Claude Sonnet 5.5

Anthropic publishes no config.json for Claude models - no architecture file exists to read. The four KV-geometry fields are UNRESOLVABLE-from-primary-source and are left null deliberately rather than filled from third-party estimates. ARCHITECTURE UNDISCLOSED, same as every Claude model: no parameter count, layer count, head geometry, or attention type is published, so all structural fields are null and none are estimated. DOCUMENTED: 1M-token context; 128K max output; adaptive thinking on by default; default effort high; knowledge cutoff Jun 2026; $2 input / $10 output per MTok; cache write $2.50/MTok (5m) and $4/MTok (1h); cache hits $0.20/MTok, which for this model is the standard 0.1x base-input multiplier. The 0.1x versus 0.05x cache-hit multiplier is a real economic difference between Claude tiers and is worth comparing directly when choosing a model for high-reuse-prefix workloads. SERVICEABILITY JOIN (added 2026-10-03; the architecture x engine matrix this record was missing, with a citation per cell in docs/13-model-compatibility.md): FAMILY=undisclosed. NO KERNEL CLASS CAN BE DERIVED FOR THIS CHECKPOINT, and that is the finding rather than a gap in the table. architecture is 'other' in this record because the vendor publishes no parameter count, no layer count, no head geometry and no attention type; every structural field is null and none is estimated. The correct cell in docs/13-model-compatibility.md is therefore UNKNOWN for every engine column, and it is unknown for a stronger reason than silence elsewhere in this document: a closed model is not served by any engine in data/engines/ at all. Every engine record here is an open-source runtime that loads a checkpoint from a hub or a local path; a closed API model is reached over HTTP and its architecture is whatever the operator cannot see. So the decision-relevant content of this record is not an engine join but the documented cost shape, which the record already carries: context window, max output, cache-write and cache-hit pricing, and reasoning-effort controls. ONE THING THE JOIN DOES ESTABLISH, and it is a real constraint: for an API-served model the engine-side kernel question is not merely unknown, it is MOOT. There is no local KV cache, no paged-attention block manager, no sliding-window layer accounting and no MLA latent to store, because none of that runs on the operator's hardware. Any comparison of the models in this document against this one is a comparison of price and context, not of kernels. Do not fill these cells by analogy with the neighbouring open-weight records. CONTEXT_SCALING_METHOD (none-verified): closed weights, no public config.json; 1M context published, scaling method not documented.