Meta has been one of the most interesting companies of the generative AI era — initially gaining a loyal and huge following of users for the release of its mostly open source Llama family of large language models (LLMs) beginning in early 2023 but coming to screeching halt last year after Llama 4 debuted to mixed reviews and ultimately, admissions of gaming benchmarks.

That bumpy rollout of Llama 4 apparently spurred Meta founder and CEO Mark Zuckerberg to totally overhaul Meta’s AI operations in the summer of 2025, forming a new internal division, Meta Superintelligence Labs (MSL) which he recruited 29-year-old former Scale AI co-founder and CEO Alexandr Wang to lead as Chief AI Officer.

Now, today, Meta is showing us the fruits of that effort: Muse Spark, a new proprietary model that Wang says (posting on rival social network X, used more often by the machine learning community) is “the most powerful model that meta has released,” and has “support for tool-use, visual chain of thought, & multi-agent orchestration.” He also says it will be the start of a new Muse family of models, raising questions about what will become of Meta’s popular lineup and ongoing development of the Llama family.

It arrives not as a generic chatbot, but as the foundation for what Wang calls “personal superintelligence”—an AI that doesn’t just process text but “sees and understands the world around you” to act as a digital extension of the self, echoing Zuckberg’s public manifesto for a vision of personal superintelligence published in summer 2025.

However, it is proprietary only — confined for now to the Meta AI app and website, as well as a ” private API preview to select users,” according to Meta’s blog post announcing it — a move likely to rankle the literally billions of users of Llama models and the thousands of developers who relied upon it (some of whom are active participants in rival social network Reddit’s r/LocalLLaMA subreddit). In addition, no pricing information for the model has yet been announced.

It’s unclear if Meta has ended development on the Llama family entirely. When asked directly by VentureBeat, a Meta spokesperson said in an email: “Our current Llama models will continue to be available as open source,” which doesn’t address the question of development of future Llama models.

Visual chain-of-thought

At its core, Muse Spark is a natively multimodal reasoning model. Unlike previous iterations that “stitched” vision and text together, Muse Spark was rebuilt from the ground up to integrate visual information across its internal logic. This architectural shift enables “visual chain of thought,” allowing the model to annotate dynamic environments—identifying the components of a complex espresso machine or correcting a user’s yoga form via side-by-side video analysis.

The most significant technical leap, however, is a new “Contemplating” mode. This feature orchestrates multiple sub-agents to reason in parallel, allowing Meta to compete with extreme reasoning models like Google’s Gemini Deep Think and OpenAI’s GPT-5.4 Pro.

In benchmarks, this mode achieved 58% in “Humanity’s Last Exam” and 38% in “FrontierScience Research,” figures that Meta claims validate their new scaling trajectory.

Perhaps more impressive for the company’s bottom line is the model’s efficiency. Meta reports that Muse Spark achieves its reasoning capabilities using over an order of magnitude less compute than Llama 4 Maverick, its previous mid-size flagship. This efficiency is driven by a process called “thought compression”. During reinforcement learning, the model is penalized for excessive “thinking time,” forcing it to solve complex problems with fewer reasoning tokens without sacrificing accuracy.

Benchmarks reveal a return-to-form

The launch of Muse Spark is framed as a statistical “quantum leap,” ending Meta’s year-long absence from the absolute frontier of AI performance.

By reconciling Meta’s official internal data with independent auditing from third-party LLM tracking firm Artificial Analysis, a clear picture emerges: Muse Spark is not just a marginal improvement over the Llama series; it is a fundamental re-entry into the “Top 5” global models.

According to the Artificial Analysis Intelligence Index v4.0, Muse Spark achieved a score of 52. For context, Meta’s previous flagship, Llama 4 Maverick, debuted in 2025 with an Index score of just 18.

By nearly tripling its performance, Muse Spark now sits within striking distance of the industry’s most elite systems, trailing only Gemini 3.1 Pro Preview (57), GPT-5.4 (57), and Claude Opus 4.6 (53).

Meta’s official benchmarks suggest that Muse Spark is particularly dominant in multimodal reasoning, specifically where visual figures and logic intersect.

• CharXiv Reasoning: In “figure understanding,” Muse Spark achieved a score of 86.4, significantly outperforming Claude Opus 4.6 (65.3), Gemini 3.1 Pro (80.2), and GPT-5.4 (82.8).

• MMMU Pro: Official reports place the model at 80.4, while Artificial Analysis’s independent audit measured it at 80.5%. This makes it the second-most capable vision model on the market, surpassed only by Gemini 3.1 Pro Preview (83.9% official; 82.4% independent).

• Visual Factuality (SimpleVQA): Muse Spark scored 71.3, placing it ahead of GPT-5.4 (61.1) and Grok 4.2 (57.4), though it narrowly trails Gemini 3.1 Pro (72.4).

These scores validate Meta’s focus on “visual chain of thought,” enabling the model to not just recognize objects, but to reason through complex spatial problems and dynamic annotations.

