Muse Wonder vs Claude Opus 5.5: Same Price, Different Jobs

When two large models are priced almost identically on the official API, the comparison becomes unusually clean: price drops out of the equation, leaving only "who does this job better." Muse Wonder and Claude Opus 5.5 are a rare control pair of exactly this kind — $4 per million input tokens, $20 per million output tokens, $0.20 per million cached input tokens, and a 1M-token context window on both sides.

On Artificial Analysis's independent index, Opus 5.5 scores 58 and Wonder scores 52. In Meta's own agentic benchmarks, the larger sibling Muse Wonder Full leads on long-horizon coding and legal tasks; even the standard Wonder available on the API beats Opus on SWE-Marathon. On a per-task cost basis, Wonder comes in at $4.52 versus $5.98 for Opus.

This article compares the two across parameters, benchmarks, per-task cost, tooling ecosystem, and data terms, answering the most practical question: which kind of work should go to which model.

Parameters and positioning

Item	Muse Wonder	Claude Opus 5.5
Model ID	muse-wonder-1.0	claude-opus-5-5
Input/output/cached per 1M tokens	$4 / $20 / $0.20	$4 / $20 / $0.20 (cache reads)
Context	1M tokens	1M tokens
Input modalities	Text, image, video, audio	Text, image, PDF
API formats	Responses, Chat Completions, Messages	Messages, plus an OpenAI-compatible endpoint
Reasoning control	effort from minimal to max; always reasoning	Multiple effort levels; extended reasoning
Official API rate limits	3,000 RPM, 4M TPM per org	Tiered by spend level
Larger sibling	Muse Wonder Full, Muse Code only	None
Weights	Closed	Closed

Two things to keep in mind before reading any benchmark. First, Wonder natively supports video and audio input; Opus 5.5 does not. Second, Opus 5.5 is the flagship of Anthropic's lineup, while Wonder is explicitly described by Meta as the smaller of its two models. So when you see a Meta chart beating Opus, check whether that column belongs to Wonder or Wonder Full.

Meta's own benchmarks: Opus wins five of eight

Using Meta's model card at maximum effort, the API-available Wonder versus Opus 5.5 shapes up as follows:

Benchmark	Muse Wonder	Claude Opus 5.5	Winner
Artificial Analysis Intelligence Index	52	58	Opus 5.5
Harvey LAB-AA (legal)	10.0	4.2	Wonder
AutomationBench-AA	39.1	41.2	Opus 5.5
JobBench	70.5	75.4	Opus 5.5
DeepSWE 1.1	78.8	74.2	Wonder
SWE-Marathon	53.1	46.3	Wonder
Terminal-Bench 4.0-AA	52.0	59.6	Opus 5.5
HealthBench Pro	60.5	65.6	Opus 5.5

Even in Meta's own table, Opus wins five of the eight rows. Wonder takes the two software-engineering-leaning groups and the legal group. The 6.8-point gap on SWE-Marathon is also what Meta emphasizes: long-running coding tasks that require an agent to hold up across many steps.

The independent view

Artificial Analysis evaluations at maximum effort on both models:

Metric	Muse Wonder	Claude Opus 5.5
Intelligence Index v4.3	52	58
AA-Omniscience (knowledge)	25	46
Hallucination rate	27%	Not listed in the same table
GDPval-AA Elo	Unavailable; Wonder Full is 1857	1866
Coding Agents Index	63	66 (with Claude Code)
Task output tokens per metric	~54,000	Not listed
Cost per metric task	$4.52	$5.98

The knowledge gap is the single largest difference: by this measurement, Opus holds nearly twice as many facts as Wonder. For retrieval-heavy products, Wonder should be paired with explicit grounding, while Opus can sometimes answer better when context is missing. On coding agents, Claude Code running on Opus 5.5 edges out Wonder 66 to 63; only Wonder Full reaches 68.

