# Meta Muse Wonder Explained: Full vs Standard, Benchmarks, Pricing, API & Muse Code (2026)

Explore Meta Muse Wonder and Wonder Full: independent benchmarks, API pricing, Muse Code access, features, limitations, and developer setup.

Canonical URL: https://aiidelist.com/blog/muse-wonder-guide

Language: en

Published: 2026-10-13

Updated: 2026-10-13

## Key Takeaways

- **Meta launched Muse Wonder and Muse Wonder Full on October 12, 2026.** They are proprietary frontier AI models designed for complex coding, long-running agents, computer use, and professional knowledge work.
- **Access differs:** `muse-wonder-1.0` is available through the Meta Model API and Muse Code. **Muse Wonder Full is not available through the public API** and requires an eligible Muse Code subscription.
- **Both models have a 1,048,576-token context window.** Meta documents text, image, video, audio, and PDF inputs, with text output.
- **Muse Wonder API pricing starts at $4 per million input tokens and $20 per million output tokens.** Cached input costs $0.20 per million tokens.
- **Independent benchmarks show a significant performance improvement.** Artificial Analysis scored Muse Wonder at 52 and Muse Wonder Full at 56 on its Intelligence Index v4.3, compared with 48 for Muse Spark 1.3.
- **Privacy, usage limits, and geographic restrictions matter.** Muse Code subscriptions default to Contributor mode, which permits training on submitted content unless disabled.

## What Is Meta Muse Wonder?

**Muse Wonder** is a family of frontier AI reasoning models developed by Meta Superintelligence Labs and officially announced on October 12, 2026. The family includes Muse Wonder and Muse Wonder Full, two models designed to handle increasingly complex personal and professional tasks.

Unlike conventional chatbots that primarily generate responses to individual prompts, Muse Wonder focuses on **long-horizon task execution**. It is designed to plan multiple steps, use external tools, work with relevant context, recover from errors, and complete complex workflows with less manual intervention.

The models follow Meta's earlier Muse Spark series and represent a significant step in the company's effort to develop what it calls personal superintelligence.

According to [Meta's official research announcement](https://research.meta.ai/blog/muse-wonder), the Muse Wonder family focuses on four major areas: domain expertise, Active Memory, Active Focus, and frontier coding.

The models also support advanced computer-use scenarios involving browsers, desktop applications, and other tools. These capabilities make Muse Wonder relevant to software developers, AI agent builders, and organizations seeking to automate complex workflows.

An important distinction is that **Muse Wonder is an AI model, Muse Code is a coding-agent product, and Muse is Meta's consumer AI assistant**. The products share technology but have different availability, subscriptions, and interfaces.

Meta announced that Wonder-powered features would gradually reach Muse users, beginning with subscribers. That announcement does not mean every consumer account received the new model immediately.

## Muse Wonder vs Muse Wonder Full: Specifications and Availability

The Muse Wonder family contains two models with different performance levels and distribution methods.

| Feature | Muse Wonder | Muse Wonder Full |
|---|---|---|
| Developer | Meta Superintelligence Labs | Meta Superintelligence Labs |
| Release date | October 12, 2026 | October 12, 2026 |
| Model identifier | `muse-wonder-1.0` | `muse-wonder-full-1.0` in Meta's catalog; not publicly API-callable |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Inputs | Text, image, video, audio, PDF | Multimodal, as documented for the family |
| Output | Text | Text |
| Meta Model API | Available | Not available |
| Muse Code | Available | Power and Maximum plans |
| API input pricing | $4 per million tokens | Not published |
| API output pricing | $20 per million tokens | Not published |
| Artificial Analysis Intelligence Index | 52 | 56 |
| Artificial Analysis Coding Agents Index | 63 | 68 |
| Open model weights | Not released | Not released |

The most important difference is not simply that Muse Wonder Full is more capable. It is **how developers can access each model**.

Regular Muse Wonder is available through a metered API, which makes it suitable for custom applications, automated workflows, and third-party integrations.

