meta
Meta: Muse Spark 1.3
meta/muse-spark-1.3
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
According to its OpenRouter catalog entry, meta/muse-spark-1.3 is a multimodal reasoning model from Meta aimed at long-running agentic, multi-agent, and coding workflows. It accepts text, image, video, and file inputs and returns text, with a reported context window of about one million tokens. The catalog marks reasoning as mandatory with adjustable effort levels, and lists tool calling and structured output support. No independent model card was available in this source pack, so these capabilities are catalog-reported rather than independently verified.[1]
Strengths
- The catalog reports a context window of roughly one million tokens, aligned with its stated goal of tracking information across extended tasks.[1]
- Per the catalog, it ingests text, image, video, and file inputs and produces text output, broadening the kinds of material it can work on.[1]
- The catalog lists parameters for tool calling, tool choice, structured outputs, and response format, which supports agentic integrations, plus adjustable reasoning effort levels.[1]
Limitations
- Only OpenRouter catalog data was supplied and no independent model card was in this pack, so reported capabilities and the catalog's published arena and index results need validation on your own workloads.[1]
- The catalog does not report a knowledge cutoff, license terms, or deployment requirements; these are unknown rather than absent features.[1]
- Reasoning is marked mandatory in the catalog, so it cannot be fully disabled, which may matter for latency- or cost-sensitive uses.[1]
Where to use it
- Agentic, multi-agent, and coding workflows, which the catalog names as the design target; treat as an editorial suggestion to evaluate rather than a proven fit.[1]
- Long-document or repository-scale tasks that could benefit from the reported large context window, subject to testing on your data.[1]
- Multimodal analysis pipelines that convert image, video, or file inputs into text outputs.[1]
Sources
- [1] OpenRouter catalog · Meta: Muse Spark 1.3Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through...
Specifications
- Model ID
- meta/muse-spark-1.3
- Canonical ID
- meta/muse-spark-1.3-20260902
- Creator
- meta
- Added to OpenRouter
- 02.09.2026
- Context length
- 1,048,576
- Modality
- text+image+file+video->text
- Input modalities
- text, image, video, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $1.25 / out $4.25
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | AA — next 20% of the cohort | reasoning: mandatory · default medium · efforts: max, xhigh, high, medium, low, minimal · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | SS — top 10% of the cohort | — |
| Agents | SS — top 10% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default medium · efforts: max, xhigh, high, medium, low, minimal · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | input: video |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 36.6 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Coding index | 75.8 | SS — top 10% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 55.5 | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 65% · rank 2 | 1,292Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 56.3% · rank 11 | 1,145Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 55.5% · rank 7 | 1,207Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 60.7% · rank 5 | 1,292Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 49.8% · rank 15 | 1,215Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 52.4% · rank 18 | 1,190Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 51.6% · rank 11 | 1,206Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 48.3% · rank 16 | 1,160Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · webappswin rate 55.2% · rank 6 | 1,283Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 63.4% · rank 9 | 1,383Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 63% · rank 3 | 1,329Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 58% · rank 5 | 1,354Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 58.4% · rank 6 | 1,343Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 56.9% · rank 10 | 1,346Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 55.1% · rank 4 | 1,316Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 59.3% · rank 7 | 1,354Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 59.3% · rank 2 | 1,361Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 45.8% · rank 8 | 1,221Elo | AA — next 20% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| 1,048,576 | $1.25 | $4.25 | — | — | 100% | |
| Meta | 1,048,576 | $1.25 | $4.25 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.