Muse Spark 1.3 Contributor
9134°
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Meta | 1,048,576 token context | $0.10/M input, $0.20/M output. 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.
Guides for this model
The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans
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Captain's notes
Notes from 2026-10-13
Muse Spark 1.3 Contributor is Meta's cost-efficient tier of a multimodal reasoning model, handling vision and video inputs alongside text across a one-million-token context window. It is built for experimentation and early-stage agentic work: tracking state across long tasks, resolving conflicting inputs, and knowing when to ask for clarification. At $0.10/$0.20 per million tokens, it is priced for iteration rather than production.
Known for
- One-million-token context window for long, multi-step tasks
- Multimodal: accepts text, vision, and video inputs
- Designed to handle conflicting information and request clarification when needed
- Built with agentic and multi-agent workflows in mind
- Very low token price suited to high-volume experimentation
Watch out for
- Contributor tier means your prompts and outputs may be used to train Meta's products — not suitable for confidential work.
- Weights are not open; you cannot run this locally or self-host it.
- Positioned as an experimentation tier, so production reliability is not the stated goal.
Prompts to try
Multi-step agentic plan
You are coordinating a three-stage data pipeline. Stage one scrapes a public API, stage two cleans and deduplicates records, stage three writes results to a database. List the tools and function calls needed at each stage, flag any step where you would need to ask the user for clarification before proceeding, and explain why.
Conflicting inputs resolution
Here are three internal memos about the same product launch date: Memo A says 14 March, Memo B says 21 March, Memo C references 'the week after the Berlin trade show' without a date. Identify the conflict, state what you can resolve from the information given, and list exactly what you would need confirmed before committing to a final date.
Video scene reasoning
I am attaching a short video clip of a manufacturing line. Identify any step in the sequence where the order of operations looks inconsistent with a standard assembly workflow, describe what you see at that moment, and suggest what additional footage or documentation would help you confirm whether it is an actual error.
Compare with
- Muse Glimmer 30BOpen-weights model131,072 token context · $0.30/M input, $1.20/M output
- Muse Spark 1.3API-only model1,048,576 token context · $1.25/M input, $4.25/M output
- Muse WonderAPI-only model1,048,576 token context · $4.00/M input, $20.00/M output
- Muse Spark 1.2API-only model1,048,576 token context · $1.25/M input, $4.25/M output
More text models from meta
Questions people ask
- Can I run Muse Spark 1.3 Contributor locally?
- No. The weights are not open and there is no Hugging Face repository, so local or self-hosted deployment is not possible.
- What does the Contributor tier mean for my data?
- Meta states that prompts and outputs submitted through this tier may be used to improve its products, so you should avoid sending confidential or sensitive information.
- How does this differ from higher tiers of Muse Spark 1.3?
- Meta has not published a detailed comparison, but the Contributor label and the low price point ($0.10/$0.20 per million tokens) indicate this is the entry-level, experimentation-focused tier rather than a production-grade offering.
Captain's verdictA genuinely cheap way to probe long-context agentic and multimodal reasoning — just keep your sensitive data well away from it.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Also ranked: #14 Hottest Text / LLM model · #14 Hottest Text / LLM text model
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Muse Spark 1.3 Contributor Popularity Trend
Heat score since 2 Sep 2026
Heat score
9,134°
This week
▲
47.3%
9,083° then, 9,134° now
This month
▲
969%
8,823° then, 9,134° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,784°
OpenRouter tokens
8,847,442,276,267 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 884,744 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+350°
Adoption rate
About 46,090 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 9,134°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.884,744 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,784°8,784°running total
- 3Add the adoption rate.About 46,090 downloads a day right now, measured between two readings, at a quarter of the weight.+350°9,134°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,134°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Muse Spark 1.3 Contributor is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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