Muse Wonder Full is currently restricted to Muse Code's eligible subscription plans. Developers cannot access Full by replacing the standard model identifier in an API request.

**Setting the reasoning effort to `max` on Muse Wonder does not turn it into Muse Wonder Full.** Meta explicitly distinguishes the underlying model variant from its configurable reasoning effort.

## Muse Wonder's Five Most Important Capabilities

### 1. Active Memory: Remembering What Matters

Active Memory is one of Muse Wonder's defining features. Meta describes it as the ability to carry important information across conversations and retrieve relevant context when it becomes useful.

Traditional conversational systems can lose important details as tasks grow longer or conversations change. Active Memory is intended to improve continuity by preserving and retrieving information relevant to the current objective.

For example, an AI travel-planning agent might need to combine a user's previously stated preferences with a new calendar event, transportation constraints, and updated destination information.

The challenge is not merely retaining a large amount of information. **Useful memory requires identifying which details matter to a particular decision.**

Developers should distinguish the model's capabilities from the application's memory infrastructure. A stateless API request does not automatically provide persistent memory across every session or product. Applications still need appropriate storage, retrieval, permissions, and data-deletion mechanisms.

### 2. Active Focus: Sustained Execution Across Complex Tasks

Active Focus describes Muse Wonder's ability to continue working toward a difficult objective, revise unsuccessful approaches, and recover from interruptions or tool errors.

This capability is especially important for long-running AI agents.

Consider an autonomous coding task. The agent may need to inspect a repository, identify dependencies, modify files, run tests, diagnose failures, and repeat the process before the requested feature works correctly.

A model that produces a convincing initial implementation but cannot respond effectively to failed tests has limited practical value.

Muse Wonder's Active Focus is intended to improve this iterative process. Meta also emphasizes asking users for clarification and confirming important actions when appropriate.

However, persistent execution should never be interpreted as unrestricted permission. Production agents still require timeouts, spending limits, approval checkpoints, and recovery mechanisms.

### 3. Frontier Coding and Software Engineering

Muse Wonder is optimized for demanding coding tasks, including repository exploration, multi-file modifications, debugging, software generation, and automated testing.

Meta demonstrated the model creating a playable 3D kitchen game from image-based inputs. The demonstration illustrates how multimodal understanding can support interactive software development.

The more consequential use case is maintaining existing software rather than generating isolated examples.

Real repositories contain architectural constraints, dependencies, legacy interfaces, tests, and undocumented assumptions. Reliable coding agents must understand these relationships and verify that their changes do not introduce regressions.

Muse Wonder Full is particularly relevant to complex engineering workflows where sustained reasoning and repeated validation matter.

### 4. Advanced Computer Use and Tool Orchestration

Muse Wonder is designed to support AI agents that work with browsers, desktop software, and other applications through external execution environments.

Potential workflows include navigating websites, gathering information, processing business documents, interacting with application interfaces, and coordinating multi-step operations.

The model can also support sub-agent delegation and parallel tool invocation, enabling complex tasks to be divided into smaller units of work.

**Computer-use capability does not mean the model automatically has access to a user's device.** Developers must provide the execution environment, tools, credentials, and security boundaries.

### 5. Professional Knowledge Work and Multimodal Reasoning

Muse Wonder targets tasks involving legal documents, financial information, health-related questions, business processes, and other knowledge-intensive workflows.

Supported inputs include text, images, video, audio, and PDF files, while the model produces text output.

This distinction is important because Muse Wonder is not primarily an image-generation or video-generation model. Meta provides separate products for those purposes, including Muse Image and Muse Video.

Professional domain capability also does not eliminate the need for expert review. Legal, financial, and medical outputs should be checked against reliable evidence and applicable professional standards.

## Muse Wonder Benchmarks: How Powerful Is It?

Early independent evaluations suggest that Muse Wonder represents a substantial improvement over Meta's previous flagship models.

