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

Same Price, Different Lanes

Muse Wonder and Claude Opus 5.5 are priced identically on their official APIs: $4 per million input tokens, $20 per million output tokens, and $0.20 per million cached-read tokens. Both offer a 1-million-token context window. Price is completely removed from the comparison equation, leaving only actual performance.

According to independent evaluator Artificial Analysis, Opus 5.5 scores 58 on the composite intelligence index while Wonder scores 52. But in Meta's own agent benchmarks, Wonder leads on long-horizon coding and legal tasks (SWE-Marathon and Harvey LAB), and comes in cheaper per task—$4.52 versus $5.98.

Spec Comparison

Item	Muse Wonder	Claude Opus 5.5
Model ID	muse-wonder-1.0	claude-opus-5-5
Input/Output/Cache (per million tokens)	$4 / $20 / $0.20	$4 / $20 / $0.20 (cache read)
Context	1M tokens	1M tokens
Input modalities	Text, image, video, audio	Text, image, PDF
API formats	Responses, Chat Completions, Messages	Messages + OpenAI-compatible endpoint
Reasoning control	effort from minimal to max, always reasons	Tiered effort, extended thinking
Self-serve rate limits	3,000 RPM and 4M TPM per team	Tiered by spend
Larger model	Muse Wonder Full (Muse Code only)	None
Model weights	Closed source	Closed source

The two most striking differences: Wonder supports video and audio input while Opus 5.5 does not; and Opus currently has a deeper tool ecosystem.

Effort tiers (also called reasoning effort or thinking budget) are the standard control on today's reasoning models, letting callers trade off between "fast and cheap" and "deep thinking." The higher the tier, the more internal reasoning tokens the model spends before answering, driving up cost and latency.

The article specifically cautions: "run each model at the effort tier you will actually use"—because vendor benchmarks are often measured at maximum effort, while production environments use mid-to-low tiers to control cost. At those levels, the relative performance of the two models can be entirely different.

The Responses API is OpenAI's newer interface, offering better support than Chat Completions for stateful conversations, built-in tool calling, and reasoning item passing; the Messages API is Anthropic's native format. Wonder's support for all three formats means low migration cost—harnesses like Claude Code and Codex can switch endpoints almost directly.

Benchmarks: Overall Results

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

Opus wins five of eight rows—even though this table comes from Meta's own benchmarks. Wonder wins in two areas: software engineering and legal tasks. Meta highlights the 6.8-point gap on SWE-Marathon, a benchmark that measures long-running, multi-step coding tasks.

Benchmarks from an Independent View

Artificial Analysis scored both at the highest effort tier:

Metric	Muse Wonder	Claude Opus 5.5
Intelligence Index v4.3	52	58
AA-Omniscience Knowledge	25	46
Hallucination rate	27%	Not in the same table
GDPval-AA Elo	No Wonder API result; Wonder Full scores 1857	1866
Coding Agents Index	63	66 (Claude Code)
Output tokens per task	~54,000	Not disclosed
Cost per task	$4.52	$5.98

The knowledge gap is the largest in the table. This metric shows Opus is nearly twice as capable at answering factual questions. Tasks that depend on information retrieval should apply more explicit grounding for Wonder; Opus can sometimes skip that step. On coding agents, Claude Code running on Opus 5.5 edges ahead 66 to 63, and only Wonder Full reaches 68.

Artificial Analysis Intelligence Index is a composite capability score maintained by independent evaluator Artificial Analysis. It aggregates dozens of public benchmarks (covering reasoning, knowledge, coding, math, instruction following, and more) into a single score (currently version v4.3). Its value is cross-model comparability: because vendors like to cherry-pick benchmarks that favor them, an independent index provides a common yardstick.

AA-Omniscience is the sub-score specifically measuring the breadth of factual knowledge. The higher the score, the better the model answers factual questions directly, without external retrieval. This is exactly why "hallucination rate" metrics matter—a model that doesn't know the answer but responds anyway hallucinates. Opus's Omniscience of 46 versus Wonder's 25 in the article means that retrieval-dependent tasks (such as research assistants or factual Q&A) must pair Wonder with RAG or grounding to work reliably.

GDPval-AA Elo uses a chess-like Elo rating to measure relative performance on open-ended real-world work tasks, where a gap of a few dozen points usually signals a generational difference.

Cost per Task, Not Cost per Token

Identical unit pricing doesn't mean identical bills. Artificial Analysis measured Wonder at $4.52 per task on its index and Opus at $5.98. The difference comes from token usage profiles, not list prices.

Wonder spends only $1.08 on output tokens (out of $4.52 total), while $3.43 goes to input—most of it cache writes and reads. Opus's cost skews more toward the output side. So which is cheaper depends on the real workload:

• Long agent loops that repeatedly send the same prefix suit Wonder's cache-heavy profile.
• Tasks that generate long answers from short prompts narrow the cost gap, since output price is the same.

The general rule for both models: read the usage field in every response and calculate cost against your own traffic:

cost =
  (input_tokens / 1,000,000) × input_price
+ (output_tokens / 1,000,000) × output_price
+ (cache_read_tokens / 1,000,000) × cache_read_price

If your API already returns itemized usage, log that value on every run and compare by cost per task (not cost per request).

