The Gap in Shared Understanding of LLM Capabilities: What "Supports" Means in Production

“Support” for LLM capabilities can differ between model cards, provider documentation, gateway catalogues, and real-world endpoint behavior, making direct testing essential for production use. In tests conducted through Eden AI across 44 provider routes for four model families, basic features such as streaming usage and most reasoning-effort requests worked reliably, but forced tool calls, strict JSON schemas, and image handling frequently produced either explicit errors or silent failures despite HTTP 200 responses. The study found that identical model weights can behave differently across providers because of varying versions, quantisation, serving stacks, and feature implementations, while model aliases and self-reported model identities may also obscure what is actually serving a request. It recommends automated capability tests that validate output structure rather than status codes, treat missing required behaviors as errors, pin tested routes or dated model versions where possible, use feature-compatible fallbacks, and log complete requests and responses to detect regressions.

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