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VentureBeat · 2026-10-15 2026-10-16-w1

NVIDIA's new, open Nemotron 3.5 Super VL with post-training beats Claude Opus 5, GPT-6.1 Sol on specialized task benchmarks

Super VL, a 120B open base, went from about 28% to about 72% on medical claims after post-training, against about 38% for Claude Opus 5. The base model sat below Opus 5, so the gain came from the customer's data and tuning budget, and the comparison is tuned against untuned. Its Artificial Analysis index of 26 trails GLM-5.3-Flash (42) and Qwen3.8-Flash-Next (40), which says the weights are not what wins. For builders the buying decision moves from which model to who owns the evaluation set and the post-training pipeline. In the week's argument this is the lightest of Nvidia's levers: it gives away the part that is commoditizing and keeps buyers building on its stack, with Bolt E4B due Oct 23 as the next test.

Meta AI Research 2026-10-15-1

Introducing Muse Spark Open, The Most Capable US Open‑Weight Model to Date

Meta's Muse Spark Open post (816B total, 42B active, 1M context) is far more specific about availability than capability: seven API hosts, four weight hubs and seven-plus engines on day one, none of them Meta. The local recipe is two 512GB M5 Ultra Mac Studios running through MLX. Meta is giving the weights away to buy presence across every stack, which fits the Spyglass reading that its open tier trails its closed one.

VentureBeat 2026-10-15-3

NVIDIA's new, open Nemotron 3.5 Super VL with post-training beats Claude Opus 5, GPT-6.1 Sol on specialized task benchmarks

NVIDIA's Super VL, a 120B open base, went from about 28% to about 72% on medical claims after post-training, against about 38% for Claude Opus 5. The base model sat below Opus 5, so the gain came from the customer's data and tuning budget, and the comparison is tuned against untuned. Set beside Meta's Muse Spark Open, it suggests the weights are the commodity and the evaluation set and training pipeline are what a buyer should own.