Muse Spark is a proprietary multimodal reasoning model developed by Meta Superintelligence Labs (MSL), the artificial intelligence division Meta reorganized in 2025. Announced on April 8, 2026, it is the first model in Meta's "Muse" series and the lab's debut release [1][2]. Meta presents it as a natively multimodal model "with support for tool-use, visual chain of thought, and multi-agent orchestration," small and fast enough to run interactively yet able to reason through hard problems in science, math, and health [1]. It is notable for two reasons beyond its capabilities: it is widely described as Meta's first frontier model, and it is the company's first flagship model released without open weights, a sharp break from the open LLaMA lineage that defined Meta's earlier AI strategy [3][4].

The model should not be confused with Microsoft's "Muse," an unrelated 2025 generative model for video-game world simulation. Meta's Muse Spark is a general-purpose large language model and reasoning system aimed at consumer assistants and developer applications.

Meta released Muse Spark 1.1 on July 9, 2026. The update expanded the model's agent, computer-use, coding, multimodal, and long-context capabilities, and became the first model offered through the public preview of Meta Model API.[13][14]

Overview

Muse Spark is positioned as the first rung on what Meta calls its scaling ladder toward "personal superintelligence" [1]. Rather than leading with a maximally large model, Meta describes a "deliberate and scientific approach to model scaling where each generation validates and builds on the last before we go bigger" [2]. Muse Spark is therefore characterized by Meta as "small and fast by design," with the next generation already in development [2].

The model accepts text, image, and voice input and can produce text alongside interactive outputs such as small websites, mini-games, and dashboards [2][5]. It ships with two everyday modes, branded Instant and Thinking, plus a heavier "Contemplating" mode that orchestrates multiple agents reasoning in parallel for the hardest queries [2][1]. Meta says it built the model with input from more than 1,000 physicians to strengthen health reasoning, a domain where it claims unusually strong results [1].

Attribute	Detail
Developer	Meta Superintelligence Labs
Series	Muse (Muse Spark is the first model)
Announced	April 8, 2026 [1][2]
Internal code name	Avocado [5][6]
Modality	Natively multimodal (text, image, voice) [1][2]
Reasoning modes	Instant, Thinking, Contemplating [2]
Weights	Proprietary / closed (no open weights) [3]
Access	meta.ai, Meta AI app, private API preview [1]

Meta Superintelligence Labs context

Muse Spark is the first publicly shipped model from Meta Superintelligence Labs, the unit Meta stood up after a leadership overhaul of its AI organization. The lab is led by Alexandr Wang, the former Scale AI chief executive who became Meta's first chief AI officer roughly nine months before the launch [5][7]. To bring Wang in, Meta paid about $14.3 billion for a 49% nonvoting stake in Scale AI, a deal that anchored the broader reset of the company's AI ambitions under Mark Zuckerberg [7].

The release marked Meta's first major model in over a year and was read by the press as the company's attempt to rejoin the frontier alongside OpenAI, Google, and Anthropic after a period of reduced visibility [7][8]. Meta says the work involved rebuilding its AI stack "from the ground up" over the preceding nine months, spanning model architecture, optimization, and data curation [1][2].

Announcement and release

Meta unveiled Muse Spark on April 8, 2026, through a Meta Superintelligence Labs blog post on ai.meta.com and a companion newsroom announcement on about.fb.com [1][2]. Reporting from CNBC, TechCrunch, and Bloomberg framed it as the first model out of the new lab and the start of a "ground-up overhaul" of Meta's AI efforts [7][5][3]. The model had been developed under the internal code name Avocado before launch [5][6].

At announcement the model began rolling out inside Meta's consumer products. A May 12, 2026 update to Meta's newsroom post described continued rollout to WhatsApp, Instagram, Facebook, Messenger, and Meta's Ray-Ban and Oakley smart glasses [2].

Muse Spark 1.1

Meta announced Muse Spark 1.1 on July 9, 2026 and described it as a significant upgrade within the same model family. The release was available in Thinking mode in the Meta AI app and on meta.ai. It also opened Meta Model API to a public developer preview, replacing the private API access described at the original model's launch with a broader but still restricted program.[13][15]

Agent operation and long context

Muse Spark 1.1 was trained for agent tasks that span native tools, Model Context Protocol servers, and custom skills. Meta describes a main-agent pattern in which the model gathers context, builds a plan, sends independent jobs to subagents in parallel, and combines their results. A subagent can report that it cannot complete a job so the main agent can revise the plan. This is an agent scaffold rather than a claim that the base model independently gains access to external services.[13][14]

