Muse Spark 1.2
import { streamText } from 'ai'
const result = streamText({ model: 'meta/muse-spark-1.2', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out Muse Spark 1.2 by Meta. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Muse Spark 1.2
Muse Spark 1.2
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingMore models by Meta
| Model |
|---|
| 131K | 0.2s | 105tps | $0.35/M | $1.50/M | Read:$0.04/M Write:— | — | +1 |   | 08/10/2026 | ||||
| 1M | 4.2s | 55tps | $0.10/M | $0.20/M | Read:$0.002/M Write:— | — | +2 | | 08/05/2026 | ||||
| 1M | 7.0s | 258tps | $1.25/M | $4.25/M | Read:$0.15/M Write:— | — | +2 | | 07/09/2026 | ||||
| 131K | 0.2s | 61tps | $0.24/M | $0.97/M | — |   | 04/05/2025 | ||||||
| 131K | 0.2s | 161tps | $0.17/M | $0.66/M | — | | 04/05/2025 | ||||||
| 131K | 0.2s | 217tps | $0.02/M | $0.05/M | — |  | 07/23/2024 |
Copy link to headingAbout Muse Spark 1.2
Muse Spark 1.2 is Meta's reasoning model for complex agentic tasks, and the coding-focused successor to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents, returns text, and works within a context window of 1.0M tokens.
Multi-agent work is the design centre. Muse Spark 1.2 runs either as a main agent that plans and delegates, or as a subagent executing in parallel, and it was built around harness patterns like context compaction, whole-repository work, and long-running tasks. That shows up as durability across long sessions rather than as a single benchmark number.
Support covers structured output, parallel function calling, and configurable reasoning effort, and the model works across multiple coding harnesses rather than assuming one.
A second slug, muse-spark-1.2-contributor, serves these same weights at a much lower rate in exchange for Meta training on your inputs and outputs. Choose between them on data governance first and price second. See the comparison on that model's page.
You can integrate Muse Spark 1.2 through AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: The cheaper
muse-spark-1.2-contributorslug is not a different model. It is the same weights on a data-sharing arrangement, so if cost is pushing you toward it, read what that arrangement covers before switching. - Configuration: A context window of 1.0M tokens invites large prompts, and cost follows what you actually send. In an agent loop most input becomes cache reads, so measure your cached-versus-fresh ratio against the pricing panel on this page rather than sizing from the headline input rate.
- Configuration: Muse Spark 1.2 is built for multi-step agentic work. On single-turn questions or short completions, a smaller model in the family will usually be the better economic fit.
- Zero Data Retention: Zero Data Retention is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use Muse Spark 1.2
Best for
- Multi-Agent Workflows: Planning main agent or parallel subagent roles
- Whole-Repository Work: Long sessions with context compaction as a design target
- Multimodal Engineering Input: Text, images, video, audio, and PDFs
- Parallel Function Calling: Multiple tools within one reasoning pass
- Harness Portability: Not tied to a single coding harness
Consider alternatives when
- Cost-Driven Workloads: The contributor tier serves the same weights for far less, with data sharing
- Single-Turn Prompts: A smaller model in the family fits the economics better
- Strict Data Control: Confirm retention terms before choosing either tier
- Open Weights Requirement: Muse Glimmer 30B is the Apache 2.0 option
Copy link to headingConclusion
Muse Spark 1.2 is Meta's coding-focused agentic model, built for multi-agent workflows across a 1.0M tokens multimodal window. Point meta/muse-spark-1.2 at AI Gateway to route requests behind one API key, and treat the cheaper contributor slug as a data-governance decision rather than a pricing one.