Muse Wonder vs Opus 5.5

Muse Wonder vs Opus 5.5 is a workflow decision: both charge $4 per million input tokens, $0.20 for cached input, and $20 for output. Meta's Muse Wonder deserves a trial for sustained coding runs; Anthropic's Claude Opus 5.5 is the stronger default for finance agents and a team already working in Claude Code. Muse Wonder Full is a separate, subscription-only choice, not the model those API prices buy.

Muse Wonder vs Opus 5.5: Which Should You Pick?

Keep Claude Opus 5.5 as your default if you already use Claude Code or need a general document-and-finance agent. Put Muse Wonder on the shortlist when your hardest problem is keeping a coding agent productive through a long sequence of changes. Choose neither if downloadable open weights are a requirement.

That recommendation turns on three things: the work being measured, the product you can access, and the workflow you would have to replace. The evidence here is current vendor documentation and Meta-reported benchmark results, not a hands-on trial of either model.

Prices and context limits were verified on 13 October 2026 against Meta's Muse Wonder model page, Anthropic's API pricing, and Anthropic's Opus 5.5 model documentation. All prices below are USD.

A context window is the material a model can work with in a request, including supplied history and documents. Matching context limits do not establish matching recall, and a model is only part of an agent. The surrounding software decides which tools it can call, what it remembers, and when it stops.

Pick This If: Four Jobs

For a founder, the practical split is straightforward. An agent maintaining a mature application is a different purchase from an agent preparing a finance research memo. Buy for the job that is failing today, not the most favorable number in a launch table.

Same API Price: What the Token Math Does and Does Not Say

There is no volume crossover in the three shared list rates: equal billable token counts produce the same subtotal. A token is a unit of text processing; inputs are material sent to the model and outputs are material it generates. Cached inputs reuse previously processed material at the listed cache-read rate.

Normalized to 1,000 tokens, both models cost $0.004 for input, $0.0002 for cached input, and $0.020 for output. These are calculated from the makers' million-token rates, not prices per request. Meta's rate card and Anthropic's rate card supply the inputs.

Consider an illustrative accounting basket of 1 million input tokens and 100,000 output tokens, spread across requests:

• With all input billed at the ordinary rate: $4.00 + $2.00 = $6.00 on either model.
• With 900,000 input tokens billed as cache reads and 100,000 at the ordinary rate: $0.18 + $0.40 + $2.00 = $2.58 on either model.

That is a 57% reduction in this defined subtotal, entirely from changing the input mix. It is not evidence that either model completes a job for $2.58. Different tokenization, reasoning, retries and successful completion rates can produce different bills for the same assignment.

In millions of tokens, the shared subtotal is 4 × uncached input + 0.20 × cache reads + 20 × output. Multiplying the workload does not make one vendor cheaper under that formula. The choice flips only when the actual usage or other charges differ, or the output becomes more useful.

Cache creation is a separate consideration. Anthropic lists Opus 5.5 cache writes at $5 per million tokens for five minutes and $8 for one hour. Meta's Wonder model page lists a cached-input price but does not specify a separate cache-write rate. The chart excludes cache writes on both sides rather than assuming equivalent cache behavior or free cache creation. Anthropic's caching prices and Meta's published pricing fields define that coverage gap.

For an engineering lead, this makes the purchase test concrete: compare the invoice and the accepted result together. A cheaper incomplete patch is not a saving. A longer successful run can be worth paying for, but that is something your evaluation must establish.

How to Reach Muse Wonder, Wonder Full and Opus 5.5

Muse Wonder: API Access or Muse Code

Muse Wonder is Meta's hosted frontier model, available as muse-wonder-1.0 through Meta Model API and through Muse Code, its terminal coding agent. Choose the API when you are building your own agent; choose Muse Code when you want Meta's coding product around the model. Meta describes its API as a public preview and says developers can adapt an existing OpenAI SDK client to it. That compatibility is a starting point, not proof that every tool definition and agent behavior will transfer unchanged. Meta Model API.

Muse Wonder Full is not on the API. Meta's model page restricts it to Muse Code's Power Usage, $50 per month, and Maximum Usage, $200 per month, plans. The listed name muse-wonder-full-1.0 does not make it an API endpoint you can buy at Wonder's token rates. Wonder Full access and plans.

That restriction matters most to a CTO building a service. A favorable Full score does not establish what your production API agent can do, and a developer subscription cannot be priced as if it were unrestricted API capacity. For a builder who specifically wants Full inside Muse Code, those plans are the relevant purchase.

Meta's launch page describes the wider Muse app rollout as forthcoming, beginning with subscribers. Do not treat an announced app rollout as an additional access route already available to every user. Meta's launch announcement.

For the surrounding terminal workflow, see how to use Muse Code. The reason to trial Wonder is its fit for sustained work; skip it as a migration project if you cannot name the current failure it should solve.

