All AI Barometers Observation Level: 🟡 – This week, AI Barometer #6 explores two complementary dimensions. On the one hand, a form of volume variability: the same system can reach the same correct result while generating very different quantities of tokens. On the other hand, a return to calm after the peak observed the previous […]
NeoMundi Observatory ran a full runtime protocol on Kimi K3 (Moonshot): 1,200 valid executions – 120 situations repeated 10 times across 12 families (factuality, reasoning, governance, medical, legal, cybersecurity, business operations, and more). This is not a ranking.This is not a judgment.This is a runtime observation. The objective is to make visible what happens during
Reading note — 15 October 2026. This campaign took place during a period of maintenance of the NeoMundi measurement instrument. Observations are retained as originally published; comparisons with adjacent periods must account for the instrument configuration in use at the time. The narrative and summary card report different aggregates: 90.10% normal regime versus 81.01% mean
Reading note — 15 October 2026. July 2026 exploratory campaigns. The two protocols below are presented in separate sections and must not be pooled. Scores depend on the measurement chain, evaluator configuration and coverage used at the time. Agreement between AI judges is not independent proof of factual truth, and a constant indicator does not
Reading note — 15 October 2026. This article presents NeoMundi’s applied research approach. References to NIST, ISO and the AI Act provide context; they do not imply endorsement, certification or proof of regulatory compliance. Historical comparisons require checking protocols, evaluator versions and coverage. Companies no longer need to be told that artificial intelligence carries risks.
Reading note — 15 October 2026. This publication reports a June–July 2026 campaign. Figures describe signals recorded by the measurement chain used at the time, not verified error rates or a general assessment of model reliability. Cross-period comparisons require matching protocols, evaluator versions and coverage; they must not be carried over automatically to a later
Reading note — 15 October 2026. This publication reports a June–July 2026 campaign. Figures describe signals recorded by the measurement chain used at the time, not verified error rates or a general assessment of model reliability. Cross-period comparisons require matching protocols, evaluator versions and coverage; they must not be carried over automatically to a later
Reading note — 15 October 2026. This publication reports a June–July 2026 campaign. Figures describe signals recorded by the measurement chain used at the time, not verified error rates or a general assessment of model reliability. Cross-period comparisons require matching protocols, evaluator versions and coverage; they must not be carried over automatically to a later
All AI Barometers Building a baseline To produce this first Barometer, we first established a public baseline. It was built from 3 execution waves × 12 de-identified systems × 4 fixed questions × 100 repetitions, representing 14,400 completed observations. This baseline is the reference for this June 2026 series. Later comparisons require an equivalent measurement
This publication concerns a 2026 campaign and the measurement configuration used at that time. Its results are not automatically comparable with those of later instrument versions. 1. For AI systems, price, stability and factual accuracy do not always move together We measured 12 major generative AI systems using the same protocol, in order to compare
