Muse Wonder vs Claude Opus 5.5: Same Price, Different Fate — Which Fits Your Workflow?

When two flagship large models have identical API pricing — $4 per million input tokens, $20 per million output tokens, $0.20 for cache reads, and a 1 million token context window — price drops out of the comparison entirely. What's left is a pure contest of capability and fit.

That's exactly the situation between Meta's Muse Wonder and Anthropic's Claude Opus 5.5. Independent evaluator Artificial Analysis gives Opus 5.5 a composite intelligence index of 58 versus Wonder's 52. But in Meta's own agentic benchmarks, Wonder's "big brother" version beats Opus on long-horizon coding and legal tasks, and even the API version of Wonder leads Opus on SWE-Marathon.

What's more interesting is the cost structure: measured by actual per-task spend, Wonder burns $4.52 while Opus burns $5.98 — same price list, different bill. This article breaks down both models across five dimensions — specs, benchmarks, per-task cost, tool ecosystem, and data terms — and closes with a practical question: which tasks should go to whom.

Specs: Two Divergent Design Philosophies

Item	Muse Wonder	Claude Opus 5.5
Model ID	muse-wonder-1.0	claude-opus-5-5
Input/Output/Cache (per million)	$4 / $20 / $0.20	$4 / $20 / $0.20
Context window	1M tokens	1M tokens
Input modalities	Text, image, video, audio	Text, image, PDF
API surface	Responses, Chat Completions, Messages	Messages (with OpenAI-compatible endpoint)
Reasoning control	effort from minimal to max, always reasoning	Tiered effort, extended thinking
Rate limits (first-party)	3,000 req/min, 4M tokens/min per team	By spend tier
"Large" variant	Muse Wonder Full (via Muse Code only)	None

Before getting into any benchmark, two differences are worth flagging. Wonder natively accepts video and audio input; Opus 5.5 does not. Also, Opus 5.5 is the absolute flagship of Anthropic's lineup, while Wonder is explicitly positioned as the smaller of Meta's two models. When Meta posts a score that beats Opus, you need to first confirm whether it's Wonder or Full — that detail flips the conclusion.

Context window and cache pricing: A 1M-token context window means a single request can hold roughly 750,000 English words or millions of lines of code — enough for an entire mid-sized codebase. But the real cost bottleneck for long context is caching: when an agentic loop resends the same prefix on every turn, cache-miss input is billed at $4 per million, while cache reads cost only $0.20 — a 20x difference. The mix of "cache write" versus "cache read" usage ends up determining your actual bill more than the sticker price does. That also explains how two identically priced models can diverge so much on per-task cost.

The effort parameter: Both vendors offer tiered reasoning investment controls (from minimal to max). Higher effort lets the model run longer internal chains of thought before producing the final answer, typically improving performance on complex reasoning tasks but significantly increasing output token consumption and latency. Comparing models without pinning the effort level makes both benchmark scores and cost incomparable.

Benchmarks: The Real Scorecard in Meta's Own Table

Data from Meta's model card at maximum effort, comparing the API-available Wonder against Opus 5.5:

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 eight. Wonder takes two software-engineering rows and one legal row. The 6.8-point margin on SWE-Marathon is what Meta emphasizes: it tests an agent's ability to keep grinding through long-horizon programming tasks.

SWE-Marathon vs Terminal-Bench: SWE-Marathon measures an agent's stamina on very long, multi-step software engineering tasks — testing context retention, error recovery, and cross-session planning, closer to a "hackathon"-style workflow. Terminal-Bench focuses on command execution, environment configuration, and DevOps operations in a terminal setting, with more immediate single-step feedback and clearer task boundaries. The differing scores show that a model good at long-horizon planning isn't necessarily good at precise interactive terminal work, and vice versa.

AA-Omniscience: Artificial Analysis's knowledge evaluation dimension, which uses large volumes of factual Q&A to measure static knowledge baked into a model's parameters rather than retrieval or reasoning. A big score gap means a clear difference in "naked answering" ability without external retrieval — for RAG-based products, this gap can be closed by retrieval; for scenarios where you want the model to answer directly, it's a hard constraint.

Independent Evaluation: The Knowledge Gap Is the Biggest Variable

Artificial Analysis's independent evaluation of both models at maximum effort:

Metric	Muse Wonder	Claude Opus 5.5
Intelligence Index v4.3	52	58
AA-Omniscience (knowledge)	25	46
Cost per index task	$4.52	$5.98

The knowledge dimension is the single largest directional gap between the two — Opus holds roughly twice as many facts as Wonder. For products heavily reliant on retrieval, Wonder needs more retrieval augmentation, while Opus can sometimes answer directly. In coding agent scenarios, Claude Code on Opus 5.5 scores 66 versus Wonder's 63 — a small gap; only Wonder Full reaches 68.

Per-Task Cost: Same Pricing, Different Bill

The price list is identical, but the actual bill isn't. Wonder's $4.52 per index task versus Opus's $5.98 comes down to output volume differences: of Wonder's $4.52, only $1.08 goes to output tokens, and $3.43 goes to input — most of that cache writes and reads. Opus's cost skews toward output.