Artificial Analysis is an independent third-party model evaluation outfit. It does not rely on data supplied by any vendor; instead it builds its own evaluation harness, calls each model's official API, and publishes its methodology and raw scores. Its flagship product, the Intelligence Index, is a composite that weights and aggregates several sub-areas such as reasoning, knowledge, math, and coding, which is why it is commonly used to compare models across vendors. AA-Omniscience is that outfit's sub-test focused specifically on factual knowledge: a higher score means the model gets more obscure factual questions right without any provided context, and a score near half means the model already has fairly solid world knowledge. GDPval-AA, meanwhile, puts models on tasks closer to real economic value and compares output quality via Elo ranking; adjacent numbers like 1857 and 1866 usually mean the two are in the same tier, and the gap may not be statistically significant.

Cost by task, not just by token

Matching price tags do not mean matching bills. Artificial Analysis measured Wonder at $4.52 per metric task and Opus at $5.98. The difference comes from cost structure: of Wonder's $4.52, only $1.08 is output cost and $3.43 is input cost, most of it cache writes and reads. Opus's cost leans more toward the output side.

Which model is cheaper depends on the workload:

• Long agentic loops that repeatedly send the same stable prefix favor Wonder, because cache costs just $0.20 per million tokens.
• Tasks that produce long answers from short prompts narrow the gap.
• Don't extrapolate from the price sheet; measure usage on your own traffic.

You can compute cost after each response with this formula:

cost =
  (input_tokens / 1_000_000 × 4)
+ (output_tokens / 1_000_000 × 20)
+ (cached_input_tokens / 1_000_000 × 0.20)

The general rule applies to both: read the usage field in each response and recompute cost against real traffic.

Cached input refers to pre-storing a recurring prompt prefix — a system prompt, tool definitions, a long document — on the model side, so that subsequent requests hitting the cache are billed at a very low unit price, avoiding repeated computation and repeated billing each time. It usually involves two actions: writing to the cache is generally billed at the normal or slightly higher input price, while cache-hit reads get the $0.20 per million token discount, so caching only really saves money once the prefix is reused enough times. This explains why $3.43 of Wonder's $4.52 per task is input: a long agentic loop resends the same context at every step, so the write and read shares naturally run high. Conversely, when a short prompt produces a long answer, the cache hit rate is low, the input-side advantage is diluted, and the cost moves closer to Opus.

Tooling and ecosystem

Claude Opus 5.5 has the deeper ecosystem: Claude Code, the Agent SDK, Managed Agents, availability on Bedrock, Google Cloud, and Microsoft Foundry, plus a large number of third-party integrations. Running long tasks on the API (up to 18 hours) set the standard for long-horizon agents.

Muse Wonder's systems ecosystem is still young, only days after launch. The model is already on Meta's own API (with three request formats), Muse Code, Vercel AI Gateway, and Cloudflare Workers AI. Meta's documentation also guides you on pointing Claude Code and Codex at api.meta.ai, aiming squarely at users of Anthropic's and OpenAI's tool harnesses. Still, Wonder has no counterpart to Managed Agents. Meta's no-retention mode also turns off server-side state, background tasks, file uploads, and web search.

Launch week also brought a bug where Muse Code quotas were exhausted quickly, though it was fixed the same day. For a new model at this stage, it's reasonable to expect some rough edges in the first month.

Managed Agents is a class of hosted capability from Anthropic: developers don't need to maintain their own task queues, state stores, and retry logic, because the server keeps the agent running until completion or timeout — the officially cited long tasks of up to 18 hours depend on exactly this layer of infrastructure. Lacking a counterpart means anyone who wants to run long tasks must implement orchestration, persistence, and resumability themselves, a clearly higher engineering investment. Bedrock, Google Cloud, and Microsoft Foundry, meanwhile, are the three major cloud vendors' model hosting platforms; once a model is listed, enterprises can call it within their existing cloud accounts, compliance boundaries, and private networks, without separately integrating with the vendor's API. For teams whose procurement processes require going through a cloud marketplace, or whose data must stay inside their own VPC, whether a model is listed often determines selection more than benchmark scores do.