The most useful evidence comes from [Artificial Analysis's October 12, 2026 evaluation](https://artificialanalysis.ai/articles/muse-wonder/), which compares both Wonder variants against Muse Spark and competing frontier systems.

### Artificial Analysis Intelligence Index

| Model | Intelligence Index v4.3, maximum reasoning |
|---|---:|
| Muse Spark 1.3 | 48 |
| Muse Wonder | 52 |
| Muse Wonder Full | 56 |

Muse Wonder Full improves by eight index points over Muse Spark 1.3, while standard Muse Wonder improves by four points.

Artificial Analysis reports that Wonder Full improved over Spark 1.3 across all ten evaluations included in the Intelligence Index.

This matters because the gains are distributed across multiple capabilities rather than being concentrated in one narrow benchmark.

### Coding Agents Index: Muse Wonder Full Reaches the Leading Group

| Model and agent setup | Coding Agents Index, maximum effort |
|---|---:|
| Muse Spark 1.3 in Muse Code | 54 |
| Muse Wonder in Muse Code | 63 |
| Claude Opus 5.5 in Claude Code | 66 |
| Muse Wonder Full in Muse Code | 68 |
| Claude Sonnet 5.5 in Claude Code | 68 |

Muse Wonder Full scores 68, matching the rounded score of the evaluated Claude Sonnet 5.5 configuration and exceeding the evaluated Claude Opus 5.5 configuration.

However, **agent benchmarks measure more than the underlying language model**. Results are influenced by the agent harness, available tools, reasoning configuration, execution time, and testing environment.

The scores therefore demonstrate strong performance in the evaluated setups, not guaranteed superiority in every production coding workflow.

### Terminal-Bench and Long-Horizon Engineering

Artificial Analysis reports that Muse Wonder Full substantially improves terminal-task performance compared with Muse Spark 1.3.

- **Terminal-Bench 4.0:** approximately 33% for Muse Spark 1.3 versus 57% for Wonder Full.
- **GDP.pdf:** Wonder Full scores 33%, placing at the top of the published professional-work comparison.
- **CritPt:** Wonder Full scores 33% on the scientific-reasoning evaluation.
- **GDPval-AA v2.1:** Wonder Full reaches 1,857 Elo, compared with 1,681 for Spark 1.3.

These results suggest meaningful progress in practical reasoning and tool-driven work.

Nevertheless, leaderboard positions should be interpreted carefully. Some differences are narrow or within reported uncertainty intervals. A leading score on one evaluation does not establish dominance across every task category.

### An Important Weakness: General Knowledge Accuracy

Muse Wonder's strongest benchmark performance does not extend uniformly across every evaluation.

On AA-Omniscience, Muse Wonder Full scores 30, below several competing frontier models.

Artificial Analysis also reports a benchmark-specific hallucination rate of approximately 23% for Wonder Full, down from 33% for Spark 1.3.

This improvement is encouraging, but the figure should not be interpreted as the probability that any arbitrary answer will be incorrect. It measures behavior under one evaluation methodology.

For research and factual question answering, retrieval, citation verification, and independent checking remain important.

## Muse Wonder Pricing: How Much Does the API Cost?

Meta publishes transparent usage-based pricing for standard Muse Wonder through the [Meta Model API pricing documentation](https://dev.meta.ai/docs/pricing-rate-limits).

| Usage category | Price |
|---|---:|
| Input tokens | $4.00 per 1M |
| Cached input tokens | $0.20 per 1M |
| Output tokens | $20.00 per 1M |
| Web search grounding | Additional $2.50 per 1,000 searches |
| Long-context premium | No additional premium listed |

This places Muse Wonder in the premium frontier-model category rather than the low-cost inference segment.

### How to Calculate Muse Wonder API Costs

A simple estimation formula is:

`cost = (uncached_input_tokens * 4 + cached_input_tokens * 0.2 + output_tokens * 20) / 1000000`

**Example 1: Standard analysis request**

An API request processes 100,000 uncached input tokens and generates 10,000 output tokens.

