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The striking detail is the cost-efficiency: at US$0.78 per Intelligence Index Task, Hy4 delivers Grok 4.7-level intelligence at a tenth of the price, which shifts the calculus for teams running high-volume agentic workloads. Developers building coding agents should weigh it against Claude Haiku 5.5, which scores higher but costs nearly twice as much; the 89% hallucination rate makes it a weaker pick for knowledge-retrieval tasks. API availability and USD pricing put it in direct reach of Southeast Asian startups comparing China-sourced models.

官方账号Artificial Analysis@ArtificialAnlys11 sourcesSource

The interesting signal is the trajectory comment: with heavy token budgets still unsaturated, frontier reasoning models may keep gaining from simply spending more compute per task. Practitioners tracking WeirdML results and reasoning-setting trade-offs can use this to decide which high-effort model configuration to benchmark next. The comparison between 1017 high and Grok 4.7 offers a concrete baseline for further testing.

Teortaxes@teortaxesTex2 sourcesSource

The result measures long-tail factual recall plus 3D generation quality in a single offline benchmark, a combination few evals test. Builders comparing frontier models on spatial reasoning and world knowledge can use the same sandboxed prompt setup for their own side-by-side runs. Claude Opus 5.5 (Anthropic) edged out Grok 4.7 (xAI) on geographic accuracy of the name placement.

官方账号Artificial Analysis@ArtificialAnlys11 sourcesSource

The hallucination gate reshuffles the leaderboard: Muse Spark 1.3 would lead at 26.7% all-pass without it, but two thirds of its passes contain material hallucinations, while GPT-6 Astra keeps nearly all its passes and jumps from joint 10th to 3rd. Legal tech teams benchmarking models for drafting work should weight groundedness over raw pass rates, and cost data matters too — Grok 4.7 tops the table at roughly US$9.50 per task, under half the ~US$21.70 of Claude Fable 5.1.

官方账号Artificial Analysis@ArtificialAnlys11 sourcesSource

Safety refusals, not coding skill, now decide leaderboard rankings on defensive security work, a factor most agent benchmarks ignore. Security teams choosing models for vulnerability triage should check refusal rates alongside raw scores, since a top coder may decline a third of real tasks. The ranking also spans Chinese and US labs, making it a useful cross-vendor comparison point.

官方账号Artificial Analysis@ArtificialAnlys11 sourcesSource

Grok 4.7's regression on engineering tasks matters for developers comparing AI coding assistants.

Cognition@cognition_labsSource