Here, yes. The source is a leaderboard snapshot from SWE-rebench v2, dated May 15–July 1, 2026, evaluating code-fixing models on 111 problems across 65 repositories. The top performers are Anthropic's Fable 5 (64.5% resolved), Grok 4.5 (63.8%), and Anthropic's Opus 5 (63.4%), all marked high-confidence. Below them sit Z.ai's GLM-5.2 and OpenAI's GPT-5.6 Sol. There's a mix of models (which are evaluated directly) and agents (which presumably orchestrate model calls). Cached token percentages run 72–97%, suggesting most inference reuses prior context. The table shows cost-per-problem and token counts per problem, though the source doesn't explain the methodology for cost calculation or whether token counts include or exclude cache. About 17 entries at the bottom show N/A across all metrics—likely models queued but not yet evaluated in this window. The changelog notes rapid iteration: 50+ model additions and deprecations since May 2025, with several models cycling out when newer versions arrive. The source doesn't say which models are currently *recommended* or whether there's a filtering threshold for display. One sharp open question: what's the evaluation methodology for distinguishing model vs. agent performance, and does "Pass@5" mean five attempts or five-shot in-context learning?