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Favicon for meta

Meta: Muse Spark 1.1

meta/muse-spark-1.1

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Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context window.

The model is designed to orchestrate multi-agent workflows, acting as either a main agent that plans and delegates or as a subagent, and generalizes zero-shot to new tools, MCP servers, and custom skills. It supports structured output, parallel function calling, built-in search with citations, and configurable reasoning effort. Meta reports strong performance on real-world coding across large codebases, computer-use workflows, and visual-to-code generation.

Modalities

In / Out Price

$1.25 / $4.25per 1M

Context

1.0M

Released

Jul 16, 2026

Compare
ProvidersPricingPerformanceUptimeBenchmarksAppsActivityFAQExplore

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Benchmarks

Scores on standardized evaluations. Higher percentages are better — and rank percentile shows where this model lands among all models on OpenRouter.

Benchmark score summary for Meta: Muse Spark 1.1 (Artificial Analysis and Design Arena)
SourceBenchmarkScore
Artificial AnalysisMuse Spark 1.1 (xhigh) Intelligence Index34.3
Artificial AnalysisMuse Spark 1.1 (xhigh) Coding Index71.3
Artificial AnalysisMuse Spark 1.1 (xhigh) Agentic Index27.5
Artificial AnalysisMuse Spark 1.1 (xhigh) GPQA Diamond89.8%
Artificial AnalysisMuse Spark 1.1 (xhigh) HLE46.2%
Artificial AnalysisMuse Spark 1.1 (xhigh) AA-LCR77.7%
Artificial AnalysisMuse Spark 1.1 (xhigh) GDPval-AA39.7%
Artificial AnalysisMuse Spark 1.1 (xhigh) CritPt15.1%
Artificial AnalysisMuse Spark 1.1 (xhigh) SciCode58.8%
Artificial AnalysisMuse Spark 1.1 (xhigh) AA-Omniscience Accuracy52.0%
Artificial AnalysisMuse Spark 1.1 (xhigh) AA-Omniscience Non-Hallucination Rate50.0%
Design ArenaMuse Spark 1.1 Agents Arena Agenticgamedev Elo1183
Design ArenaMuse Spark 1.1 Agents Arena Androidnative Elo1189
Design ArenaMuse Spark 1.1 Agents Arena Full Stack Elo1214
Design ArenaMuse Spark 1.1 Agents Arena Godotgamedev Elo1141
Design ArenaMuse Spark 1.1 Agents Arena Htmlslides Elo1197
Design ArenaMuse Spark 1.1 Agents Arena Mobile Apps Elo1201
Design ArenaMuse Spark 1.1 Agents Arena Python-Pptxslides Elo1156
Design ArenaMuse Spark 1.1 Agents Arena Webapps Elo1216
Design ArenaMuse Spark 1.1 Models Arena 3D Elo1294
Design ArenaMuse Spark 1.1 Models Arena Asciiart Elo1302
Design ArenaMuse Spark 1.1 Models Arena Code Categories Elo1291
Design ArenaMuse Spark 1.1 Models Arena Data Visualization Elo1290
Design ArenaMuse Spark 1.1 Models Arena Game Development Elo1299
Design ArenaMuse Spark 1.1 Models Arena SVG Elo1276
Design ArenaMuse Spark 1.1 Models Arena UI Component Elo1307
Design ArenaMuse Spark 1.1 Models Arena Website Elo1281

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

Quick Start

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

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Throughput

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Latency

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P50, best provider

AutoExacto Benchmarks
vgi_benchauto-routing54.7%Meta54.4%
Uptime (3d)The model was reachable. Request routed to a provider.

100.00%

Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.

98.33%

Availability over the last 3 days

Last 72 hours
Availability 98.33%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
99.07%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.

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Frequently asked questions

Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context window. The model is designed to orchestrate multi-agent workflows, acting as either a main agent that plans and delegates or as a subagent, and generalizes zero-shot to new tools, MCP servers, and custom skills.

Muse Spark 1.1 costs $1.25/M input tokens and $4.25/M output tokens, with separate rates for Cache Read at $0.15/M tokens and Web Search at $2.50/1K calls.

Muse Spark 1.1 has a 1,048,576 token context window.

Yes. Muse Spark 1.1 accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

Muse Spark 1.1 accepts text, images, video, files such as PDFs and audio as input and returns text.

Muse Spark 1.3 Contributor, Muse Spark 1.3, Muse Spark 1.2 Contributor and 2 more are other text models from Meta.

Muse Spark 1.1 was released on July 16, 2026.

More models from Meta

Muse Voice Transcribe 1.0

Muse Voice Transcribe 1.0 is a synchronous speech-to-text model from Meta. It is suited for push-to-talk, endpointing, and speaker-aware transcription, with keyword biasing for domain terms and language biasing through language-name hints. It accepts mono 16-bit PCM WAV audio at 16 kHz or 24 kHz for recordings up to 10 minutes. It does not provide word-level timestamps or confidence scores, and other audio formats must be converted to WAV before upload.

Transcription$0.18/hour
Muse Spark 1.3 Contributor

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.

Text1.0M context$0.10 / $0.20
Muse Spark 1.3

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 conflicting inputs, and request clarification or confirmation when needed, with an emphasis on concise execution.

Text1.0M context$1.25 / $4.25
Muse Image

Muse Image is an agentic image generation model from Meta that generates and edits images from text and reference images. Unlike single-pass image models, it reasons before it renders, breaking down multi-part prompts and refining its output within the chain of thought, and invokes web search for factual accuracy on knowledge-intensive prompts.

The model supports text-to-image generation, targeted image editing, multi-image composition, reference-image conditioning for style and subject consistency across a series, and precise text rendering within generated images. Iterative editing works by passing the previous output image back with a new instruction.

Image$0.01/image
Muse Spark 1.2 Contributor

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.

Text1.0M context$0.10 / $0.20
Muse Glimmer 30B

Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon agentic and coding workflows, with multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across more than 100 languages.

Text131K context$0.30 / $1.10
Muse Glimmer 30B

Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon agentic and coding workflows, with multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across more than 100 languages.

Text131K context$0.175 / $0.75
Muse Spark 1.2

Muse Spark 1.2 is a reasoning model from Meta, designed 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.

Text1.0M context$1.25 / $4.25