Artificial Analysis evaluated Grok 4.7 at xhigh effort and scored it 46 on its Intelligence Index, two points above Grok 4.6 at high effort. The more pronounced change appeared in the firm's Coding Agent Index, where Grok Build with Grok 4.7 scored 56, up nine points from Grok 4.6 at xhigh. In the snapshot published on launch day, that placed the combination fourth among models running in their native coding‑agent harnesses, behind Claude Fable 5.1, GPT‑6 Astra and Claude Opus 5.
The coding index is not one synthetic question set. Artificial Analysis says its current methodology equally weights DeepSWE v1.1, Terminal‑Bench 4.0 and SWE‑Atlas‑QnA, covering implementation, terminal work and technical repository questions. Grok 4.7 with Grok Build improved across all three components in the published comparison: 73% versus 65% on DeepSWE, 33% versus 18% on Terminal‑Bench, and 63% versus 58% on SWE‑Atlas‑QnA.
The gain was narrower outside those agentic workloads. Artificial Analysis reported that Grok 4.7 broadly matched Grok 4.6 on the other Intelligence Index tasks, with improvements on Terminal‑Bench and GDP.pdf and regressions on AA‑LCR and AutomationBench‑AA. Its separate factuality measure compared Grok 4.7 at xhigh with Grok 4.6 at high: the hallucination rate was 29% versus 34%, while accuracy was nearly unchanged at 47% versus 48%. That combination supports a focused reading of the release: the clearest evidence is better execution on long‑horizon tasks, not a universal capability leap.
There is also an important harness caveat. The Intelligence Index standardizes how models are tested, while the Coding Agent Index uses each model inside a native agent; Grok 4.7 was paired with Grok Build. The coding result therefore measures the model‑and‑agent system that a user might actually run, but it cannot isolate how much of the improvement came from the model, the harness or their interaction.