Which model saves more depends on your actual workload. Long-horizon agentic loops that resend a fixed prefix will favor Wonder's cache-heavy cost structure — cache reads at just $0.20 per million. Short-prompt tasks that generate very long responses will narrow the gap. The safest approach is to read the usage field in every response and price your workload yourself.

Tool Ecosystem: Depth vs Freshness

Opus 5.5 has the deeper ecosystem: Claude Code, the Agent SDK, Managed Agents, availability on Bedrock, Google Cloud, and Microsoft Foundry, plus a year's worth of third-party framework integrations. Long-running tasks are its baseline in large-scale API agent scenarios.

Muse Wonder's ecosystem is just getting started. It offers three request formats on the Meta API, Muse Code, Vercel AI Gateway, and Cloudflare Workers AI. Meta's docs explain how to run Claude Code and Codex by pointing at api.meta.ai — a move aimed squarely at Anthropic's and OpenAI's framework users. But Meta has no Managed Agents equivalent yet, and its zero-data-retention policy disables server-side state, background requests, file uploads, and web search.

According to release-week analyses by Bilibili creators and YouTube channels, Muse Code had a bug that exhausted quotas, fixed the same day. Expect more rough edges over the coming month.

Managed Agents and multi-cloud deployment: Claude's Managed Agents let developers host long-running, stateful agents on Anthropic's side, saving them from building their own orchestration and state storage. Support for Bedrock, Google Cloud, and Microsoft Foundry means enterprises can call the model within their existing cloud contracts, compliance boundaries, and billing systems — no new vendor needed. By contrast, a model available only on the first-party API has less flexibility on procurement, data residency, and failover.

The side effects of zero-data-retention policies: To meet compliance requirements, some vendors offer a mode that stores neither requests nor responses — but this usually requires stateless requests. The server can't retain sessions, can't run background tasks asynchronously, and can't host file uploads or perform web search on your behalf, because all of those require temporary persistence. When choosing this mode, confirm that the tools you need are still available.

Security and Data Terms

Both vendors recommend human confirmation before agents take actions with significant consequences. Meta trained Wonder to request confirmation before important actions and to avoid unauthorized sharing of private information; the full safety report is still pending, and high-risk chemical and biological capabilities can't currently be ruled out. Anthropic publishes its system card at launch, and its prompt-injection defense scored 85%.

On data, Meta API's Standard mode doesn't train on your traffic; but the Muse Code subscription defaults to Contributor mode, which does. Anthropic doesn't train on API traffic by default. That difference is a key decision point for compliance-sensitive teams.

Task Routing: What to Give to Which Model

Task type	Choose	Why
Long-horizon coding agents, multi-step, predictable 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 search 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 advice	Opus 5.5	HealthBench 65.6 vs 60.5
Video or audio input	Wonder	Opus 5.5 doesn't support it
Anything running on Bedrock / Google Cloud / Foundry	Opus 5.5	Wonder is limited to first-party, Vercel, and Cloudflare
Strongest model for interactive coding	Opus 5.5 API, or Wonder Full on Muse Code	Full is subscription-only

The honest conclusion: Opus 5.5 is the smarter general-purpose model and the safer default. Wonder deserves serious evaluation only when your workload matches Meta's benchmarks — long-horizon tool-using agent runs, plus a need for video or audio input.

Next Step: Let Your Own Tasks Be the Judge

Since both models accept OpenAI-style and Anthropic-style requests, a single API test project can hold the same suite against both. Set up two environments: Meta's base_url is https://api.meta.ai/v1, and Anthropic uses its base URL, with each key managed as a secret variable. Create a Responses request for Wonder and a Messages request for Opus, using the same system prompt, tools, and tasks. Validate the structured output you need, and add a post-processor that logs usage and computes cost in dollars.

Run both models at the effort level you'd actually ship with, not the maximum. Ten real tasks will tell you more than any vendor's table.

One-line Summary

Muse Wonder and Claude Opus 5.5 go head-to-head at the same price, with different strengths and bills — route by task.

Paragraph Summary

Muse Wonder and Claude Opus 5.5 have identical pricing — $4 per million input, $20 output, $0.20 cache reads, and a 1M-token context window — so the contest is pure capability and fit. The independent Intelligence Index puts Opus at 58 versus Wonder's 52, with the widest gap in knowledge (46 vs 25). But in Meta's own benchmarks, Wonder beats Opus on long-horizon coding, software engineering, and legal tasks, and its actual per-task cost is lower ($4.52 vs $5.98), a difference driven by cache-heavy versus output-heavy cost structures. Wonder uniquely supports video and audio input, while Opus is more mature on tool ecosystem, multi-cloud deployment, and data terms. The article closes with a task routing table: try Wonder for long-horizon coding and legal tasks; pick Opus 5.5 for factual Q&A, terminal ops, health advice, and cloud-platform scenarios.