- Input cost: $0.40.
- Output cost: $0.20.
- **Total estimated cost: $0.60.**

**Example 2: Multi-step agent workflow**

Across multiple requests, an agent uses 300,000 uncached input tokens, 800,000 cached input tokens, and 80,000 output tokens.

- Uncached input: $1.20.
- Cached input: $0.16.
- Output: $1.60.
- **Total estimated cost: $2.96**, excluding tool fees.

Aggregate token usage across a long-running task can exceed the one-million-token context window because the window applies to individual model requests.

**Example 3: Monthly SaaS application costs**

An application makes 100 requests per day, averaging 30,000 uncached input tokens and 5,000 output tokens per request.

At that usage level, the estimated cost is $0.22 per request, or approximately **$660 for a 30-day month**.

Actual spending depends on caching, retries, reasoning effort, and external tool usage.

### Why Benchmark Cost Matters More Than Token Price Alone

Artificial Analysis estimates that standard Muse Wonder costs approximately **$4.52 per Intelligence Index task**, compared with $1.60 for Muse Spark 1.3.

For comparison, the evaluation estimates approximately $3.26 for GPT-6 Astra, $5.98 for Claude Opus 5.5, and $1.99 for Gemini 4 Argon in their respective tested configurations.

Wonder's higher cost comes from its premium token rates and substantial context consumption in extended workflows.

Artificial Analysis reports approximately 7.69 million input tokens per evaluation task for Wonder, compared with 6.44 million for Wonder Full. Both generate roughly 54,000 output tokens per task.

These figures represent benchmark workloads involving multiple model calls. They should not be interpreted as universal per-request prices.

For production agents, the most useful economic metric is often **cost per successfully completed and accepted task** rather than cost per individual request.

## Muse Wonder Full Pricing: Muse Code Subscription Plans

Muse Wonder Full is currently distributed through eligible Muse Code subscriptions rather than a public per-token API.

| Muse Code plan | Monthly price | Usage allowance relative to Everyday | Wonder Full access |
|---|---:|---:|---|
| Power Usage | $50 | 20x | Yes |
| Maximum Usage | $200 | 80x | Yes |

Meta documents five-hour and weekly usage resets for these subscriptions.

The advertised allowances do not mean unlimited access, and a conventional token-based API price for Wonder Full has not been published.

Developers expecting sustained autonomous work should review their actual available quota before assigning multi-hour coding tasks.

A practical comparison should measure how many completed tasks each subscription supports, including retries, model switches, and human review.

## How to Use Muse Wonder API: Developer Tutorial

Muse Wonder is available through Meta's OpenAI-compatible Model API.

The essential configuration values are:

- **Base URL:** `https://api.meta.ai/v1`
- **Model ID:** `muse-wonder-1.0`
- **Authentication:** Bearer token using `MODEL_API_KEY`
- **Context window:** 1,048,576 tokens
- **Availability:** Preview, subject to account and regional eligibility

### Step 1: Create an API Key

Open the [Meta developer platform](https://dev.meta.ai/models/muse-wonder), create an API key using the supported account workflow, and store it securely as an environment variable.

Production API keys should remain on a backend server. Do not embed them in public browser JavaScript or distribute them with client-side applications.

### Step 2: Install the OpenAI SDK

The Meta Model API supports OpenAI-compatible SDK integrations.

For Node.js and TypeScript projects, install the SDK:

```bash
npm install openai
```

### Step 3: Send a Request to Muse Wonder

The following TypeScript example uses Meta's documented Responses API pattern:

```typescript
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.meta.ai/v1",
  apiKey: process.env.MODEL_API_KEY,
});

const result = await client.responses.create({
  model: "muse-wonder-1.0",
  input: "Review this TypeScript function for correctness and edge cases.",
});

console.log(result.output_text);
```

This example requires valid credentials and access from a supported region. It demonstrates the documented integration pattern rather than an independently verified live request.

