meta
Meta: Muse Spark 1.1
meta/muse-spark-1.1
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
The OpenRouter catalog describes meta/muse-spark-1.1 as a Meta multimodal reasoning model intended for agentic tasks. It accepts text, images, video and files, including PDFs per the catalog description, and returns text. The catalog lists a context window of about one million tokens, mandatory reasoning with selectable effort levels, and support for tools plus structured outputs. Its benchmark entries cover coding, document and interface-generation tasks, but this pack contains no independent model card, so real-world fit should be validated.[1]
Strengths
Limitations
- Evidence is catalog-only; no independent model card was supplied, so architecture, license, safety behavior and knowledge cutoff remain unreported.[1]
- Reported benchmark coverage is limited to selected agentic, coding and design categories and may not predict performance on a target workload.[1]
- Only text output is listed despite multimodal input.[1]
Where to use it
- Potentially useful for multimodal question answering or summarization over long documents, images or video; validate accuracy on representative data.[1]
- May fit agentic workflows that require reasoning controls, tool calls and structured text responses.[1]
- Could be evaluated for code, data-visualization/interface or document-format generation tasks reflected in the catalog's benchmark categories.[1]
Sources
- [1] OpenRouter catalog · Meta: Muse Spark 1.1Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, and PDF documents and returns text, with a 1M-token context window....
Specifications
- Model ID
- meta/muse-spark-1.1
- Canonical ID
- meta/muse-spark-1.1-20260709
- Creator
- meta
- Added to OpenRouter
- 16.07.2026
- 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 | No measurement | reasoning: mandatory · default medium · efforts: xhigh, high, medium, low, minimal · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | AA — next 20% of the cohort | — |
| Agents | BB — next 30% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default medium · efforts: 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 | Coding index | 71.3 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 25.8 | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 48.5% · rank 12 | 1,132Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 48.8% · rank 20 | 1,174Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 35.9% · rank 19 | 1,009Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 37% · rank 21 | 1,078Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 44.6% · rank 21 | 1,175Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 39.8% · rank 30 | 1,086Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 51.8% · rank 12 | 1,091Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 46.2% · rank 26 | 1,172Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 47.1% · rank 23 | 1,144Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 44.4% · rank 23 | 1,118Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 43.1% · rank 21 | 1,115Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 48.7% · rank 24 | 1,189Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 48.5% · rank 34 | 1,241Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 52.9% · rank 15 | 1,257Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 51.1% · rank 30 | 1,267Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 49.9% · rank 28 | 1,264Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 47.5% · rank 30 | 1,258Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 49.1% · rank 21 | 1,225Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 51.2% · rank 27 | 1,281Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 51.5% · rank 31 | 1,264Elo | 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) |
|---|---|---|---|---|---|---|
| Meta | 1,048,576 | $1.25 | $4.25 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.