Muse Spark 1.3 Contributor
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information across extended tasks, work through conflicting inputs, and request clarification or confirmation when needed. Prompts and outputs may be used to improve Meta’s products.
- Input / output price
- $0.1/$0.2per 1M tokens
- Context
- 1.0M
- Released
- 2026-09-02
Pricing
Related Models
Muse Spark 1.3
metaMuse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through conflicting inputs, and request clarification or confirmation when needed, with an emphasis on concise execution.
- Context
- 1.0M
- Max output
- 943.7K
- Released
- 2026-09-02
- Input
- $1.25/M
- Output
- $4.25/M
- Cache read
- $0.15/M
Muse Spark 1.2
metaMuse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks. It accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context window. The model is built to support multi-agent workflows, whether as either a main agent that plans and delegates or as a subagent executing in parallel. It works across multiple coding harnesses and supports structured output, parallel function calling, and configurable reasoning effort. In Meta’s testing, it performs well on multi-file refactors, extended debugging sessions, whole-repository generation, and tasks that stretch well past a single prompt.
- Context
- 1.0M
- Max output
- 943.7K
- Released
- 2026-08-05
- Input
- $1.25/M
- Output
- $4.25/M
- Cache read
- $0.15/M
Muse Spark 1.1
metaMuse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, and PDF documents and returns text, with a 1M-token context window. The model is designed to orchestrate multi-agent workflows, acting as either a main agent that plans and delegates or as a subagent, and generalizes zero-shot to new tools, MCP servers, and custom skills. It supports structured output, parallel function calling, built-in search with citations, and configurable reasoning effort. Meta reports strong performance on real-world coding across large codebases, computer-use workflows, and visual-to-code generation.
- Context
- 1.0M
- Max output
- 943.7K
- Released
- 2026-07-16
- Input
- $1.25/M
- Output
- $4.25/M
- Cache read
- $0.15/M