### Step 4: Add Advanced Agent Capabilities

Meta's API documentation describes support for several useful capabilities:

- **Tool calling:** Allow applications to expose selected tools to the model.
- **Structured outputs:** Request responses conforming to a defined JSON schema.
- **Prompt caching:** Reduce the cost of repeated input prefixes.
- **Reasoning effort:** Adjust computational effort for different workloads.
- **Response state:** Continue multi-turn workflows using supported response-management features.
- **Search grounding:** Incorporate current web information when necessary.

Meta also offers Chat Completions and an Anthropic-compatible Messages interface.

The API supports up to 3,000 requests per minute and 4 million tokens per minute for `muse-wonder-1.0` under its published team-level limits. Actual access and quotas remain subject to Meta's service rules.

Applications should handle rate-limit responses, use exponential backoff with jitter, inspect quota headers, and monitor total task spending.

## How to Access Muse Wonder Full Through Muse Code

Muse Code is Meta's terminal-based AI coding agent. It provides an integrated environment for code editing, tool execution, and agent-driven software engineering.

Unlike the standard API, Muse Code provides subscription access to Wonder Full.

### Install Muse Code on macOS or Linux

Meta documents the following installation command:

```bash
curl -fsSL https://dev.meta.ai/install.sh | sh
```

Start Muse Code with:

```bash
muse
```

After installation, follow the supported sign-in process and select an available model.

Wonder Full requires the Power Usage or Maximum Usage subscription tier.

Before using proprietary repositories, verify the active privacy setting and whether Contributor mode is enabled.

Developers can learn more from [Meta's Muse Wonder developer launch guide](https://dev.meta.ai/resources/blog/build-with-muse-wonder).

## Can Muse Wonder Be Used Through OpenRouter?

Yes. [OpenRouter lists Muse Wonder](https://openrouter.ai/meta/muse-wonder/) with the model identifier `meta/muse-wonder`.

This provides an additional integration option for applications already connected to OpenRouter-compatible infrastructure.

However, OpenRouter's model identifier differs from the native Meta Model API identifier, and gateway-specific billing, routing, privacy, and feature conditions should be checked independently.

**The availability of standard Wonder on OpenRouter does not mean Wonder Full is publicly API-accessible.**

## Advanced Best Practices for Building Agents With Muse Wonder

A capable model is only one part of a reliable autonomous system. The surrounding execution architecture often determines whether a task succeeds safely and economically.

### Define Verifiable Acceptance Criteria

Instead of asking an agent to improve an application, specify the relevant files, expected behavior, tests, and completion criteria.

For example, a repository maintenance task should identify which tests must pass and whether changes outside a particular module are permitted.

### Separate Planning and Execution

Require an implementation plan before consequential operations, especially deployments, destructive file changes, financial transactions, or modifications to third-party accounts.

Human approval checkpoints reduce the risk of an agent completing the wrong task with technically valid tool calls.

### Use Persistent Checkpoints

Long-running agents should store task progress outside the prompt whenever appropriate.

Checkpoints can include completed subtasks, modified files, test reports, outstanding issues, and relevant decisions.

This reduces the risk of losing progress when execution is interrupted or a session must be resumed.

### Use Prompt Caching for Repeated Context

Large repositories and multi-agent workflows often reuse substantial context.

Caching repeated prompt prefixes can reduce costs, but developers should inspect reported cached-token usage rather than assuming that every repeated request receives a cache discount.

### Route Simple Tasks to Cheaper Models

Not every request justifies a premium frontier model.

Classification, extraction, summarization, and straightforward tool calls may be more economical on Muse Spark or other lower-cost alternatives.

More complex planning and recovery tasks can be routed to Muse Wonder. High-difficulty engineering work may justify Wonder Full when subscription access is available.

### Evaluate End-to-End Outcomes

A practical benchmark should measure more than whether the model produces plausible-looking code.

Track task completion rate, tests passed, total elapsed time, tool errors, human corrections, input and output tokens, and final cost.

