--- title: Open weights had a week: Meta's 816B coder and NVIDIA's 120B agent model arrive with day-one infrastructure url: https://hangingcontext.com/stories/2026-10-16/open-weights-week/ published: 2026-10-16T00:40:05Z edition: 2026-10-16T00 newsroom: artificial-intelligence beat: models-labs audience: ai_developer trust: confirmed source_count: 6 cited_manifest_ids: ['1792097735331012141', '1792096888701074007', '1792089875498619483', '1792090958410378559', '1792079826191259054', '1792066118701922808'] --- # Open weights had a week: Meta's 816B coder and NVIDIA's 120B agent model arrive with day-one infrastructure *Meta released the weights for an 816B-parameter coding model and NVIDIA opened a 120B-parameter agent model the same week. Intel, Modal, and AI21 had plans ready before the ink was dry.* Confirmed · 6 sources · Artificial Intelligence · Published 2026-10-16 00:40 UTC ## Key facts - Meta released weights for Muse Spark Open, an 816B mixture-of-experts coding model with 42B active parameters and a 1M-token context window; Meta's own evaluations put it at 76.4 on Terminal-Bench 2.1 and 60 on DeepSWE 1.1. (self-reported) [cites: 1792097735331012141, 1792096888701074007] - Intel shipped day-0 support for Muse Spark Open on Xeon and its GPU D-series, using its AutoRound algorithm to quantize the model to MXFP4 and INT4, and says a compact four-card configuration can host the 816B-class model. (self-reported) [cites: 1792089875498619483] - NVIDIA released Nemotron 3.5 Super VL as an open 120B-parameter model, saying it matches the company's largest reasoning model at roughly one-fifth the parameter count and reaches up to 5x the throughput of peers on the Artificial Analysis Intelligence Index. (self-reported) [cites: 1792090958410378559] - AI21's forward-deployed researchers post-trained Nemotron 3.5 Super VL with three partners: Gong's data-extraction agent matched Claude Opus 5.5 on speed and accuracy in Gong's own evaluations, Deel's payroll QA model hit 72% on an 8-country benchmark (8 points above Claude Opus 5.5), and Alice gained 41 points of attack resilience on its RL gym. (self-reported) [cites: 1792079826191259054] - In a preprint, the Apertus team released Apertus 1.5, extending its fully open model family to multimodal reasoning with a 262,144-token context window and training only on openly available data; the 8B model gained up to 20 points on mathematical reasoning. (self-reported) [cites: 1792066118701922808] - Modal made Muse Spark Open available on its Auto Endpoints the same day the weights were released, offering it as a shared endpoint with per-token billing. (self-reported) [cites: 1792096888701074007] ## What shipped Meta released the weights for Muse Spark Open, an 816B-parameter mixture-of-experts model with 42B active parameters and a 1M-token context window built for agentic coding, according to NVIDIA's developer blog, which notes it was trained on NVIDIA GB300 NVL72 Blackwell Ultra hardware. Modal's launch post adds that Meta's own evaluations score the model at 76.4 on Terminal-Bench 2.1 using the Terminus 2 agent and 60 on DeepSWE 1.1 with Meta's preferred harness. [cites: 1792097735331012141, 1792096888701074007] In the same week, NVIDIA released Nemotron 3.5 Super VL as an open 120B-parameter model aimed at specialized always-on agents, saying it delivers accuracy comparable with NVIDIA's largest reasoning model at roughly one-fifth the parameter count and up to 5x the throughput of comparable models on the Artificial Analysis Intelligence Index. The model was trained on about 26 trillion tokens across pretraining and continued pretraining, then post-trained with roughly 3.8 million samples and reinforcement learning across dozens of environments. Alongside it came Bolt variants for local workloads, including a 74B-A9B model that uses native 2-bit quantization to fit in 24GB of memory and a 4GB model aimed at conversational agents. [cites: 1792090958410378559] The research world added its own entry in a preprint: Apertus 1.5 extends the fully open Apertus family from text to multimodal reasoning, quadrupling the context window to 262,144 tokens. The team trained only on openly available data, excluding restrictively licensed material, and reports that the 8B model gained up to 20 points on mathematical reasoning while leading fully open models of its class on knowledge, instruction following, and cultural benchmarks. [cites: 1792066118701922808] > **From Synorb** — Save what you follow as a Beacon. A Beacon is a saved query that your agent pulls with an explicit lookback window. Every new account starts with 3 starter Beacons: Business & Technology Podcasts, AI Companies and Central Banks. [See Beacons](https://synorb.com/connect) ## Day-one infrastructure What made this week unusual was not just the releases but how ready the ecosystem was. Intel announced day-0 support for Muse Spark Open across Xeon processors and its GPU D-series, using its AutoRound algorithm to quantize the model's original BF16 weights to MXFP4 and INT4 variants, with upstream support in mainline vLLM. Intel says a compact four-card configuration can host the 816B-class model within conventional air-cooled racks, which turns an intimidating download into something a mid-size team can actually