# Meta Opens a Paid Model API With Muse Spark 1.1, Its Turn Toward Metered Access

The Superintelligence Labs model ships a one-million-token context window and agentic tool use, priced at $1.25 per million input tokens and $4.25 per million output tokens.

- Published: 2026-07-14T05:33:22.579Z
- Canonical: https://polylog.news/ai/2026-07-14/meta-opens-a-paid-model-api-with-muse-spark-1-1-its-turn-tow
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [Meta AI](https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/), [MarkTechPost](https://www.marktechpost.com/2026/07/09/meta-superintelligence-labs-releases-muse-spark-1-1/), [DataCamp](https://www.datacamp.com/blog/muse-spark-1-1)

Meta released [Muse Spark 1.1 on July 9](https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/). For the first time, it made a first-party model available only through a paid, metered endpoint called the Meta Model application programming interface (API), now in public preview for developers in the United States. The company describes Muse Spark 1.1 as a multimodal reasoning model built for agentic tasks, with a one-million-token context window and improvements in tool use, computer use, coding, and multimodal understanding. It is the [second model from Meta Superintelligence Labs](https://www.marktechpost.com/2026/07/09/meta-superintelligence-labs-releases-muse-spark-1-1/) and an upgrade of the original Muse Spark that debuted in April.

Pricing is what makes this release significant. Meta lists [$1.25 per million input tokens and $4.25 per million output tokens](https://www.datacamp.com/blog/muse-spark-1-1), with $20 in free credits for new sign-ups before pay-as-you-go rates apply. That places the model in direct price competition with the metered APIs from OpenAI, Anthropic, and Google, rather than in the download-and-self-host approach Meta established with Llama.

The capability claims come from a vendor launch, and independent reproductions of the tool-use, coding, and long-context benchmarks are not yet available. What is verified is the commercial structure. There are no downloadable weights, there is a published rate card, and the preview is limited to one region.

## What this means

The mechanism here is monetization, not capability. Meta built its standing by distributing Llama weights for download. A metered model with no downloadable weights shifts revenue toward inference billing and away from the open-weight ecosystem it helped create. Closed-API incumbents gain a well-capitalized price competitor. Teams that standardized on downloadable Llama models would lose their largest Western supplier of that tier if Meta routes its best models to the API only.

## What to watch

- Whether Meta continues to ship downloadable weights for its strongest models alongside the API, which would signal a dual strategy rather than a full retreat from open weights.
- Independent benchmark reproductions of Muse Spark 1.1 against GPT, Claude, and Gemini, because the agentic and coding claims are so far vendor-stated.