Muse Spark 1.3 Contributor
Neural Network
Muse Spark 1.3 Contributor is an economical tier of Meta's multimodal reasoning model for code and agentic scenarios.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 718 tokens
- Reasoning effort
- minimal, low, medium, high, extra high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Muse Spark 1.3 Contributor?
Muse Spark 1.3 Contributor is the Contributor tier in Meta's multimodal reasoning model line: the same reasoning approach, but in an economical version designed for experiments, training, and early stages of agentic, multi-agent, and coding scenarios. The model works with more than just text and maintains task logic throughout the conversation, making it suitable for prototypes that are later migrated to heavier configurations. In BotHub, it is accessible without a VPN or foreign card: top up your balance with a Russian card, pay per token used, with no subscription. Access over 250 models in one window—switch and compare answers to the same prompt, and for production, connect via a unified OpenAI-compatible API; traffic is encrypted with AES-GCM, and for companies, we offer contracts, invoices, EDI, and an admin panel with limits. Simple scenarios: test a hypothesis in code, draft a multi-agent scheme, understand an unfamiliar repository, practice prompts before moving to a larger model, or quickly check an architectural idea.Frequently asked questions about Muse Spark 1.3 Contributor
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
muse-spark-1.3-contributor accepts text, documents, and images: it analyzes files and screenshots, answers based on content, writes and edits code, and solves reasoning tasks. A context window of up to 1,048,576 tokens allows you to keep an entire project or voluminous documentation in a single request without splitting it up.
In a single response, the model can output up to 943,718 tokens—enough for a very long text: full documentation, a large report, or a voluminous code module in its entirety, without splitting it into parts. The actual length depends on your request and generation settings.
Yes, reasoning before answering is supported: the model first outlines the solution path and then formulates the result. This is noticeable in STEM tasks, logic, code analysis, and long documents where it is important not to guess the answer but to derive it sequentially.
Yes, the model supports function calling and structured JSON output. You can connect it to external tools, databases, and services, receive answers in a specified schema, and integrate them into your pipelines. In BotHub, this is done via a unified OpenAI-compatible API.
Yes, images and documents are supported: send a screenshot, diagram, or file and ask it to analyze the content. Audio and video input is not noted in our data, so for such formats, it is more reliable to use a specialized model—it is available in the same window in BotHub, without rewriting the integration.
The language of prompts and responses is not specified in our data, so we will not make specific promises. It is easier to test it with your own task: ask a question in the way that is convenient for you and compare the result with another model in the same window. Access from Russia is available without a VPN or foreign card.