Meta Muse Spark 1.1: Pricing, API and Specs for Developers
Meta's Muse Spark 1.1 API charges $1.25/$4.25 per million tokens and drops into the OpenAI SDK. Here is what changes for developers and how it compares.
Meta opened a paid, hosted API for the first time on July 9, 2026, pricing its new Muse Spark 1.1 model at $1.25 per million input tokens and $4.25 per million output tokens — a fraction of what Anthropic and OpenAI charge for comparable flagship-tier models. For developers, the practical headline is that Meta is no longer just an open-weight lab: the Meta Model API is a metered, OpenAI-compatible endpoint you can point existing agent code at, and its price makes high-volume coding and agentic workloads meaningfully cheaper to run.
What Meta actually shipped
Muse Spark 1.1 is an upgrade to the original Muse Spark model Meta released in April 2026, built by Meta Superintelligence Labs. Per Meta's announcement, it is a multimodal reasoning model with a 1,048,576-token context window and a 131,072-token max output, aimed at agentic tasks that require planning and orchestration across tools, computer use, and multi-agent workflows. Meta says it gained ground on tool use, computer use, coding, and multimodal understanding (images, video, PDFs) compared to the original Muse Spark — those comparisons are vendor-reported and not independently benchmarked.
Two things separate this from prior Meta model releases:
- It's closed-weight. Unlike Llama, Muse Spark 1.1 is not available as downloadable weights — access is only through the hosted API.
- It's a public preview, currently limited to developers in the United States, available via the new Meta Model API rather than through a partner cloud.
Consumers get the same model for free in "Thinking" mode inside the Meta AI app and on meta.ai; the API is the new, paid surface aimed specifically at developers.
Pricing: how it stacks up
According to Meta's Model API documentation and pricing coverage from outlets including The Decoder, Muse Spark 1.1's rate card looks like this:
| Item | Price |
|---|---|
| Input tokens | $1.25 / 1M |
| Output tokens (incl. reasoning) | $4.25 / 1M |
| Cached input | $0.15 / 1M |
| Web search grounding | $2.50 / 1,000 queries |
| New account credit | $20 one-time |
That output price is roughly 6-7x cheaper than the $25-$30 per million output
tokens that Anthropic's Claude Opus 4.8 and OpenAI's GPT-5.6 Sol charge at their
flagship tier — see our GPT-5.6 developer guide
for the OpenAI side of that comparison. Muse Spark 1.1 is a reasoning model, so
"thinking" tokens spent before the model answers are billed at the output rate.
The reasoningEffort parameter (Meta's docs list high as an available setting,
alongside lower tiers) is the main lever for keeping that cost in check: matching
effort to task difficulty matters more here than with non-reasoning models, because
every reasoning token is a full-price output token.
Dropping it into existing agent code
Meta built the Model API to be a near drop-in replacement for the OpenAI and
Anthropic SDKs rather than a bespoke client. Per Meta's getting-started docs, the
API exposes an OpenAI-compatible Responses and Chat Completions interface and an
Anthropic-compatible Messages API, both reachable at https://api.meta.ai.
from openai import OpenAI
client = OpenAI(
base_url="https://api.meta.ai/v1",
api_key=os.environ["MODEL_API_KEY"],
)
response = client.responses.create(
model="muse-spark-1.1",
input="Summarize the open pull requests in this repo.",
reasoning={"effort": "high"},
)Because the interface matches the OpenAI SDK shape, frameworks that already talk to OpenAI or Anthropic-compatible endpoints — LangChain, LlamaIndex, the Vercel AI SDK, OpenCode, and most agent CLIs — should work by swapping the base URL, API key, and model identifier. That lowers the switching cost for teams that want to A/B test Muse Spark 1.1 against an existing coding-agent setup without rewriting integration code, similar to how teams evaluate other new entrants covered in our AI coding agents practical guide.
What changes for developers
- Cost modeling gets a new cheap option. At $4.25/M output, the same token volume that costs $250 in Opus 4.8 ($25/M) or GPT-5.6 Sol ($30/M) output tokens would cost roughly $35-$43 in Muse Spark 1.1 output tokens — before accounting for any difference in output quality or token efficiency per task, which Meta's announcement does not quantify against rivals.
- Reasoning effort is now a cost dial you must tune, not just a quality one. Defaulting every call to high reasoning effort will erode the price advantage fast on agentic loops that run many turns.
- US-only access for now. Teams outside the US public-preview region will need to wait for wider rollout before they can rely on this in production.
- No open weights. Teams that valued Llama's self-hostable weights should not assume the same is coming for Muse Spark — this release is API-only.
A pragmatic first step
Given the public preview is US-only and unaudited on independent benchmarks, the
sensible move is a bounded pilot: pick one narrow, high-volume workload (bulk
summarization, classification, or a single coding-agent step), route it through the
OpenAI-compatible endpoint at a fixed reasoningEffort, and compare real cost and
output quality against whatever model handles that step today before shifting
anything mission-critical.
Further reading
Frequently asked questions
How much does the Meta Model API cost?
Muse Spark 1.1 is priced at $1.25 per million input tokens and $4.25 per million output tokens, with cached input at $0.15 per million and web search grounding at $2.50 per 1,000 queries, according to coverage of Meta's July 9, 2026 launch. New accounts get $20 in one-time free credits.
Is Muse Spark 1.1 open weight like Llama?
No. Muse Spark 1.1 ships only through the hosted Meta Model API, not as downloadable weights, marking a shift from Meta's earlier open-weight Llama releases toward a metered API business model.
Can I use Muse Spark 1.1 with my existing OpenAI or Anthropic code?
Yes. Meta's developer documentation describes the Meta Model API as compatible with the OpenAI SDK (Responses and Chat Completions APIs) and the Anthropic Messages API, so most existing agent frameworks work by changing the base URL and model name.
What is the context window and does reasoning affect cost?
Muse Spark 1.1 has a 1,048,576-token context window and a 131,072-token max output. It is a reasoning model, so internal 'thinking' tokens are billed at the output rate — the reasoningEffort parameter is the main lever for controlling that cost.