Claude Opus 5.5: API, Apps and Claude Code

Claude Opus 5.5 is Anthropic's hosted model for agentic coding and knowledge work. Developers reach it as claude-opus-5-5 through the Claude API; users also reach it in the Claude apps and Claude Code, subject to the access and limits of their account. The model has a 1M-token context window and a 128K-token standard output limit. Opus 5.5 model documentation and Anthropic's launch page.

Its strongest purchasing argument for an existing Claude Code user is continuity. You can evaluate the model in a workflow you already understand. That does not guarantee better outputs, but it avoids making a model experiment depend on adopting another coding product at the same time.

There are implementation limits to check. Opus 5.5's adaptive thinking is always on, and forced tool use is not supported. If your custom agent assumes it can disable reasoning or force a particular tool call, that assumption needs revision. Anthropic's documented behavior changes.

The wider Claude API pricing guide covers the surrounding bill. For a self-contained model assessment, read Claude Opus 5.5 for agents and coding. Skip either hosted model if your deployment requires the underlying weights on infrastructure you control.

Meta-Reported Benchmarks: Where Each Model Wins

Muse Wonder leads the sustained-coding rows; Opus 5.5 leads terminal coding and building programs from scratch. These are different tests of coding work, so a single overall winner would hide the decision you need to make.

Every score below is Meta-reported, copied from Meta's model-page comparison. The pair order is Muse Wonder / Claude Opus 5.5, both columns labeled max by Meta. These are the page's score values, not our measurements or an independently verified head-to-head. Benchmark names identify different evaluations; do not average them into a homemade overall score.

Meta's model card attributes DeepSWE results for these models to Datacurve, SWE-Marathon to Abundant AI, the AA-labeled rows to Artificial Analysis, finance to Vals AI, and GDP.pdf to Surge. It mixes third-party, provider-reported and internal evaluations; SWE-Marathon is explicitly not a controlled comparison using the same agent software. MemoryBench is Meta's internal evaluation. These provenance differences limit how directly the scores transfer to your deployment.

Long Coding Runs: Muse Wonder Wins the Relevant Reported Rows

• DeepSWE 1.1: 78.8 / 74.2.
• SWE-Marathon: 53.1 / 46.3.
• Terminal-Bench 4.0-AA: 52.0 / 59.6.
• ProgramBench: 50.0 / 70.5.

Winner for the sustained-coding shortlist: Muse Wonder. Winner for terminal tasks and from-scratch programming: Opus 5.5. All four results are Meta-reported. ProgramBench tests clean-room program reconstruction, which is narrower than every kind of new application work. The useful question is whether your backlog resembles sustained repository changes or producing a program against a defined behavioral target.

For example, a solo builder maintaining a sprawling integration should include Wonder in an evaluation that preserves the same acceptance tests across models. A founder commissioning a new internal tool should start with Opus, then judge the working artifact. Neither choice follows merely from the word “coding.”

Full must remain separate: Meta lists 79.0 on DeepSWE, 57.5 on SWE-Marathon, and 56 on the Artificial Analysis Index for Wonder Full. Its SWE-Marathon cell is explicitly marked xhigh, despite the column heading saying max. Those are not the base Wonder scores above, and they do not establish API performance. Meta-reported Full column.

Document and Finance Agents: Opus Is the Default, With a Legal-Work Exception

• Harvey LAB-AA: 10.0 / 4.2.
• Vals Finance Agent V2: 56.6 / 58.6.
• HealthBench Pro: 60.5 / 65.6.
• AutomationBench-AA: 39.1 / 41.2.
• GDP.pdf: 30.8 / 30.6.
• JobBench: 70.5 / 75.4.

Winner for a finance-heavy agent: Opus 5.5. Winner on the reported legal-agent row: Muse Wonder. These Meta-reported scores support a workload-specific shortlist, not a claim that one model understands every business document better.

For a senior operator assembling a financial research packet, evaluate whether the output preserves source citations, distinguishes assumptions from reported figures, and reconciles the numbers. Opus is the starting choice because the finance row points there. Wonder's small lead on GDP.pdf does not erase that distinction; its Harvey result is a separate reason to evaluate legal-document work.

The scores do not measure your approval process or your organization's tolerance for a wrong assertion. Keep the final deliverable, source checking and human review in the acceptance criteria.

Memory, Computer Use and General Scores

• MemoryBench: 85.6 / 81.5.
• OSWorld V2.1: 72.6 / 81.8 on partial scoring; Wonder's binary score is 34.3, while Opus's binary cell is unreported.
• Personal Superintelligence Index: 54.3 / 55.2, which Meta describes as its internal composite.
• Artificial Analysis Index: 52 / 58, as reproduced by Meta.

