Nemotron 3.5 Super VL vs Muse Spark Open: Which Should You Use?
Compare Nemotron 3.5 Super VL and Muse Spark Open.
| Dimension | Nemotron 3.5 Super VL | Muse Spark Open | Source |
|---|---|---|---|
| License | OpenMDW-1.1 (commercial-friendly) | Muse Model License (limited commercial terms) | HF cards (2026-10-17 vs 2026-10-16) |
| Gating | Ungated (click-through download) | Gated (manual, requires accepting license form) | HF API checks |
| Parameters | 120B total / 12B active | 816B total / 42B active | Vendor docs + HF cards |
| Context length | 1,048,576 tokens (1M) | 1,048,576 tokens (1M) | Vendor docs |
| Modalities | Text + image + video → text | Text + image + audio + video → text | HF cards |
| Hardware (vendor figures) | NVFP4 on 1× H200, 2× H100, or 1× GB200; BF16 on 4× H100 or 2× GB200 | NVFP4 ≥700 GB VRAM (4-GPU Blackwell); BF16 ~2 TB VRAM (~8× B300) | NVIDIA docs (2026-10-17) vs Meta docs (2026-10-16) |
| Deployment size (quantized) | NVFP4 ~147 GiB on disk | NVFP4 ~496 GiB on disk | HF API usedStorage |
| Commercial use restrictions | None (attribution "Built with NVIDIA Nemotron" required) | MaaS revenue threshold (USD 20M notification + agreement) | LICENSE files |
| HF likes (sourced snapshot) | 142 (BF16 card, created 2026-10-15) | 264 (BF16 card, created 2026-09-15) | HF API 2026-10-17 vs 2026-10-16 |
| Quality / performance | Not independently tested | Not independently tested | n/a |
When to choose which
- Smaller active-parameter datacenter deployment (12B active): Nemotron 3.5 Super VL — Lower active-parameter count may reduce inference cost, vendor figures only.
- Ungated commercial-friendly license with no revenue thresholds: Nemotron 3.5 Super VL — OpenMDW-1.1 permits MaaS without separate agreements.
- Largest-scale Meta multimodal MoE (816B total / 42B active): Muse Spark Open — Gated manual access, limited commercial terms.
- Audio input modality: Muse Spark Open — Muse supports text + image + audio + video; Nemotron 3.5 Super VL supports text + image + video.
Read the entity pages for the full sourced tables: Nemotron 3.5 Super VL and Muse Spark Open. Both are listed on ChatGPT alternatives and closed LLM APIs.
Key differences
Gating and license
Nemotron 3.5 Super VL weights are ungated on Hugging Face (click-through download), under OpenMDW License 1.1. Commercial use including Model-as-a-Service is permitted with no revenue thresholds. Attribution “Built with NVIDIA Nemotron” required.
Muse Spark Open weights are gated: manual on Hugging Face (requires accepting the Muse Model License form before download), under Muse Model License (SPDX other). Section 3: if you offer MaaS and aggregate gross revenue exceeds USD 20 million in any consecutive 12 months, you must notify Meta and enter a separate written agreement. Attribution “Built with Muse Spark Open” required. Section 3 does not apply to internal use, research, or non-MaaS commercial activity.
Not legal advice.
Scale and hardware
Nemotron 3.5 Super VL: 120B total / 12B active sparse MoE. NVIDIA docs (2026-10-17) say NVFP4 deploys on 1× DGX Spark, 1× H200, 2× H100, or 1× GB200; BF16 on 4× H100 or 2× GB200. NVFP4 ~147 GiB on disk.
Muse Spark Open: 816B total / 42B active sparse MoE. Meta docs (2026-10-16) say NVFP4 needs at least 700 GB VRAM (4-GPU NVIDIA Blackwell node; weights ~537 GB) and BF16 needs roughly 2 TB VRAM (~8× B300; weights ~1.65 TB). NVFP4 ~496 GiB on disk. The Meta blog also says it runs on two Apple M5 Ultra Mac Studios via MLX.
Both are datacenter-class. Active-parameter difference (12B vs 42B) is not a measured speed or quality comparison.
Modalities
Nemotron 3.5 Super VL: text + image + video → text.
Muse Spark Open: text + image + audio + video → text.
Deployment options
Nemotron 3.5 Super VL: vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, Ollama, Unsloth. Hosted partners: Baseten, Fireworks, Groq, Modal, Nebius, OpenRouter, Together. NVIDIA NIM microservices.