Meta also raised the supported context window to 1 million tokens. The company says 1.1 actively manages that window by retrieving earlier actions and compacting history while preserving steps needed later in a task.[13] The evaluation report gives the model a score of 54.1 on a 1 million-token MRCR retrieval test, compared with 74 for GPT-5.5 in Meta's table. The window size therefore describes capacity, not reliable recall of every item placed anywhere in the context.[14]

For computer use, Meta says the model can choose between interface actions and scripts, batch related actions, and adjust when displayed information changes. These capabilities require an application to supply a computer-control harness and permissions. The model does not directly control a user's computer by default. Coding work was another focus of the update, with support for codebase exploration, bug fixes, features, migrations, testing, and delegation through agentic coding harnesses.[13][14]

Developer access

At release, Meta Model API was in public preview for US developers. Meta's developer site said new accounts received USD 20 in credits and could adapt existing OpenAI SDK code by changing the API configuration.[15][16] Reuters reported list prices of USD 1.25 per million input tokens and USD 4.25 per million output tokens.[18] The model remained proprietary: API access did not include downloadable weights or a reproducible training recipe.

The API surface exposed the model's tool use and multimodal inputs for developer applications. Meta's launch examples included visual-to-code work, detailed descriptions of images and video, and agents that inspect visual or audio material while completing a larger job.[13] Access conditions, regional eligibility, and preview pricing can change independently of the model version.

Evaluation and safety

Meta reported large gains on several tool, computer-use, and coding tests. The table below reproduces selected scores from the company's evaluation report. All 1.1 results used API access with xhigh reasoning, and the agent benchmarks depended on Meta's chosen tools, prompts, and harnesses. Meta cautioned that its setups might not be tuned to competitor strengths.[14]

Benchmark	Muse Spark 1.1	Original Muse Spark
Humanity's Last Exam, no tools	52.2	42.8
MCP Atlas	88.1	82.2
Toolathlon Verified	75.6	49.4
OSWorld Verified	80.8	53.3
TerminalBench 2.1	80.0	67.3
SWE-Bench Pro	61.5	55.0
DeepSWE 1.1	53.3	10.0
BabyVision	76.3	39.9
CharXiv Reasoning	88.4	88.9

The results were not uniformly higher. CharXiv Reasoning declined slightly, and the long-context retrieval result remained well below the strongest comparison in Meta's table.[14] Artificial Analysis scored 1.1 at 51 on its July 2026 Intelligence Index, eight points above its then-current rescored baseline of 43 for the original model but behind several leading systems. The firm had supported Meta's pre-release evaluation of the xhigh configuration. It also found that the hallucination rate on its Omniscience test fell from 73% to 38%, while the attempted-answer rate fell from 95% to 82% and accuracy fell from 45% to 41%. This suggests that more abstention accounted for part of the measured reduction in hallucinations.[17]

Meta's frontier-risk report made a distinction between the unmitigated model and the released system. Before mitigations, the evaluators could not rule out the report's high-risk threshold for chemical and biological capabilities or cybersecurity. After applying multiple layers of controls, Meta assessed the residual risk as moderate or lower in every evaluated domain and approved release.[14]

Model-level safety results improved substantially on several tests. StrongREJECT attack success fell from 25.2 for the original model to 0.5 for 1.1, and AgentDojo prompt-injection attack success fell from 11.9 to 0.7.[14] The report nevertheless recommends application-level policy checks, strict tool allowlists, workspace isolation, and other system controls. It also notes weaker performance than leading systems in some prompt-injection scenarios, including file-based attacks. The release therefore did not remove the need to constrain tools and review consequential actions.

Architecture and approach

Meta has disclosed the broad shape of how Muse Spark was built without releasing weights or a full technical report. The headline claim is efficiency: Meta states the model reaches its capabilities "with over an order of magnitude less compute than our previous model, Llama 4 Maverick," which it credits to an overhauled pretraining stack covering architecture, optimization, and data curation [1][3]. (For reference, Llama 4 Maverick was a mixture-of-experts model in the earlier, open-weight Llama 4 Scout and Maverick generation.)

On the post-training side, Meta describes reinforcement learning that shows log-linear scaling with smooth, predictable gains [1]. A distinctive element is what third-party coverage labeled "thought compression": during RL the model first improves by reasoning for longer, after which a penalty on thinking time pushes it to condense its chain of thought while holding onto the accuracy gains [9]. At inference, this thinking-time penalty combines with multi-agent orchestration to manage test-time compute [1].

Meta calls the model "natively multimodal," meaning vision and language were trained together from the start rather than a vision encoder being attached to a finished text model [9]. The company highlights performance on visual STEM questions, entity recognition, and localization as evidence of that integrated training [1].

