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Meta: Muse Spark 1.2 Contributor API Pricing

Meta: Muse Spark 1.2 Contributor costs $0.100 per 1M input tokens and $0.200 per 1M output tokens (cache reads: $0.0020). Context window: 1.0M. Prices verified 2026-10-21 · source

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Price history
2026-08-23 → 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
$0.100
Output / 1M tokens
$0.200
Context window
1.0M
Max output
944K

Token economics

Token typePricevs base input
Standard input$0.100—
Cache read$0.0020−98%
Output$0.200—
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:xhighhighmediumlowminimal

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

What would Meta: Muse Spark 1.2 Contributor cost you per month?

WorkloadRequests/moAvg tokens (in/out)Est. monthly cost
Light chatbot10,000800 / 400$1.60
Coding agent5,0008,000 / 2,000$6.00
RAG pipeline30,0004,000 / 500$15.00
Doc summarizer2,00020,000 / 1,000$4.40

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

About Meta: Muse Spark 1.2 Contributor

Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost. It’s meaningfully cheaper than Muse Spark...

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Popular head-to-heads — see all on the comparisons index.

Frequently asked questions

How much does Meta: Muse Spark 1.2 Contributor cost?

Meta: Muse Spark 1.2 Contributor costs $0.100 per 1M input tokens and $0.200 per 1M output tokens; cached input tokens cost $0.0020 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 $0.0020/1M, about 98% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.

What's a cheaper alternative?

ScaleDown: Summarize saves about 78% on a typical workload with comparable quality.

Official resources