Meta Muse Spark 1.3 is the latest Muse Spark model served through Meta's Model API, tuned for agentic workflows — multi-step tool use, browser operation, and long-horizon tasks — with improved coding over the previous version and a 1,048,576-token context window, suited to long documents and long conversations. It is a reasoning model: use the reasoning-effort dropdown (minimal / low / medium / high / xhigh / max, default medium; this model has no 'none' setting) and the thinking-display option to tune how deeply it thinks. Turn on web search to let the model look things up in real time and cite its sources, or upload images and documents for analysis. Use it like any other chat module: type your question or instruction and send; replies stream back. Calls go through Meta's official Model API (Standard tier; conversations are not used for model training). Best for coding, data wrangling, research, and multi-step reasoning tasks. If you need to search the web for information, please turn on the "Web Search" switch. Prompt example 1: (Turn on web search) Summarize electric scooter market trends over the past year in five key points, and include reference sources. Prompt example 2: Write a program that reads the date and amount columns from a CSV file, totals them by month, and writes a new CSV, with test cases and run instructions. Prompt example 3: I need to organize a 200-person tech conference in three months. Break it into a multi-step plan with a task list, roles, timeline, and risks. Prompt example 4: (Upload a 300-page product technical manual PDF) Summarize it chapter by chapter and table every error code it mentions with its meaning and fix. Prompt example 5: Write a Python scraper for https://shop.example.com/products that collects product names and listing dates, handles pagination and timeout retries, and saves JSON. Prompt example 6: (Upload a customer list XLSX) Find duplicate or inconsistent phone numbers and emails, propose cleanup rules, and list the records that need manual review. Prompt example 7: (Turn on web search) Compare three open-source project management tools on features, licensing and community activity in a table, with reference sources. Prompt example 8: Last week's daily store visitors: Mon 132, Tue 118, Wed 95, Thu 240, Fri 156, Sat 310, Sun 288. Work out the daily average and weekend share, and suggest staffing for each day next week. Prompt example 9: (Upload a JPG photo of a printed class schedule) Read each class's weekday, time and room, convert them into .ics file content for importing into a calendar, and explain how to import it. Prompt example 10: Write a Playwright test script that logs in to https://app.example.com, adds a to-do item and verifies it appears in the list, with run instructions. Maximum context window (input and output combined): about 1,048,576 tokens. Maximum total upload size per request: 512MB (all files and message content combined). Accepted document formats: pdf, docx, xlsx, pptx, txt, csv (file content is provided to the model as extracted text). Accepted image formats: gif, jpeg, jpg, png, webp. Maximum number of images: 50. Maximum size per image: 47.7MB. Image dimensions: at least 200 pixels per side. Maximum image resolution: 36,000,000 pixels in total. Output content formats: Text Pricing: Medium Continuous conversation: Supported AI can make mistakes. Please verify important information.