The key concept here is prompt caching. When an agent loop repeatedly sends the same prefix (system prompt, tool definitions, conversation history), the provider can cache that portion, and subsequent calls are billed at a cache-read price far below the input price ($0.20 versus $4 input in this case—20× cheaper).

Wonder's cost profile skews toward caching: $3.43 goes to input, most of it cache writes and reads, indicating its workflow heavily reuses long prefixes. Cache writes themselves are typically billed at the input price (or slightly higher) and only pay off on subsequent reads. So for tasks with "short prompts and one-off long outputs," Wonder's caching advantage doesn't materialize—it actually loses out on cache writes.

Understanding this is necessary to correctly interpret "cost per task"—it isn't a fixed property of model pricing but the product of model behavior × your traffic pattern. The same model under different agent architectures can vary severalfold in cost per task.

Tools and Ecosystem

Claude Opus 5.5 has the deeper ecosystem, spanning Claude Code, Agent SDK, Managed Agents, Bedrock, Google Cloud, Microsoft Foundry, and a year's worth of accumulated third-party integrations. Its API design for long-horizon tasks (18-hour runs) still sets a new standard for long-running agents.

Muse Wonder is only days old, and its ecosystem is relatively young. It currently includes Meta's official API (supporting three request formats), Muse Code, Vercel AI Gateway, and Cloudflare Workers AI. Meta's documentation shows how to run Claude Code and Codex, pointing at api.meta.ai, directly targeting Anthropic's and OpenAI's harness users—but there's no equivalent to Managed Agents yet, and Meta's no-data-retention mode disables server-side state, background requests, file uploads, and web search.

Launch week also saw a bug that exhausted Muse Code quotas, fixed the same day. Expect roughly a month before the system stabilizes.

Safety and Data

Both vendors recommend human confirmation before agents take significant actions. Meta trained Wonder to request confirmation before critical operations and to avoid sharing personal information without authorization. However, a full safety report has not yet been released, and without safeguards in place, conclusions on high-risk chemical and biological capabilities can't be drawn.

Anthropic published a system card at launch.

On data, Meta API's Standard mode won't train on your traffic, but signing up for Muse Code defaults to Contributor mode, which trains on your usage data. Anthropic does not train on API traffic by default.

Which Model Fits Which Task

Task	Recommended Option	Reason
Long-horizon, multi-step coding agents expected to hit obstacles	Try Wonder	SWE-Marathon 53.1 vs 46.3, plus cache-heavy cost profile
Legal document agents	Try Wonder	Harvey LAB 10.0 vs 4.2
Factual Q&A and research assistants	Opus 5.5	Omniscience 46 vs 25
Terminal agents and DevOps	Opus 5.5	Terminal-Bench 59.6 vs 52.0
Health and professional-grade advice	Opus 5.5	HealthBench 65.6 vs 60.5
Video or audio input	Wonder	Opus 5.5 doesn't support it
Running on Bedrock, Google Cloud, or Foundry	Opus 5.5	Wonder is only on its official API, Vercel, and Cloudflare
Interactive coding with the strongest model	Opus 5.5 API or Wonder Full (Muse Code)	Wonder Full is members-only

Put plainly: Opus 5.5 is the smarter general-purpose model and the safer default; Wonder is the one to try when your workload resembles Meta's benchmarks—such as long-running agents with tools, or when you need video and audio input.

Run Your Own Comparison in Apidog

Since both models accept OpenAI- and Anthropic-format requests, you can keep one test suite in a single Apidog project and split it into two environments:

1. Create an environment named Meta and set base_url to https://api.meta.ai/v1
2. Create an environment named Anthropic and set Anthropic's base URL
3. Store each API key as a secret variable
4. Create a Responses request set for Wonder and a Messages request set for Opus
5. Use the same system prompt, tools, and tasks across all tests
6. Validate results against the structure you expect
7. Add a post-processor that logs usage and dollar cost on every run

Run each model at the effort tier you'll actually use, not always at the maximum. Benchmarks show trends, but your own ten real tasks are more valuable for decision-making than any vendor's table.

FAQ

Is Muse Wonder better than Claude Opus 5.5?

Overall, no. Opus 5.5 leads the independent index 58 to 52 and wins five of eight rows in Meta's own table. Wonder leads on software engineering and legal benchmarks and comes in cheaper per task.

Are the prices the same?

Yes—list price is $4 per million input, $20 per million output, and $0.20 per million cached. But per task, Artificial Analysis measured Wonder at $4.52 and Opus at $5.98.

Which is better for Claude Code?

Claude Code is built for Claude, and the Coding Agents Index gives Claude Code on Opus a 66 versus Wonder's 63. That said, Claude Code can also run on Wonder via Meta's Messages endpoint, and the gap only narrows with Wonder Full—which has no API.

Does Muse Wonder Full change the answer?

On benchmarks, yes. Wonder Full scores 56 on the index and leads most agent rows, but the practical limitation is that it's exclusive to Muse Code and can't be deployed via API on your own.

Next Steps

Pick your longest agent task, run both models at the effort tier you'll actually use, and compare the number of human interventions, output quality, and usage. Price isn't the deciding factor in this comparison—your real workload is.