Winner on the reported memory row: Muse Wonder. Winner on reported computer-use partial scoring and the general index: Opus 5.5. The absent OSWorld binary result is missing data, not zero. The Artificial Analysis Index is attributed to Artificial Analysis as the named index; the scores here were read on Meta's page, not verified against an independent run.

This also limits what “max” tells you. Anthropic documents Opus 5.5's default effort as medium. A max-effort benchmark is not a promise about the behavior, speed or spending of the default configuration you deploy. Opus effort defaults.

Active Memory and Active Focus Are Meta's Claims

Meta claims Active Memory retrieves relevant earlier context, while Active Focus helps the model keep working through obstacles. Its launch explanation describes memory across conversations and continued progress through alternative approaches, clarification and check-ins. Those are Meta's descriptions, not capabilities independently exercised for this comparison. Meta's explanation.

The distinction is useful even before testing. An agent that remembers a customer's constraint but stops after the first failed tool call has a persistence problem. An agent that works for hours after forgetting that constraint has a memory problem. Your evaluation should catch both.

For a custom API service, the brand names do not settle which history is stored, who can retrieve it, or which actions require permission. Define those behaviors in your application and check what the specific product exposes. A model-level claim does not substitute for a documented memory interface or your own access controls.

API Data Handling: What the Makers State

Anthropic makes an explicit default no-training statement for API data; Meta's Wonder model page does not state a Wonder-specific API training policy. That is a documentation distinction, not evidence that Meta necessarily trains on the data.

Anthropic says it does not use commercial-product inputs or outputs to train its models by default. It also says explicitly submitted feedback, bug reports or data a customer chooses to allow may be used. The policy identifies the Anthropic API as a commercial product. Anthropic's commercial training policy.

Meta's Wonder model page does not give that statement. Its Model API page labels data use for some Spark entries, but the Wonder row does not carry the same clarification. A promise attached to another model is not a Wonder policy.

Winner on clarity in the reviewed API documentation: Anthropic. Before moving private source code or finance documents to a new provider, get the terms for the exact product and account you intend to use. Training, retention and permission to process data are separate questions; a no-training default answers only the first.

Do not automatically apply the API policy to a personal Claude subscription either. Anthropic publishes a separate consumer policy, including Claude Code when accessed with consumer accounts.

Switching From Claude to Muse Wonder: What Actually Moves

A team already productive in Claude Code should keep it until a Wonder pilot demonstrates a specific gain. The token rate card supplies no savings cushion for migration. The relevant cost is the work of changing the system and checking that it still behaves correctly.

For an API agent, inventory the model-specific request format, tool schemas, prompts, cached prefixes, stored conversation state and error handling. Keep business records and evaluation fixtures portable. Do not assume a saved reasoning transcript is a transferable memory format: Anthropic specifically ties thinking blocks to the model and conversation that produced them. Opus model behavior.

For a coding-product switch, inventory repository instructions, permissions, integrations and review habits. Measure whether the new product finishes the work with less intervention; replacing a familiar interface is worthwhile only when the result earns it. For the reverse move, from Muse Wonder to Opus, use the same rule and retain your task fixtures rather than translating vendor claims into expectations.

Do not switch merely to obtain the same published context size, the same token prices, or a Full benchmark result unavailable to your API deployment. Wonder is a credible alternative to Opus for sustained coding; it is not automatically a replacement for every part of Claude Code.

Monday's Move: Run a Narrow Buying Evaluation

1. 1

   Choose the job that is failing

   Pick an existing repository change for sustained coding, or a document-and-finance assignment with source files and an expected deliverable. Write down what success means before comparing outputs.

2. 2

   Compare the products you can buy

   Use muse-wonder-1.0 and claude-opus-5-5 for the API comparison. Evaluate Wonder Full separately inside an eligible Muse Code plan. Record the effort setting, tool access and approval rules for each run.

3. 3

   Record outcomes alongside usage

   Keep accepted outputs, failures, retries, review time and the actual usage report. Decide whether the gain survives your existing workflow. Promote the successful configuration for that job before considering a wider migration.

Frequently Asked Questions

Is Muse Wonder better than Claude Opus 5.5?

For sustained coding, Muse Wonder leads the DeepSWE and SWE-Marathon rows reported by Meta. Opus leads the reported terminal, from-scratch programming and finance rows. Use that split to choose what to evaluate, not as proof of a universal winner.

Is Muse Wonder cheaper than Claude Opus 5.5?

Their published ordinary input, cache-read and output rates match: $4, $0.20 and $20 per million tokens. Equal token counts produce equal subtotals for those categories. Cache writes, actual usage and other charges still affect the invoice.

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

Wonder is available through Meta Model API and Muse Code. Wonder Full has separate benchmark results and is available only through Muse Code's Power Usage and Maximum Usage subscriptions. It is not the API model sold at Wonder's token rates.

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