Latest

Every published item, in order · 398 items
10月13日
ModelsBuildersSelected04:17
FrontierCode 1.1 Leaderboard Launches on Artificial Analysis

The benchmark penalises behaviours conventional evals ignore, such as oversized diffs, out-of-scope file edits and unhelpful tests, so its ranking offers a different picture of coding ability than SWE-style test suites. Even the best model fails over 40% of tasks, meaning maintainer cleanup is still unavoidable. Teams choosing models by cost-performance should note GPT-6.1 Sol matching Claude Fable 5.1 at under a tenth of the per-task price.

Cognition@cognition_labsSource
ModelsUS vs ChinaSelected04:15
Artificial Analysis launches leaderboard for Cognition's FrontierCode 1.1

The benchmark's merge-focused grading penalises the messy diffs and unhelpful tests that cheaper evaluations ignore, so leaderboard gaps here reflect real maintainer acceptance rather than raw test-passing. Leading models still fail over 40% of tasks, and nine tasks went unsolved by every model evaluated. Teams choosing coding agents can weigh GPT-6.1 Sol's 49% score at US$0.81 per task against Claude Opus 5.5's 52% at far higher cost, with open-weights options like MiMo-V2.6-Pro at 45% close behind.

官方账号Artificial Analysis@ArtificialAnlysSource
10月12日
10月11日
10月10日
10月9日
10月8日
ModelsBuildersSelected07:37
Agent Arena tests show Jev Router trails DeepSeek V4.1 Flash on cost

The evaluation suggests routers are judged less on raw cost-performance and more on how quickly they adapt to user feedback, where Jev Router nearly matches Claude Opus 5.5 (High). Teams building multi-model agent pipelines can use these latency and cost figures to decide whether a routing layer beats picking DeepSeek V4.1 Flash directly. The full thread contains additional session-level insights.

lmarena.ai@lmarena_aiSource
10月7日
10月6日
10月4日
10月2日
10月1日
9月30日