Capabilities and intended use

Muse Spark powers a refreshed Meta AI assistant and is meant to be used conversationally across Meta's apps and glasses [2]. Beyond plain question answering, Meta and reviewers point to several specific abilities:

Multi-agent reasoning. The Contemplating mode launches multiple subagents that work a problem in parallel, generate candidate solutions, refine them, and aggregate a final answer. Meta pitches this as a rival to extreme reasoning features such as Google's Gemini Deep Think and OpenAI's GPT Pro mode [1][9].
Visual chain of thought. The model can reason over images and produce interactive visual annotations and displays, which Meta ties to its strong multimodal STEM results [1].
Health reasoning. Trained with input from over 1,000 physicians, Meta presents health as a standout area for the model [1].
Interactive generation. It can produce small interactive artifacts on the fly, including mini-games, dashboards, simple websites, and appliance-troubleshooting walk-throughs [2][5].
Voice and camera. It supports real-time voice conversation and live visual understanding through a device camera [2].

Benchmarks

Meta and independent benchmarker Artificial Analysis published a range of scores. The figures below combine Meta's own disclosures with Artificial Analysis's evaluation and a benchmark table compiled by MarkTechPost from Meta's materials. Where a comparison comes from a single source, that source is cited directly; cross-domain comparisons of this kind should be read with the usual caution about vendor-influenced framing [10][4][9].

Meta's headline results, in Contemplating mode where noted:

Benchmark	Muse Spark	Notes / comparison
Humanity's Last Exam (Contemplating)	58%	Meta-reported [1][4]
Humanity's Last Exam (with tools)	58.4	vs GPT-5.4 Pro 58.7, Gemini 3.1 Deep Think 53.4 [9]
FrontierScience Research (Contemplating)	38.3	vs GPT-5.4 Pro 36.7, Gemini 3.1 Deep Think 23.3 [9][1]
HealthBench Hard	42.8	vs GPT-5.4 Xhigh 40.1, Gemini 3.1 Pro High 20.6, Claude Opus 4.6 Max 14.8 [9]
ARC-AGI-2	42.5	weak spot vs Gemini 3.1 Pro High 76.5, GPT-5.4 Xhigh 76.1 [9]

Artificial Analysis's independent evaluation placed Muse Spark fourth on its Artificial Analysis Intelligence Index with a score of 52, inside the top five and behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6 [4][10]:

Benchmark (Artificial Analysis)	Muse Spark	Best competitor
Intelligence Index (overall)	52 (rank 4)	Gemini 3.1 Pro, GPT-5.4, Claude Opus 4.6 above [10]
Humanity's Last Exam	39.9%	Gemini 3.1 Pro Preview 44.7%, GPT-5.4 41.6% [10]
MMMU-Pro (vision)	80.5%	Gemini 3.1 Pro Preview 82.4% [10]
CritPT (physics research)	11%	fifth-highest; above Gemini 3 Flash 9% [10]
GDPval-AA (real-world tasks)	1427	Claude Sonnet 4.6 1648, GPT-5.4 1676 [10]

Artificial Analysis also reported that Muse Spark used about 58 million output tokens to complete the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and well below Claude Opus 4.6 (157M) and GPT-5.4 (120M), which the firm read as evidence of the model's efficiency-oriented design [10]. Its summary was that "Muse Spark essentially closes the gap to the frontier in a single release" [10].

Availability

At launch Muse Spark was available to general users for free on the meta.ai website and in the Meta AI app, and through a private API preview offered to select partners [1]. Using the consumer surface requires signing in with a Meta account via Facebook or Instagram [5]. Contemplating mode was described as rolling out gradually rather than being on by default [1].

Unlike the open-weight LLaMA models, Muse Spark is closed: its weights are not downloadable and, per Bloomberg, its design and code are not being made public [3][6]. Meta has said it hopes to open-source future versions of the model, and Zuckerberg reiterated that Meta still plans to release open-source models in the future, but the flagship itself is proprietary [2][5].

Reception

Coverage was mixed. Market-focused outlets treated the launch as a credible re-entry: Artificial Analysis called it a model that closes the gap to the frontier in one release, and some analysts framed it as evidence of "significant" monetization potential for Meta's assistant [10][8]. CNBC noted that while the model showed promise, Wall Street remained focused on the broader question of Zuckerberg's AI strategy and return on tens of billions of dollars of spending [11].

Reviewers were more skeptical on capability. Gizmodo's coverage, headlined that the model "doesn't exactly spark joy," argued that Muse Spark was clearly stronger than Meta's prior offerings but "typically fell short" of leading rivals, singling out coding and agentic tasks as continued weaknesses, and cautioned readers to treat Meta's benchmark figures with skepticism given the company's history [12]. Several outlets characterized the release as moving Meta from out of the race to competing for "also-ran" status rather than for the top spot [12]. Wang himself acknowledged the model was a starting point, saying it was MSL's first model and that there were "certainly rough edges we will polish over time" [4].

