Which models are really strongest: an ongoing record of benchmark scores, evaluation methodology debates, and leaderboard changes.
Google DeepMind's Gemini 4 Argon scored 97.5% on ARC-AGI-3 using the Provider Adapter Harness, at a cost of $4.
Why it matters: The original piece compiles evaluation data from both ARC-AGI-3 and Epoch ECI, and points out that scores depend heavily on the test environment, offering verifiable details for cross-model comparisons.
Microsoft and Hugging Face released the ThinkingBox agent sandbox and ThinkingBox-Bench benchmark, with 507 stateful business workflows and 20 runs per task, using executable checks to evaluate end-state database state and side effects, now open on Hugging Face via OpenEnv.
Why it matters: The article uses executable backend-state checks to distinguish surface success from real outcomes, and provides consistency and cost data for each model, a directly useful reference for evaluating agents.
Voice Arena and Hugging Face released four Monsoon evaluation sets for the Open ASR Leaderboard, covering Indian English and Hindi, with Hindi the first non-European language on the multilingual leaderboard.
Why it matters: The original details the Monsoon sets' split structure, speaker coverage and regional analysis examples, helping readers understand sources of model differences beyond aggregate WER.