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No. Meta presents Muse Wonder and Muse Wonder Full as hosted models, not as open-source or open-weight downloads. Commercial use through Meta Model API is intended under Meta’s API terms, but that is separate from the model’s licensing status. Muse Wonder Full is listed for Muse Code subscribers, so its use is governed by the terms that apply to that subscription.
Are Muse Wonder and Muse Wonder Full open source?
Meta’s official materials describe both models as services you access through Meta platforms; they do not identify a Wonder-specific public weight download or separate open model license. That means you should not assume you can download, modify, or self-host either model. Meta describes Muse Glimmer separately as open weight and runnable on user hardware, but that does not make the Wonder models open weight. See Meta’s October 12, 2026 announcement and the Muse Wonder model page.
How do the two models differ in access and commercial terms?
| Model | Access route listed by Meta | Commercial-use basis | Deployment indicated |
|---|---|---|---|
| Muse Wonder | Meta Model API and Muse Code | For API use, Meta Model API Terms of Service and incorporated policies apply. Muse Code use is subject to applicable subscription terms. | Hosted service; no Wonder weight download is identified in the cited materials. |
| Muse Wonder Full | Muse Code subscription | Applicable Muse Code subscription terms; the API terms are not a substitute for those terms. | Hosted service; no Wonder Full weight download is identified in the cited materials. |
Meta’s October 12, 2026 Meta Model API overview lists Muse Wonder as muse-wonder-1.0 and Muse Wonder Full as muse-wonder-full-1.0. It describes Wonder as in preview. Access, pricing, preview status, and availability may change, so check Meta’s current model page before choosing a route.
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Can you use Muse Wonder commercially?
Meta’s Model API Terms of Service, last updated October 9, 2026, say: “The Services are intended for your commercial use, except where Meta expressly makes the Services available for personal or consumer use, including through a consumer subscription.” The terms also state that use of a model through the API is governed by the API terms, rather than by a separate model license. Read the current Meta Model API Terms of Service and their incorporated policies before deploying it in a product or workflow.
#1 Best Overall
- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
This commercial-use statement applies to the API service. It does not establish that every Muse Code subscription permits every commercial deployment. If you plan to use Muse Wonder Full through Muse Code, check the current subscription terms and any applicable use restrictions for your account and intended use.
What rights do you have in API inputs and outputs?
Meta’s Developer Help Center says, “No, Meta does not claim copyright over outputs generated with Model API.” It also cites Terms of Service Section 5.1, under which users own rights, title, and interests in their Inputs and Outputs. This describes Meta’s position on API-generated outputs; it is not a guarantee that a particular output qualifies for copyright protection or is free of third-party rights. Review the Meta AI Developer Help Center explanation alongside the terms.
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
What should you check before using the models in a business?
- Confirm the route: Wonder API use and Wonder Full through a Muse Code subscription are distinct access arrangements.
- Read the relevant current terms: Check the API Terms of Service for API use, or the applicable subscription terms for Muse Code. Review the Acceptable Use Policy and geographic-use policy as well.
- Check location and eligibility: Meta’s Model API Model Designation and Geographic Use Policy, last updated October 12, 2026, lists both Wonder models as Standard Models for Model API purposes. That is an API service designation, not an open license or evidence of downloadable weights.
- Check data and contribution settings: Meta’s pricing and rate limits documentation says prompts, code, files, context, and responses in Standard mode are not used to train Meta models, and Contributor mode is unavailable for Muse Wonder on Model API. Those API statements do not establish Muse Code subscription settings; check those separately.
What does Meta list for API pricing?
On Meta’s model and API documentation pages, Muse Wonder is listed in preview at $4 per million input tokens, $0.20 per million cached input tokens, and $20 per million output tokens. These are the listed Standard API rates; the cited page gives no per-token API price for Muse Wonder Full, which it lists as available by Muse Code subscription. Prices and access can change, so verify the current model listing and API overview before budgeting.
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- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
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- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
- PCIe 5.0 Support: PCI Express 5.0 x16 interface for maximum bandwidth with the latest workstation platforms.
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- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
