Muse Wonder vs. Claude Opus 5.5: Same Price, Different Work

Muse Wonder and Claude Opus 5.5 are priced identically on the first-party API: $4 per million input tokens, $20 per million output tokens, $0.20 for cache, and both offer a 1M token context window. When price drops out of the equation, all that's left is the work itself. This is the cleanest model comparison of the quarter—you no longer need to agonize over budget, just figure out which model suits your task better.

Muse Wonder vs. Claude Opus 5.5

Independent evaluation firm Artificial Analysis gives Opus 5.5 a composite Intelligence Index of 58, and Wonder 52. But in Meta's published agent benchmarks, Wonder leads on long-horizon code execution and legal tasks; and in the API-available version of Wonder, SWE-Marathon also beats Opus. Billed per task, Wonder costs $4.52, while Opus costs $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 1M)	$4 / $20 / $0.20	$4 / $20 / $0.20 (cache read)
Context	1M token	1M token
Input types	Text, image, video, audio	Text, image, PDF
API formats	Responses, Chat Completions, Messages	Messages, plus OpenAI-compatible endpoint
Reasoning control	`effort` from `minimal` to `max`; always reasons	`effort` levels; extended reasoning
First-party rate limits	3,000 RPM / 4M TPM per team	Tiered by spend
Bigger sibling	Muse Wonder Full, only available in Muse Code	None
Weights	Closed source	Closed source

Two differences emerge before the benchmarks even start. Wonder natively supports video and audio input, while Opus 5.5 does not. Additionally, Opus 5.5 is the flagship in Anthropic's lineup, while Wonder is explicitly the smaller of Meta's two models. When you see Meta charts showing its own model winning, be sure to check whether the bar belongs to Wonder or Wonder Full—the latter is only available through the subscription service Muse Code, with no API.

Benchmarks: Meta's Published Table

With effort maxed out, the comparison data published in Meta's model card is 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 published table, Opus wins 5 out of 8 rows. Wonder wins two software engineering metrics and the legal metric. The 6.8-point gap on SWE-Marathon is what Meta emphasizes—this is the long-horizon code task where agents need to keep going.

Independent Evaluation Perspective

Artificial Analysis evaluated both models at maximum effort:

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)	Not disclosed; Wonder Full is 1857	1866
Coding Agents Index	63	66 (with Claude Code)
Output tokens per task	~54,000	Not disclosed
Cost per task	$4.52	$5.98

The knowledge gap is the single largest difference among all metrics: Opus holds nearly twice as many facts as Wonder. Products that rely heavily on information retrieval need to provide Wonder with external grounding, while Opus can sometimes rely directly on its own knowledge. On code agents, Claude Code with Opus 5.5 wins narrowly 66 to 63, and only Wonder Full reaches 68.

Artificial Analysis Intelligence Index is a single score computed by the third-party evaluation firm Artificial Analysis, which aggregates multiple standardized benchmarks (such as MMLU, GPQA, math reasoning, code generation, etc.) into a weighted score for cross-vendor model comparison. It is not a single test, but rather a composite index from 0–100 that normalizes results across knowledge, reasoning, coding, math, and other dimensions. Therefore, a 6-point gap (58 vs. 52) means a comprehensive lag across multiple subdimensions. Note that this index leans toward knowledge and reasoning tasks and does not adequately cover engineering-side factors like long-horizon agent execution or caching costs, so it's better suited for judging "the upper bound of general intelligence" rather than "which is more cost-effective for a specific workload."

AA-Omniscience is an Artificial Analysis metric designed to measure the breadth and accuracy of factual knowledge, testing models on open-domain knowledge across a wide range of disciplines. Opus leads 46 to 25 on this metric, indicating that its parametric memory or training data coverage is significantly broader, which is more favorable for applications that rely on their own knowledge rather than external retrieval.

Billed Per Task, Not Per Token

Identical price sheets can produce different bills. Wonder's $4.52 per task and Opus's $5.98 in the Artificial Analysis index stem from usage patterns: of Wonder's $4.52, only $1.08 is spent on output tokens, while $3.43 is spent on input, with cache writes and reads making up the bulk. Opus's cost leans more toward the output side.

Which model is cheaper depends on the workload:

Long-horizon agent loops that repeatedly send a stable prefix amplify Wonder's cache-heavy advantage (cache at just $0.20/million).
Tasks with short prompts producing long responses diminish Wonder's advantage.

Practical advice: read actual usage from the usage field of each response and calculate your own traffic costs, rather than copying someone else's bill.

Cache write vs. cache read is the key to understanding cost differences. When the same prefix (such as a system prompt, tool definitions, or a long document) is sent repeatedly, providers allow it to be cached, and subsequent requests read from the cache rather than recomputing. Cache reads are typically billed at a steep discount off the input price ($0.20 per million here), but the initial cache write may be billed at the input price or even higher. Therefore, in long-horizon agent loops where a stable prefix appears repeatedly, the cost advantage of caching is greatly amplified; conversely, tasks with short prompts and ever-changing content can barely benefit, and the cost structure reverts to standard input/output pricing. The fact that $3.43 of Wonder's bill goes to input (mostly cache reads and writes) reflects exactly this pattern.

Tools and Ecosystem

Claude Opus 5.5 has the deepest ecosystem: Claude Code, Agent SDK, Managed Agents, availability on Bedrock/Google Cloud/Microsoft Foundry, plus a year of accumulated third-party integrations. Its 18-hour long task execution capability sets the current standard for long-horizon agents that can be exposed via API.