Limitations

The disclosed limitations fall into three buckets. First, capability gaps: independent and editorial reviews placed Muse Spark below the strongest frontier models on overall intelligence, coding, and agentic benchmarks, and Meta's own materials show a notable weakness on abstract-reasoning tests such as ARC-AGI-2 [12][9][10]. Second, transparency: as a closed model with no released weights and no full technical paper at launch, many architectural details remain undisclosed, and benchmark comparisons rely partly on Meta-supplied numbers [3][9]. Third, privacy and trust: because the consumer experience requires a Meta login and Meta has a contested history with user data, commentators raised concerns about how personal data might intersect with the model, especially given its health-assistant ambitions [5][12].

See also

Meta Superintelligence Labs
Meta AI
Llama 4
Large language model
Multimodal model

References

Meta Superintelligence Labs. "Introducing Muse Spark: Scaling Towards Personal Superintelligence." Meta AI Blog. https://ai.meta.com/blog/introducing-muse-spark-msl/
Meta. "Introducing Muse Spark: Meta's Most Powerful Model Yet." Meta Newsroom. https://about.fb.com/news/2026/04/introducing-muse-spark-meta-superintelligence-labs/
Maximilian Schreiner. "Meta's Muse Spark is its first frontier model and its first without open weights." The Decoder. https://the-decoder.com/metas-muse-spark-is-its-first-frontier-model-and-its-first-without-open-weights/
Artificial Analysis. "Muse Spark: Meta is back in the AI race." https://artificialanalysis.ai/articles/muse-spark-everything-you-need-to-know
Maxwell Zeff. "Meta debuts the Muse Spark model in a 'ground-up overhaul' of its AI." TechCrunch. https://techcrunch.com/2026/04/08/meta-debuts-the-muse-spark-model-in-a-ground-up-overhaul-of-its-ai/
AI CERTs. "Meta Superintelligence: Project Avocado and Muse Spark Strategy." https://www.aicerts.ai/news/meta-superintelligence-project-avocado-and-muse-spark-strategy/
Jonathan Vanian. "Meta debuts new AI model, attempting to catch Google, OpenAI after spending billions." CNBC. https://www.cnbc.com/2026/04/08/meta-debuts-first-major-ai-model-since-14-billion-deal-to-bring-in-alexandr-wang.html
Benzinga. "Meta Bets Big On Superintelligence With Muse Spark Launch, Analyst Sees 'Significant' Monetization Potential." https://www.benzinga.com/markets/tech/26/04/51723219/meta-bets-big-on-superintelligence-with-muse-spark-launch-analyst-sees-significant-monetization-potential
MarkTechPost. "Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents." https://www.marktechpost.com/2026/04/09/meta-superintelligence-lab-releases-muse-spark-a-multimodal-reasoning-model-with-thought-compression-and-parallel-agents/
Artificial Analysis. "Muse Spark: everything you need to know" (Intelligence Index evaluation). https://artificialanalysis.ai/articles/muse-spark-everything-you-need-to-know
Jonathan Vanian. "Meta Muse Spark has promise, Wall Street wants Zuckerberg AI strategy." CNBC. https://www.cnbc.com/2026/04/28/meta-muse-spark-has-promise-wall-street-wants-zuckerberg-ai-strategy.html
Kyle Barr. "Meta's First AI Model From Its Superintelligence Lab Doesn't Exactly Spark Joy." Gizmodo. https://gizmodo.com/metas-first-ai-model-from-its-superintelligence-lab-doesnt-exactly-spark-joy-2000744082
Meta Superintelligence Labs. "Introducing Muse Spark 1.1 and the Meta Model API." Meta AI Blog. https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/
Meta Superintelligence Labs. "Muse Spark 1.1 Evaluation Report." July 9, 2026. https://ai.meta.com/static-resource/muse-spark-1-1-evaluation-report
Meta. "Muse Spark on Meta Model API." Meta for Developers. https://developer.meta.com/ai/
Meta. "Build with Muse Spark, now available on Meta Model API." Meta for Developers. https://developer.meta.com/ai/resources/blog/build-with-muse-spark/
Artificial Analysis. "Muse Spark 1.1: Everything You Need to Know." https://artificialanalysis.ai/articles/muse-spark-1-1-everything-you-need-to-know
Reuters. "Meta debuts Muse Spark 1.1 with preview open to developers." July 9, 2026. https://www.investing.com/news/stock-market-news/meta-debuts-muse-spark-11-with-preview-open-to-developers-4784384

Editing: Muse Spark