run. [cites: 1792089875498619483] Modal, for its part, had Muse Spark Open live on its Auto Endpoints the same day the weights were released, as a shared endpoint with per-token billing aimed at agentic coding and long-horizon tasks. NVIDIA packages Nemotron 3.5 Super VL as a NIM microservice and says running it on NVIDIA Dynamo lets organizations serve up to 8x more users with 85% lower latency. The pattern is consistent: open weights now arrive with a serving story attached, not months later. [cites: 1792096888701074007, 1792090958410378559] ## The specialists move in The most telling evidence came from AI21 Labs, whose forward-deployed researchers spent three weeks post-training Nemotron 3.5 Super VL with three partners, and the results read like a case for specialization over scale. Gong's data-extraction agent, which reviews customer interactions to identify deal and account information, matched models like Claude Opus 5.5 on speed and accuracy in Gong's own evaluations. Deel's payroll QA model reached 72% accuracy on an 8-country benchmark built on expert-verified training data, sitting 8 points above Claude Opus 5.5. And Alice improved attack resilience by 41 points on its RL gym, which uses the Rabbit Hole dataset sourced from real-world threat actor behavior. [cites: 1792079826191259054] AI21 says the work, combining SFT with DPO, self-distillation, and GRPO depending on the partner, took weeks rather than quarters, which is the number that matters most. NVIDIA reports dozens of organizations are already post-training Super VL on their own data and workflows, including Salesforce customizing it for government workflow automation. This suggests the open-model playbook is converging: release strong base weights, and let partners distill them into specialists that beat frontier models on narrow jobs. [cites: 1792079826191259054, 1792090958410378559] ## What is still unresolved - No independent, non-company benchmark scores have been published yet for Muse Spark Open or Nemotron 3.5 Super VL; all cited numbers come from the releasing companies or their partners. - Modal says Muse Spark Open bills per token but does not disclose the per-token price. - The Apertus 1.5 results come from the team's own preprint and have not been peer-reviewed. ## Decision card - **Useful for:** AI developers evaluating open models for agentic coding or document-workflow agents; Infrastructure teams deciding whether 816B-class models fit existing GPU fleets; Teams comparing post-training a specialist against renting a frontier model - **Not for:** Teams that need a managed, turnkey API with SLAs; Anyone choosing a model on independent benchmark data alone - **Compare against:** Frontier closed models on cost per completed task, not raw benchmark scores; Prior open releases, which typically shipped without day-one quantization and serving support ## Provenance ledger | Manifest id | Title | Source | Claims used | Published | |---|---|---|---|---| | `1792097735331012141` | Scale Agentic Coding Workflows with Meta's Muse Spark Open on NVIDIA GB300 NVL72 | [nvidia-developer-blog](https://developer.nvidia.com/blog/scale-agentic-coding-workflows-with-metas-muse-spark-open-on-nvidia-gb300-nvl72) | 1792106110186517432, 1792106110254680197, 1792106110270978237, 1792106110331124788, 1792106110165012453 | 10-15 00:00 | | `1792096888701074007` | Muse Spark Open is now available on Modal | [modal-labs-blog](https://modal.com/blog/meta-muse-spark-open-now-available-on-modal) | 1792109463143504806, 1792109463155566319, 1792109463103591872, 1792109463115460699, 1792109463163769012, 1792109463171451250 | 10-15 00:00 | | `1792089875498619483` | Intel Offers Day-0 Support for Meta's Muse Spark Open | [intel-blog](https://intel.com/content/www/us/en/newsroom/news/artificial-intelligence/intel-offers-day-0-support-for-meta-muse-spark-open.html) | 1792091459915778619, 1792091459943295911, 1792091459947176066 | 10-15 00:00 | | `1792090958410378559` | Open From Data Center to Desktop: NVIDIA Expands Nemotron Family With Highest Efficiency Super VL and Bolt Models | [nvidia-newsroom-rss](https://blogs.nvidia.com/blog/nemotron-3-5-super-vl) | 1792091263592815704, 1792091263597726214, 1792091263598575161, 1792091263604907196, 1792091263605783151, 1792091263600579076, 1792091263596315563, 1792091263607971083, 1792091263608212920, 1792091263601999477, 1792091263602533061 | 10-15 00:00 | | `1792079826191259054` | AI21 Partners with Gong, Deel and Alice to Post-Train Specialized Models on NVIDIA Nemotron 3.5 Super VL | [ai21-labs-blog](https://ai21.com/blog/ai21-post-trains-nvidia-nemotron-35-super-vl) | 1792085724975055487, 1792085724982787383, 1792085724985292902, 1792085724990597715, 1792085725011433831, 1792085725013587891, 1792085724991895659, 1792085725019342308, 1792085725020546309, 1792085725022735687 | 10-15 00:00 | | `1792066118701922808` | Apertus 1.5: From Open Text Models to Open Multimodal Reasoners | [huggingface-daily-papers](https://huggingface.co/papers/2610.15542) | 1792066470414060114, 1792066470420060134, 1792066470428550127, 1792066470424445188, 1792066470426172069 | 10-15 00:00 | ## Run these Manifests yourself Every Manifest cited here can be read in full with your own Synorb Key. 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