Muse Spark Open: SGLang, vLLM, TensorRT-LLM, Transformers, Apple MLX, llama.cpp, Unsloth. Hosted partners: Baseten, Fireworks, Groq, Modal, Nebius, OpenRouter, Together. Catalogs: Amazon SageMaker AI, Google Cloud Model Garden, Microsoft Foundry Models, Hugging Face.
Sourced snapshot
| Field | Nemotron 3.5 Super VL (2026-10-17) | Muse Spark Open (2026-10-16) |
|---|---|---|
| Host | huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 (not gated) | huggingface.co/meta-models/Muse-Spark-Open-816B-A42B (gated: manual) |
| HF likes / downloads | 142 / 40 | 264 / 402 |
| License | OpenMDW-1.1 | Muse Model License (SPDX other; limited commercial terms) |
| Size | 120B total / 12B active hybrid MoE | 816B total / 42B active sparse MoE (256 experts, top-8) |
| Job | Text + image + video in, text out | Text + image + audio + video in, text out |
| Context | 1M tokens | 1M tokens |
| Quality vs ChatGPT | Not independently tested | Not independently tested |
Why this comparison exists
Both are datacenter-class multimodal MoEs with 1M-token context released in October 2026. Nemotron 3.5 Super VL is NVIDIA’s vision-language model with ungated OpenMDW-1.1 license and 120B/12B parameters. Muse Spark Open is Meta’s gated 816B/42B multimodal MoE with a USD 20M MaaS notification threshold. The choice is primarily licensing and scale: ungated commercial-friendly (NVIDIA) vs gated limited-commercial larger-scale (Meta).
What we left out
Vendor benchmark tables (GPQA-Diamond, HLE, MMLU-ProX, etc. on Nemotron 3.5 Super VL card; JobBench, MCP-Atlas, GPQA Diamond, Terminal-Bench, etc. on Muse Spark Open card) stay self-reported. We have not loaded either model on a named cluster for a head-to-head speed or quality test. iPhone / Mac Studio claims for Muse Spark Open (blog mentions two M5 Ultra Mac Studios via MLX) are vendor figures. Native Multi-Token Prediction (MTP) on Nemotron vs unspecified speculative-decoding method on Muse is not a measured comparison.
How to choose
- Need ungated weights with no manual access gate: Nemotron 3.5 Super VL.
- Need no MaaS revenue thresholds in the license: Nemotron 3.5 Super VL (OpenMDW-1.1).
- Need audio input modality: Muse Spark Open.
- Need the largest-scale Meta multimodal MoE (816B/42B) and accept manual gating: Muse Spark Open.
- Need smaller active-parameter count (12B vs 42B) for potentially lower inference cost: Nemotron 3.5 Super VL (vendor figures only, not measured).
See also ChatGPT alternatives, closed LLM APIs, and AI Chat & Agents.
FAQ
Is Nemotron 3.5 Super VL faster than Muse Spark Open?
We have no sourced benchmark that says so. Both are datacenter-class MoEs with 1M context. Active-parameter difference (12B vs 42B) is not a speed measurement.
Can I use both commercially?
Yes, with different terms (not legal advice). Nemotron 3.5 Super VL is OpenMDW-1.1 with "Built with NVIDIA Nemotron" attribution, no revenue thresholds for MaaS. Muse Spark Open is Muse Model License with "Built with Muse Spark Open" attribution and a USD 20M MaaS notification threshold. Read both LICENSE files.
Which is better for my use case?
If you need ungated weights and no MaaS revenue thresholds, start with Nemotron 3.5 Super VL. If you accept manual gating and want the larger Meta checkpoint, evaluate Muse Spark Open. We have not tested either on a production load.
Do I need a GPU cluster to run them?
Yes. Nemotron 3.5 Super VL NVFP4 targets 1× H200, 2× H100, or 1× GB200. Muse Spark Open NVFP4 needs ≥700 GB VRAM (vendor: 4-GPU Blackwell node). Both are datacenter-class, not laptop checkpoints.
Sources & verification
- https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16
- https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B
- https://developer.nvidia.com/topics/ai/Nemotron
- https://research.meta.ai/blog/muse-spark-open
- https://dev.meta.ai/docs/muse-spark-open
License notes are not legal advice. Read the original LICENSE. Trademark & disclaimer.