Muse Wonder launched just days ago, and its ecosystem is just beginning. It offers three request formats on the Meta API, Muse Code, Vercel AI Gateway, and Cloudflare Workers AI. Meta's documentation shows how to point Claude Code and Codex at api.meta.ai, which is clearly aimed at users of Anthropic and OpenAI tools. But there is no Managed Agents counterpart yet, and Meta's zero data retention disables server-side state, background requests, file uploads, and web search. During launch week there was also a Muse Code quota consumption bug, fixed the same day. Expect some early bumps within about a month.

SWE-Marathon is a benchmark specifically designed to evaluate agent performance on long-cycle software engineering tasks, where tasks typically require multi-step planning, repeatedly running tests, fixing errors, and pushing forward, rather than generating a single piece of code. It examines whether an agent can keep going after hitting obstacles, maintain context, and ultimately deliver. Wonder leads here 53.1 to 46.3, which is Meta's key differentiating advantage and explains why the article recommends giving long-horizon, multi-step code agents with expected obstacles to Wonder.

Terminal-Bench 4.0-AA focuses on terminal and DevOps scenarios, testing a model's ability to complete system administration, deployment, debugging, and other operations via the command line. Opus 5.5 wins 59.6 to 52.0, indicating it is more reliable at structured, precise command execution, which is the basis for the article's recommendation of Opus for terminal and DevOps agents.

Safety and Data

Both providers recommend human confirmation before agents perform important actions. Meta trained Wonder to verify before performing important actions and to avoid sharing private information without permission. A full safety report has not yet been published, and Meta has not ruled out unguarded scenarios for high-risk chemical and biological capabilities. Anthropic publishes a system card at release.

On data, the Meta API standard mode does not use your traffic for training; but the Muse Code subscription defaults to Contributor mode, which does use it for training. Anthropic does not use API traffic for training by default.

Zero Data Retention is a privacy protection mode in which the provider promises not to store customer request and response data. But the side effect is that many features relying on server-side state stop working, such as background asynchronous requests, file uploads, web search, and session persistence, because these features require the provider to temporarily store data to function. The article mentions that Meta's zero data retention disables server-side state, background requests, file uploads, and web search, which is exactly a manifestation of this trade-off. If your workload depends on these features, you need to weigh privacy against functionality.

Contributor mode is the default setting for the Muse Code subscription, allowing Meta to use user traffic to train models. It differs from the API standard mode's promise of "not used for training," and subscribers need to proactively check and adjust this setting.

Which Model for Which Work

Work type	Choose	Why
Long-horizon code agents, multi-step, expected 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 and research assistants	Opus 5.5	Omniscience 46 vs. 25
Terminal and DevOps agents	Opus 5.5	Terminal-Bench 59.6 vs. 52.0
Health and professional advice	Opus 5.5	HealthBench 65.6 vs. 60.5
Video or audio input	Wonder	Opus 5.5 does not support it
Bedrock/Google Cloud/Foundry workloads	Opus 5.5	Wonder is only available first-party, on Vercel, and on Cloudflare
Interactive coding with the strongest model	Opus 5.5 API or Wonder Full (Muse Code)	Wonder Full is subscription-only

An honest summary: Opus 5.5 is the most intelligent model overall and the safest default choice. When your workload resembles Meta's benchmarks—long-horizon agent execution with tools—or when video and audio input matters, Wonder is worth testing.

Running the Comparison in Apidog

Since both models accept OpenAI-style and Anthropic-style requests, one Apidog project can maintain the same test suite for both.

Create two environments: Meta's base_url is https://api.meta.ai/v1; use the corresponding URL for Anthropic.
Store each API key as a secret variable.
Using the same system prompt, the same tools, and the same task, build a Responses request for Wonder and a Messages request for Opus.
Add assertions to verify the required structured output.
Use post-processors to record usage and the computed cost per run in dollars.
Run at the effort level you use in production, not maxed out.

Your own ten tasks will tell you more than any vendor table.

FAQ

Is Muse Wonder better than Claude Opus 5.5?

Overall, no. Opus 5.5 leads on the independent index 58 to 52, winning 5 of 8 rows in Meta's own table. Wonder leads on software engineering and legal benchmarks, at a lower cost per task.

Are they the same price?

Same price sheet: $4 per million input, $20 per million output, $0.20 per million cache. 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. It can run on Wonder via Meta's Messages endpoint, but the Coding Agents Index shows Claude Code with Opus at 66 and Wonder at 63. The gap only narrows on Wonder Full, which has no API.

Does Muse Wonder Full change the conclusion?

On benchmarks, yes: Full has an index of 56 and leads in most agent rows. In practice, no, unless you work within Muse Code, because Full cannot be deployed via API.

Next Steps

Take your longest agent task and run it on both models at the effort level you use in production, comparing the number of human interventions, output quality, and usage. Price is not the deciding factor here—let the work itself decide.

One-line Summary

Muse Wonder and Claude Opus 5.5 cost the same; Opus leads in overall intelligence, but Wonder is worth testing for long code and legal tasks and audio/video input.

Paragraph Summary

Muse Wonder and Claude Opus 5.5 are priced identically on the first-party API ($4 input, $20 output, $0.20 cache per million tokens, 1M context), so the comparison comes down to task performance. Independent firm Artificial Analysis gives Opus a composite intelligence score of 58 and Wonder 52, with Opus's knowledge nearly double; even in Meta's own published 8 benchmarks, Opus wins 5, with Wonder leading only in SWE-Marathon, DeepSWE, and legal benchmarks. Billed per task, Wonder costs $4.52 and Opus $5.98, with Wonder's cost more dependent on cache reads and writes, suiting long-horizon agent loops. Ecosystem-wise, Opus is deeper (Claude Code, Agent SDK, Bedrock, Foundry), while Wonder is just starting but natively supports video and audio. The conclusion: choose Opus for factual Q&A, terminal DevOps, and health advice; choose Wonder for long-horizon code agents, legal documents, and audio/video input, and test with your own real tasks.