--- title: "Muse Spark 1.3 Contributor AI model: facts and benchmarks | Kendr" canonical: "https://kendr.org/models/meta-muse-spark-1-3-contributor" date_modified: "2026-09-06" profile_kind: "reference" --- # Muse Spark 1.3 Contributor model profile Muse Spark 1.3 Contributor is a Meta model profile with a 1,048,576-token context window. The dated catalog snapshot records text, image, video, file, audio modalities and interfaces for audio input, file input, prompt caching, reasoning controls, structured 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. Muse Spark 1.3 Contributor is a reference profile: Meta publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Muse Spark 1.3 Contributor is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt. Muse Spark 1.3 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. The dated reference snapshot lists $0.1 input and $0.2 output per million tokens. Cached input is $0.002 per million tokens. No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated. Muse Spark 1.3 Contributor ranked #28 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. Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated. ## 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-1.3-contributor ## When to pick Muse Spark 1.3 Contributor Muse Spark 1.3 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 1.3 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 1.3 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. - **Reference model ID:** meta/muse-spark-1.3-contributor - **Canonical version:** meta/muse-spark-1.3-contributor-20260902 - **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:** 3.50 s - **Best p50 throughput:** 75 tok/s - **Availability with routing:** 99.91% over the sampled window - **Availability without routing:** 99.90% over the sampled window - **Performance date:** 2026-09-06 | Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Meta | unknown | $0.1 / 1M | $0.2 / 1M | $0.002 / 1M | 1,048,576 tokens | 3.50 s | 75 tok/s | 100.00% | ## 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 1.3 Contributor ranked #28 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:** #28 - **Global market share:** Not inferred - **Observation date:** 2026-09-06 ## Frequently asked questions ### When should I use Muse Spark 1.3 Contributor? Muse Spark 1.3 Contributor is a reference profile: Meta publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. 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. ### What is Muse Spark 1.3 Contributor? Muse Spark 1.3 Contributor is a Meta model profile with a 1,048,576-token context window. The dated catalog snapshot records text, image, video, file, audio modalities and interfaces for audio input, file input, prompt caching, reasoning controls, structured output. ### What context window does Muse Spark 1.3 Contributor have? The dated profile lists 1,048,576 tokens of context and up to 943,718 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits. ### What benchmark evidence is available for Muse Spark 1.3 Contributor? 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. ### Is Muse Spark 1.3 Contributor available through Kendr? This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases. ### Does the popularity rank represent Muse Spark 1.3 Contributor's global market share? Muse Spark 1.3 Contributor ranked #28 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. No global market-share percentage is inferred when the source does not publish one. ## Sources and evidence dates - [Muse Spark 1.3 Contributor third-party catalog record](https://openrouter.ai/meta/muse-spark-1.3-contributor) — catalog, checked 2026-09-06 - [Third-party model catalog methodology](https://openrouter.ai/docs/guides/overview/models) — methodology, checked 2026-09-06 - [Meta official model documentation](https://github.com/meta-llama/llama-models) — primary, checked 2026-09-06 Live Kendr operational availability is published separately at https://api.kendr.org/api/public/models.