A representative evaluation set of at least 30 tasks is a useful starting point, with larger samples needed for stronger statistical confidence.

### Example Agent Prompt

A practical task specification might look like this:

```text
Goal: Fix the failing checkout integration tests.
Scope: packages/checkout and its direct dependencies.
Acceptance criteria:
1. Reproduce the failure before editing.
2. Make the smallest change that addresses the root cause.
3. Run the relevant unit and integration tests.
4. Report changed files, test results, and unresolved risks.
Constraints:
- Do not modify secrets or deployment configuration.
- Ask for approval before destructive commands.
- Stop and report if test infrastructure is unavailable.
```

This approach makes the agent's work verifiable and helps distinguish genuine completion from a persuasive but untested final response.

## Privacy and Security: Standard Mode vs Contributor Mode

Meta provides different data-use arrangements depending on how the model is accessed.

### Meta Model API Standard Mode

According to Meta's published policies, Standard mode does not use submitted prompts, code, files, context, or model responses to train future Meta models.

Regular Muse Wonder is available through the public API in Standard mode only.

### Muse Code Contributor Mode

Muse Code subscriptions default to Contributor mode. This mode allows Meta to use submitted content, including code and model responses, for future model training.

Subscribers can turn Contributor mode off in settings.

The distinction matters for proprietary repositories, customer data, internal documents, and confidential business processes.

**A no-training commitment does not automatically imply zero data retention.** Organizations should review the applicable terms, retention policies, security controls, and contractual requirements before processing sensitive information.

For autonomous agents, least-privilege tool permissions and prompt-injection defenses are also essential. Retrieved webpages, repository comments, and external documents should not be treated as trusted instructions.

## Geographic Restrictions and Regional Availability

Meta's [Model API Geographic Use Policy](https://dev.meta.ai/legal/geographic-use-policy), updated October 12, 2026, identifies territories where access is restricted.

The list includes mainland China, Hong Kong, and Macau, among other jurisdictions.

The policy restricts accessing the service from those territories and offering integrated products to end users located there. It also prohibits bypassing geographic access controls.

Being outside the restricted list does not automatically guarantee service availability. Developers should verify eligibility and account-level access before planning a production integration.

## Muse Wonder Limitations and Common Pitfalls

Despite its strong early benchmark results, Muse Wonder has several important limitations.

- **Muse Wonder Full has no public API:** Applications cannot obtain the higher-tier model simply by changing an API parameter.
- **No downloadable model weights:** Muse Wonder is not an open-weight model suitable for ordinary self-hosted deployment. Meta's Muse Glimmer is a separate open-weight family.
- **Text-only output:** Multimodal input understanding does not mean native image, video, or audio generation through Wonder.
- **High operating costs:** Large-context agent loops can accumulate substantial token charges, even when caching reduces repeated input costs.
- **Agent-harness dependencies:** Tool selection, execution environments, and permissions affect real-world performance.
- **Limited launch history:** Long-term production reliability, regional availability, and subscription economics require further observation.
- **Remaining reasoning errors:** Strong benchmark performance does not eliminate incorrect assumptions, hallucinations, or mistakes in unfamiliar workflows.

A common mistake is treating a model's one-million-token context window as evidence that it will reliably retrieve every important fact from an enormous conversation. Context capacity and retrieval accuracy are different properties.

Another mistake is comparing Wonder Full's agent-benchmark results against the pricing of regular Wonder as though they describe the same publicly accessible API product.

## Muse Wonder vs Claude, GPT-6, and Muse Spark: Which Model Should Developers Choose?

The best model depends on task complexity, access requirements, operating cost, and integration constraints.

**Choose Muse Wonder** when an application needs a strong API-accessible reasoning model for complex coding, document analysis, multimodal understanding, or long-running agent workflows.

**Choose Muse Wonder Full** when high-end coding-agent performance is the priority and the organization can operate within Muse Code's subscription, quota, and availability requirements.