Safety and data

Both Meta and Anthropic recommend human confirmation before consequential agentic actions. Meta trained Wonder to confirm before important actions and not to share private information without permission. A full safety report is not yet complete, and Meta does not rule out the possibility of high-risk chemical or biological capabilities in the absence of safeguards. Anthropic published a system card at launch, and the prompt injection details regarding Opus 5.5 include an 85% figure.

On data, Meta API's Standard mode does not train on your traffic. Signing up for Muse Code defaults to Contributor mode, which does train. Anthropic does not train on API traffic by default.

Prompt injection refers to an attacker hiding malicious instructions in content the model will read — web pages, documents, tool return values — inducing the agent to deviate from its original task and perform dangerous actions, such as exfiltrating private data or calling tools it shouldn't. The 85% figure Anthropic gives in its system card is generally understood as the proportion of cases where the model refuses or resists injection under some controlled attack test, but the exact definition needs to be checked against the original document and cannot be taken directly as a general protection rate. No-retention mode, meanwhile, requires the server not to store requests and responses, at the cost of disabling any feature that depends on server-side state, which is why Meta's such mode turns off background tasks, file uploads, and web search: those capabilities inherently require the server to retain intermediate data.

Which job goes to which model

Work	Choose	Why
Long, multi-step coding agents expected to hit obstacles	Try Wonder	SWE-Marathon 53.1 vs 46.3; cache-heavy cost structure
Legal document agents	Try Wonder	Harvey LAB 10.0 vs 4.2
Factual Q&A, research assistants	Opus 5.5	Omniscience 46 vs 25
Terminal and DevOps agents	Opus 5.5	Terminal-Bench 59.6 vs 52.0
Health, professional advisory	Opus 5.5	HealthBench 65.6 vs 60.5
Video or audio input	Wonder	Opus 5.5 does not support it
Deploying to Bedrock, Google Cloud, or Foundry	Opus 5.5	Wonder is limited to the official API, Vercel, and Cloudflare
Interactive coding with the strongest model	Opus 5.5 API, or Wonder Full in Muse Code	Wonder Full is subscription-only

An honest summary: Opus 5.5 is the smarter general-purpose model and the safer default. When the workload resembles Meta's benchmark shapes — especially long agentic runs paired with tools — or when video or audio input is a hard requirement, Wonder is worth a try.

Running the comparison in Apidog

Since both models accept OpenAI- and Anthropic-style requests, a single Apidog project can hold the same test cases for both. Here's how:

1. Create a Meta environment with base_url set to https://api.meta.ai/v1.
2. Create an Anthropic environment with the corresponding base URL.
3. Set each vendor's API key as a secret variable.
4. Create a Responses request for Wonder and a Messages request for Opus.
5. Keep the same system prompt, tools, and tasks.
6. Verify the output structure your application requires.
7. Add a post-processor that logs usage and dollar cost after each run.
8. Run at the effort level you actually deploy, not the maximum.

Ten tasks from your own system beat any vendor's benchmark table. Run both models side by side in the same workspace, and the answer will surface on its own.

One-line Summary

Muse Wonder and Claude Opus 5.5 share identical pricing and context; choose Opus for knowledge-heavy tasks, Wonder for long-horizon coding and video/audio scenarios.

Paragraph Summary

Muse Wonder and Claude Opus 5.5 are priced identically on the official API ($4 input, $20 output, $0.20 cached per million tokens) with the same 1M-token context, so the comparison comes down to who does the job better. In Meta's own benchmarks, Opus wins five of eight rows, while Wonder leads only on SWE-Marathon and Harvey LAB; independent evaluation shows Opus's biggest edge is in knowledge (Omniscience 46 vs 25), while Wonder's per-task cost is lower ($4.52 vs $5.98), largely due to its cache-heavy cost structure. Wonder additionally supports video and audio input, but its ecosystem is young, it lacks counterparts to Managed Agents, and Contributor mode trains on traffic by default. The conclusion: give knowledge, terminal, and health tasks to Opus; try Wonder for long-horizon coding, legal, and multimedia input; and measure costs on your own real traffic.