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Meta: Muse Wonder API Pricing

Meta: Muse Wonder costs $4.00 per 1M input tokens and $20.00 per 1M output tokens (cache reads: $0.200). Context window: 1.0M. Prices verified 2026-10-21 · source

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Alternatives to consider

🏠 Same provider
Meta: Muse Spark 1.3 Contributor
$0.100 in / $0.200 out

Cheapest meta model — 98% less per month

Price history
2026-10-13 → 2026-10-21 · 59 daily snapshots
inputoutput

Rebuilt daily from our snapshot archive — the chart appears once a model has 5+ snapshots of history.

Pricing behavior
Stable — no price moves across our 59 daily snapshots (2026-08-23 → 2026-10-21).

From our own daily snapshot archive — independent of provider announcements.

Input / 1M tokens
$4.00
Output / 1M tokens
$20.00
Context window
1.0M
Max output
944K

Token economics

Token typePricevs base input
Standard input$4.00—
Cache read$0.200−95%
Output$20.00—
Web search tool$2.5/1k searches—

Prompt caching stores repeated context server-side: reads cost far less, but writes carry a one-time premium.

Reasoning behavior
Mode: Always on (mandatory)
Effort levels:maxxhighhighmediumlowminimal

Higher effort = deeper thinking = more reasoning tokens billed as output.

What would Meta: Muse Wonder cost you per month?

WorkloadRequests/moAvg tokens (in/out)Est. monthly cost
Light chatbot10,000800 / 400$112.00
Coding agent5,0008,000 / 2,000$360.00
RAG pipeline30,0004,000 / 500$780.00
Doc summarizer2,00020,000 / 1,000$200.00

Estimates use list prices without prompt caching. Caching can cut input cost by up to 90% on repeated context.

About Meta: Muse Wonder

Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with...

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Frequently asked questions

How much does Meta: Muse Wonder cost?

Meta: Muse Wonder costs $4.00 per 1M input tokens and $20.00 per 1M output tokens; cached input tokens cost $0.200 per 1M. Verified 2026-10-21 — provider source.

What is the context window?

1.0M tokens, with up to 944K output tokens per response. That's roughly 1311 pages of text in a single request.

Does it support prompt caching?

Yes — cached input is $0.200/1M, about 95% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.

What's a cheaper alternative?

ScaleDown: Summarize saves about 99% on a typical workload with comparable quality.

Official resources