Meta Model API
Meta's developer platform with API access to Muse Spark foundation models (Meta Superintelligence Labs). Drop-in compatible with OpenAI and Anthropic SDKs.
Regional availability·1 region
- United States (public preview)
Description
Meta Model API is Meta's developer platform providing access to Muse Spark family foundation models built by Meta Superintelligence Labs. Launched July 9, 2026 in public preview for US developers alongside Muse Spark 1.1 — the first frontier model from the unit led by Alexandr Wang.
Platform purpose
Meta Model API lets developers build with Muse Spark using the tools they already know. Its distinguishing feature is drop-in compatibility with the OpenAI SDK and Anthropic SDK — existing applications can be ported to Muse Spark without changing integration code. It also supports agent CLIs such as OpenCode and Claude Code.
Muse Spark 1.1 capabilities via the API
Muse Spark 1.1 is a multimodal reasoning system designed for agentic tasks. Key capabilities exposed by the Meta Model API: parallel tool calling, computer use (agent-driven computer control), multi-agent orchestration, structured output, streaming, function calling, and context compaction up to 1 million tokens. The model is particularly strong on coding tasks.
Pricing and access
Billing model: usage-based pay-as-you-go. Muse Spark 1.1: USD 1.25 per 1M input tokens, USD 4.25 per 1M output tokens. At launch Meta offers free credits, a cookbook (github.com/meta-models/meta-model-cookbook), and developer resources. In public preview access is restricted to US developers; transition to full billing is announced after the preview phase.
Strategic context
Meta Model API replaces Meta's earlier era of open-weight models (Llama 1-4, discontinued April 2026) with a new chapter: frontier models in preview via API. The launch positions Meta as a direct competitor to OpenAI (GPT) and Anthropic (Claude) in the developer API segment — with a clear positioning of 'drop-in compatible, cheaper, open tooling ecosystem'.
MLOps LifecycleMLOps LifecycleFull model lifecycle: registry, feature store, prompt management, monitoring and human-in-the-loop.
Model Registry
Feature Store
Prompt Management
Monitoring
Human-in-the-Loop
Data & KnowledgeData & Knowledge ManagementData connectors, vector database integration, native vector search and data management (PII, provenance, synthetic data).
ApplicationsAI ApplicationsDomains and use cases this platform is best suited for — from RAG and fine-tuning to scientific research.
SecurityEnterprise SecurityCertifications, access controls and data-protection features essential for corporate deployments and cloud privacy compliance.
Developer EcosystemDeveloper EcosystemDeveloper resources: available SDKs, supported programming languages, and infrastructure features and model-deployment methods.
Pricing & Business ModelPricing & Business ModelBilling models (usage-based, provisioned throughput), resource limits and SLA parameters (uptime, support tiers).
Pricing models
Resource quotas
SLA & Support