5, Fable 5, Opus 5, Sonnet 5, and Haiku 4.5. The issue also extends to other Claude models beyond those explicitly named. Anthropic confirmed that they are investigating elevated errors on requests to these models. The notice was posted on their status page, indicating an active investigation.
Developers using the Claude API or related tools may encounter higher error rates when making requests. This can increase the operational cost of applications that rely on consistent model responses. Teams might need to add retry mechanisms, fallback logic, or monitor error rates more closely. Such adjustments add development overhead and could affect latency if retries are attempted.
Applications that require low-latency, high-reliability interactions with Claude may see degraded user experience. Services such as claude.ai, Claude Code, and Claude Cowork could become temporarily unreliable during the incident. Until Anthropic resolves the issue and provides an update, the affected services may not meet expected performance thresholds. Users should consider temporary workarounds or alternative models if available.
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