Muse Spark 1.2 Contributor model profile

Profile facts

Profile type

Model research profile

Provider or publisher

Meta

Context window

1,048,576 tokens

Snapshot date

2026-09-06

Knowledge cutoff

Not disclosed

Reference catalog ID

meta/muse-spark-1.2-contributor

When to pick Muse Spark 1.2 Contributor

Muse Spark 1.2 Contributor is a reference profile: Meta publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.

• Reach for it when cost is the binding constraint. At $0.1 in and $0.2 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot.
• Reach for it when a whole corpus has to fit in one prompt. The 1.05M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked.
• Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
• Reach for it when the input is speech. The snapshot records audio input, so recordings can go to this route rather than being transcribed first.
• Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
• Look elsewhere when the request is trivial and latency-sensitive. Reasoning is mandatory on this route and cannot be turned off, so short factual requests still pay reasoning tokens and reasoning latency.

Context, modalities, and identity

Muse Spark 1.2 Contributor accepts text, image, video, file, and audio and returns text. The snapshot records audio input, file input, prompt caching, reasoning controls, structured output, text generation, tool calling, video input, vision, and web search pricing as capabilities or interfaces.

Provider or publisher

Meta

Context window

1,048,576 tokens

Maximum output

943,718 tokens

Input modalities

text, image, video, file, and audio

Output modalities

text

Knowledge cutoff

Not disclosed

Model overview

Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost. It’s meaningfully cheaper than Muse Spark 1.2. Your prompts and outputs may be used to improve Meta’s products, making it ideal for experimentation, learning, and early-stage projects without worry about spend. It is a reasoning model from Meta, tailored for complex agentic tasks. It accepts text, images, video, audio, 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.

Reference model ID

meta/muse-spark-1.2-contributor

Canonical version

meta/muse-spark-1.2-contributor-20260805

Hugging Face ID

Not separately documented

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters

include_reasoning, max_tokens, reasoning, reasoning_effort, repetition_penalty, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p

• audio input
• file input
• prompt caching
• reasoning controls
• structured output
• text generation
• tool calling
• video input
• vision
• web search pricing

Dated pricing snapshot

The dated reference snapshot lists $0.1 input and $0.2 output per million tokens. Cached input is $0.002 per million tokens.

These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.

Price date

2026-09-06

Input

$0.1 per 1M tokens

Cached input

$0.002 per 1M tokens

Output

$0.2 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 1 provider endpoint. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints

1

Best p50 latency

2.80 s

Best p50 throughput

109 tok/s

Availability with routing

99.86% over the sampled window

Availability without routing

99.65% over the sampled window

Performance date

2026-09-06

Benchmark evidence and limitations

No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Popularity and market context

Muse Spark 1.2 Contributor ranked #48 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Third-party catalog rank

#48

Global market share

Not inferred

Observation date

2026-09-06

Frequently asked questions

Sources and evidence dates

1. Muse Spark 1.2 Contributor third-party catalog record (catalog, checked 2026-09-06)
2. Third-party model catalog methodology (methodology, checked 2026-09-06)
3. Meta official model documentation (primary, checked 2026-09-06)