By the toolist.ai editors. Researched from public sources, not hands-on tested by toolist.ai. Updated November 3, 2026; prices checked November 3, 2026.

Muse Wonder vs Claude Opus 5.5

Verdict

At the same list prices ($4 in, $20 out and $0.20 cached per million; Anthropic also charges $5 per million for 5-minute cache writes, which Meta does not list), Claude Opus 5.5 is the better buy on published results. It scores higher than Muse Wonder on all four Artificial Analysis benchmarks both have, from the Intelligence Index (59 against 44) to HLE-Diamond (68% against 39%), and Anthropic's Batch API halves its price, while Meta lists no batch discount. Muse Wonder, still in preview on Meta's API, makes sense for video or audio input or inside Meta's Muse Code.

Verdict based on published prices, limits and benchmarks, not on our own tests.

Choose Muse Wonder if

  • You send video or audio input; Claude Opus 5.5 accepts neither
  • You code in Muse Code, where every plan from $5 a month includes Muse Wonder
  • You want faster output: 170.8 tokens a second at max effort, against 77.9 for Opus 5.5 at medium

Choose Claude Opus 5.5 if

  • You want the higher published scores (60% against 52% on Terminal-Bench 4.0; 59% against 26% on Terminal-Bench-Science)
  • You run batch jobs, since the Batch API costs half
  • You need a published output limit (128,000 tokens; Meta does not publish one for Muse Wonder)

Price

Input costs the same on both. Output costs the same on both.

Per million tokensMuse WonderClaude Opus 5.5
Input$4 source$4 source
Cached input$0.20 source$0.20 source
Output$20 source$20 source

Muse Wonder: Cached-input rates apply when part of a prompt matches a cached prefix. There is no long-context premium. Web search grounding costs $2.50 per 1,000 searches. Meta lists no batch discount, and the discounted Contributor mode is not offered for Muse Wonder on the API.

Claude Opus 5.5: Cache writes cost $5 per million tokens (5-minute cache) or $8 (1-hour cache). The Batch API costs half. There is no long-context surcharge. Fast mode costs $8 in and $40 out.

Worked example: what a month costs

We priced three monthly workloads at the standard rates. The provider does not publish these figures.

WorkloadMuse WonderClaude Opus 5.5
Support chatbot
100,000 replies, each 3,000 input and 400 output tokens, no caching (300M in, 40M out)		 $2,000$2,000
Document Q&A
5,000 questions over the same 60,000-token document set (90% cache hits), 800 output tokens each (300M in, 4M out)		 $254$254
Coding agent
200 tasks, each 1.5 million input tokens (90% cache hits) and 50,000 output tokens (300M in, 10M out)		 $374$374

Price your own workload

ModelCost a month
Muse Wonder $300
Claude Opus 5.5 $300

Standard API prices, no batch or priority discounts. Cached input is billed at the cache-read price; cache-write charges are not included. Reasoning models bill their thinking as output tokens.

Limits and release dates

Muse WonderClaude Opus 5.5
ReleasedOctober 12, 2026 sourceSeptember 22, 2026 source
Context window1,048,576 tokens source1,000,000 tokens source
Maximum outputnot published128,000 tokens source
Knowledge cutoffnot publishedJune 2026 source
Input and outputText, images, video, audio and PDF in; text out sourceText and images in; text out source
API model namemuse-wonder-1.0claude-opus-5-5

Benchmarks

Scores as published by the provider or measured by Artificial Analysis; higher is better. A dash means no score was published.

BenchmarkMuse WonderClaude Opus 5.5
Artificial Analysis Intelligence Index
max effort		 44.0 source59.0 source
Terminal-Bench 4.0
max effort		 52.0% source60.0% source
HLE-Diamond
max effort		 39.0% source68.0% source
Terminal-Bench-Science 0.1
max effort		 26.0% source59.0% source
Intelligence Index at everyday effort –48.0 (medium effort, the API default) source
Output speed 170.8 tokens/s (max effort, Meta API; Artificial Analysis lists no default-effort variant) source77.9 tokens/s (medium effort, Artificial Analysis) source

Benchmark and speed data from Artificial Analysis where linked.

Read more

Sources