**Choose Muse Spark 1.3** for lower-cost, high-volume workflows that do not require the strongest reasoning performance. Meta lists Standard-mode Spark 1.3 pricing at $1.25 per million input tokens and $4.25 per million output tokens.

**Compare Claude Opus 5.5, Claude Sonnet 5.5, GPT-6 Astra, and Gemini 4 Argon using realistic task-level evaluations.** Artificial Analysis shows Wonder Full competing strongly with leading coding-agent configurations, but benchmark setups and operational characteristics differ.

For many production systems, a multi-model architecture is more economical than using one expensive model for every task.

Routine requests can be handled by a lower-cost model, with difficult cases escalated to a more capable system. The resulting architecture should be evaluated by successful task completion, reliability, latency, and total operating expense.

## Frequently Asked Questions

### What is Muse Wonder?

Muse Wonder is Meta's frontier AI model family for complex reasoning, coding, long-running agents, computer use, and professional knowledge work. It launched on October 12, 2026, with two variants: Muse Wonder and Muse Wonder Full.

### Is Muse Wonder free?

Muse Wonder has a paid, token-metered API. Promotional or trial access may vary by account and should not be assumed. Muse Wonder Full requires an eligible paid Muse Code subscription.

### What is the difference between Muse Wonder and Muse Wonder Full?

Muse Wonder is the standard, API-accessible model. Muse Wonder Full is the higher-capability model available through Muse Code's Power and Maximum subscription plans. Both have one-million-token context windows.

### Can Muse Wonder Full be called through an API?

Not through the public Meta Model API as of October 13, 2026. Its documented access is through eligible Muse Code subscriptions.

### What is the Muse Wonder API model ID?

The native Meta Model API identifier is `muse-wonder-1.0`. OpenRouter uses the separate identifier `meta/muse-wonder`.

### Does Muse Wonder support a one-million-token context window?

Yes. Both variants have a 1,048,576-token context window. This does not guarantee perfect recall across extremely large inputs.

### Is Muse Wonder open source?

No public downloadable weights were announced for Muse Wonder or Muse Wonder Full at launch. They are proprietary hosted models.

### Is Muse Wonder better than Claude Opus 5.5?

Not universally. Wonder Full scored 68 on Artificial Analysis's Coding Agents Index, compared with 66 for the evaluated Claude Opus 5.5 configuration. However, different applications and agent environments may produce different results.

### Does maximum reasoning turn Muse Wonder into Wonder Full?

No. The reasoning-effort setting changes how the selected model processes a task. It does not switch the underlying model to Wonder Full.

### Can Muse Wonder generate images or videos?

Muse Wonder supports visual understanding but produces text output. Meta offers separate products, including Muse Image and Muse Video, for media generation.

### Can existing OpenAI SDK applications use Muse Wonder?

Yes. Meta provides an OpenAI-compatible API. Updating the base URL, authentication, and model identifier is the starting point, although application-specific features still require testing.

## Conclusion

**Muse Wonder represents a significant advance in Meta's frontier AI capabilities**, particularly in agentic coding, long-running task execution, computer use, and professional workflows.

Independent benchmarks indicate a substantial improvement over Muse Spark 1.3, with Muse Wonder Full reaching competitive scores against leading coding-agent configurations. However, the strongest model remains subscription-gated, while regular Wonder carries premium API pricing and significant potential context costs.

The most effective adoption strategy is to **test regular Muse Wonder against an existing model on representative real-world tasks**, measuring completed work, total cost, latency, and human corrections. Organizations considering Wonder Full should also evaluate Muse Code quotas, privacy settings, and regional eligibility.

Explore the [official Muse Wonder model page](https://dev.meta.ai/models/muse-wonder), review the [Meta developer documentation](https://dev.meta.ai/docs/models), and compare the results with [independent Artificial Analysis benchmarks](https://artificialanalysis.ai/articles/muse-wonder/) before deploying the model into production.