Muse Wonder 1.0: context, access and model facts

Publisher-sourced inventory. Specifications come from official documentation and model cards. “Not documented” means we have no verified value. This inventory is separate from our editorial reviews.

Coverage & sources ↗

Current catalog answers

What to know about Muse Wonder 1.0

AI-compiled from linked model documentation. This is a catalog reference, not an independent performance review. Catalog checked October 23, 2026.

Which Muse Wonder 1.0 does this page describe?

Muse Wonder 1.0 is a model record from Meta in the Muse Wonder family. Source

The recorded model identifier or checkpoint is muse-wonder-1.0. Use that exact identifier when checking documentation or a provider listing. Source

How can you access Muse Wonder 1.0?

The catalog documents API access for Muse Wonder 1.0. Source

As of the catalog check, this version was listed as a preview. Source

For API use, confirm the exact model identifier, your account and region, and the endpoint's current limits in the linked documentation. An API listing does not establish access through a consumer subscription.

How much does Muse Wonder 1.0 cost?

The recorded token rates for Muse Wonder 1.0 are:

Input: US$4 per 1 million input tokens. Source
Output: US$20 per 1 million output tokens. Source

Rate conditions: Standard mode direct Meta API prices in USD per million uncached input/output tokens. Cached input is $0.20 per million. No long-context premium; web search is an additional $2.50 per 1,000 queries. Muse Code subscriptions are a separate product. Source

Illustrative arithmetic: 10,000 input tokens plus 2,000 output tokens would cost US$0.08 at those recorded rates. This assumes every token is billed at the displayed rate and excludes any cache, batch, tool, storage or tier adjustments; it is not a quote or measured usage.

Check the source's pricing conditions before budgeting. A token rate does not state the price of an app subscription or the full cost of running downloaded weights.

What are Muse Wonder 1.0's context and output limits?

The documented context window is 1,048,576 tokens. Source

A separate maximum-output value is not established in this record. The context or input figure should not be used as an output allowance.

Context, input and output limits describe different constraints. Do not add the figures together to infer a larger window. Check the endpoint's accounting for conversation history, system instructions, tools, media and generated tokens; a documented maximum is not a guarantee of answer quality or a measured usable capacity.

What can Muse Wonder 1.0 receive and generate?

Documented inputs: text, image, video, audio, and pdf. Source

Documented outputs: text. Source

Input support and output generation are separate capabilities. Reading an image does not establish image generation; a text output listing does not establish speech output. File sizes, formats and media limits depend on the documented implementation.

What distinguishes Muse Wonder 1.0?

These details come from the linked publisher material for this model. Reported capabilities and training descriptions are publisher statements, not independent test results.

Standard data policy: Standard Mode content is not used to train Meta models. This is a mode-specific policy; it does not describe Contributor Mode. Source
Preview and access: Muse Wonder 1.0 is a preview available through the Model API in Standard Mode. The separate Muse Wonder Full offering is accessed through Muse Code, not by adding a Model API max-effort flag. Source
Regional restriction: The model guide says Muse Wonder is unavailable in South Korea. Source
Team rate-limit scope: The listed limit for muse-wonder-1.0 is 3,000 requests and 4,000,000 tokens per minute. Limits apply across all API keys in one team, separately for each model; adding another key does not create another allowance. Source

The documented record

Specifications, with their sources.

A missing value remains unknown. Input limits, output limits and the total context window are separate measurements.

Publisher: MetaSource ↗
Family: Muse WonderSource ↗
Version: muse-wonder-1.0Source ↗
Availability: PreviewSource ↗
Access: APISource ↗
Inputs: Text · Image · Video · Audio · PdfSource ↗
Outputs: TextSource ↗
Context window: 1,048,576 tokensSource ↗
Maximum input: Not documented
Maximum output: Not documented
Knowledge cutoff: Not documented
Training-data cutoff: Not documented
Training completed: Not documented
Release date: Not documented
License: Not documented
Input price / 1M tokens: $4Source ↗
Output price / 1M tokens: $20Source ↗

Read this with the numbers.

Standard mode direct Meta API prices in USD per million uncached input/output tokens. Cached input is $0.20 per million. No long-context premium; web search is an additional $2.50 per 1,000 queries. Muse Code subscriptions are a separate product.

Model identifiers and aliases 0

No additional identifier has been verified.

Follow the evidence

Official sources.

models — official Meta documentation ↗dev.meta.ai
pricing rate limits — official Meta documentation ↗dev.meta.ai

From the Headless Thrones newsroom

Editorial about Muse Wonder 1.0

No published articles are linked to this exact model yet. New coverage will appear here as it is published.

Your starting point

Muse Wonder 1.0

Meta · API

Browse all models ↗

Inputs: Text · Image · Video · Audio · PdfSource ↗
Outputs: TextSource ↗
Context window: 1,048,576 tokensSource ↗
Knowledge cutoff: Not documented

Google

Gemini 3.1 Pro Preview Customtools

Text + Image + Video + Audio + Pdf input; text output · Shared access: API · Capacity match not established

A record you can follow

The source belongs beside the fact.

Official sources

Model records cite the developer’s documentation, release notes and model cards. A listing alone is not a tested service or a recommendation.

Dates mean different things

Knowledge cutoff, training-data cutoff, training completion and release date are separate fields. A model’s name or repository upload date does not establish any of them.

Limits need context

Some publishers use rounded K/M token figures. Deployment, reasoning settings and regional access can change usable limits. Read the source before choosing.

Coverage and last check

Independent editorial model library assembled from primary vendor documentation and official publisher model cards. This is a dated, auditable inventory of the sources reviewed, not a claim to every model worldwide. First-party language-model inventory for OpenAI, Anthropic, Google Gemini/Gemma, xAI, Meta and Reflection. Includes vendor-listed legacy/retired identities with explicit statuses; official pretrained and instruction-tuned checkpoints remain distinct. Excludes embeddings, moderation/guard models, image/audio/video-only generation, community fine-tunes, speculative draft helpers, format/runtime/quantization duplicates, and PaliGemma dataset-specific benchmark fine-tunes. Availability means documented vendor API or downloadable official weights, not guaranteed account eligibility. Unknown specifications and dates remain null. Rounded K/M limits preserve vendor granularity; exact integers are used only where documented. No OpenRouter facts are used. Primary-source inventory of language-capable models from Alibaba/Qwen, DeepSeek, Moonshot, MiniMax, Z.ai, ByteDance Seed, Tencent, Baidu, Xiaomi, StepFun, 01.AI, Shanghai AI Laboratory, Baichuan and OpenBMB. Includes full reachable official public weight inventories plus documented hosted API models and explicit retirements. Embeddings, reward/classifier/router/tokenizer/draft-head components, standalone OCR/document converters and image/video/audio-only generators are excluded; multimodal language models retained. Official packaging variants are folded into aliases where a canonical listing was identifiable; remaining research/base/SFT variants retain exact identities. Not a guarantee every historical/private/regional model is available. Unknown specifications remain null; K/M API display quantities use decimal normalization where explicitly noted, rather than implying verified exact serving ceilings. Model-name dates and repository creation timestamps are never used as release/training dates. Open weights does not mean unrestricted or OSI-open-source licensing. Some model-card bodies were unreachable; their public inventory metadata is retained and disclosed. Official Mistral, Cohere, AI21, NVIDIA Nemotron, Microsoft Phi/MAI, Amazon Nova, IBM Granite, AllenAI OLMo/Molmo, TII Falcon, Nous Research, Arcee and Essential AI model inventory checked 2026-10-23. Includes base, instruction, reasoning, coding and text-producing multimodal models, including legacy API versions. Excludes embeddings/rerankers/reward and safety classifiers, image/audio/video-only generation, robotics-only models, duplicate quantization/runtime packages, adapters, intermediate checkpoint experiments, and third-party mirrors. Arcee coverage focuses on its named product families rather than exploratory merge repositories. Repository listing means listed, not independently verified API availability. K/M figures are nominal decimal normalizations when the source supplies shorthand; deployment-specific exact token limits may differ. Unverified values remain null. This is a source-backed inventory, not a complete claim-level independent editorial review. Supplemental official Liquid AI, Reka and Inception generative language catalog. Excludes media-only generators and decision/classification-only models. No assumed training dates.

Catalog edition updated Oct 23, 2026. Source links are provided on each model record.

View the full coverage report and discovery sources ↗