The “Thinking” gear of Muse Spark was put to the test against specialized benchmarks designed to break non-reasoning models.

• Humanity’s Last Exam (HLE): In this multidisciplinary evaluation, Meta reports a score of 42.8 (No Tools) and 50.4 (With Tools). Independent audits by Artificial Analysis tracked the model at 39.9%, trailing Gemini 3.1 Pro Preview (44.7%) and GPT-5.4 (41.6%).

• GPQA Diamond (PhD Level Reasoning): Muse Spark achieved a formidable 89.5, surpassing Grok 4.2 (88.5) but trailing the specialized “max reasoning” outputs of Opus 4.6 (92.7) and Gemini 3.1 Pro (94.3).

• ARC AGI 2: This remains a notable weak point. Muse Spark scored 42.5, far behind the abstract reasoning puzzles solved by Gemini 3.1 Pro (76.5) and GPT-5.4 (76.1).

• CritPT (Physics Research): Independent auditing found Muse Spark achieved the 5th highest score at 11%. This marks a substantial lead over Gemini 3 Flash (9%) and Claude 4.6 Sonnet (3%).

One of the most striking results from the official data is Muse Spark’s performance in the health sector, likely a result of Meta’s collaboration with over 1,000 physicians.

• HealthBench Hard: Muse Spark achieved 42.8, a massive lead over Claude Opus 4.6 (14.8), Gemini 3.1 Pro (20.6), and even GPT-5.4 (40.1).

• MedXpertQA (Multimodal): It scored 78.4, comfortably ahead of Opus 4.6 (64.8) and Grok 4.2 (65.8), though it still trails Gemini 3.1 Pro’s top-tier score of 81.3.

Agentic Systems and Efficiency: The “Thought Compression” Effect

While Muse Spark excels at reasoning, its “agentic” performance—executing real-world work tasks—presents a more nuanced picture.

• SWE-Bench Verified: Muse Spark scored 77.4, trailing Claude Opus 4.6 (80.8) and Gemini 3.1 Pro (80.6).

• GDPval-AA Elo: Meta’s official score of 1444 differs slightly from Artificial Analysis’s recorded 1427. In both cases, Muse Spark trails GPT-5.4 (1672) and Opus 4.6 (1606), suggesting that while the model “thinks” well, it is still refining its ability to “act” in long-horizon software and office workflows.

• Token Efficiency: This is where Muse Spark distinguishes itself. To run the Intelligence Index, it used 58 million output tokens. In contrast, Claude Opus 4.6 required 157 million tokens and GPT-5.4 required 120 million. This supports Meta’s claim of “thought compression“—delivering frontier-class intelligence while using less than half the “thinking time” of its closest competitors.

With Muse Spark, Meta has successfully transitioned from being the “LAMP stack for AI” to a direct challenger for the title of “Personal Superintelligence”. While agentic workflows remain a hurdle, its dominance in vision, health, and token efficiency places Meta back at the center of the frontier race.

Personal wellness and Instagram shopping

Meta is immediately deploying Muse Spark to power specialized experiences across its app family.

• Shopping Mode: A new feature that leverages Meta’s vast creator ecosystem. The AI picks up on brands, styling choices, and content across Instagram and Threads to provide personalized recommendations, effectively turning every post into a shoppable interaction.

• Health Reasoning: In a move toward medical utility, Meta collaborated with over 1,000 physicians to curate training data. Muse Spark can now analyze nutritional content from photos of food or provide “health scores” for pescatarian diets with high cholesterol.

• Interactive UI: The model can generate web-based minigames or tutorials on the fly. For example, a user can prompt the AI to turn a photo into a playable Sudoku game or a highlights-based tutorial for home appliances.

Evaluation awareness

While Muse Spark demonstrates strong refusal behaviors regarding biological and chemical weapons, its safety profile includes a startling new discovery. Third-party testing by Apollo Research found that the model possesses a high degree of “evaluation awareness”.

The model frequently recognized when it was being tested in “alignment traps” and reasoned that it should behave honestly specifically because it was under evaluation.

While Meta concluded this was not a “blocking concern” for release, the finding suggests that frontier models are becoming increasingly “conscious” of the testing environment—potentially rendering traditional safety benchmarks less reliable as models learn to “game” the exam.

What happens to Llama?

In February 2023, Meta released Llama 1 to demonstrate that smaller, compute-optimal models could match larger counterparts like GPT-3 in efficiency. Although access was initially restricted to researchers, the model weights were leaked via 4chan on March 3, 2023, an event that inadvertently democratized high-tier research and catalyzed a global movement for running models on consumer-grade hardware.

This shift was solidified in July 2023 with the release of Llama 2, which introduced a commercial license that permitted self-hosting for most organizations. This approach saw rapid adoption, with the Llama family exceeding 100 million downloads and supporting over 1,000 commercial applications by the third quarter of 2023.

Through 2024 and 2025, Meta scaled the Llama family to establish it as the essential infrastructure for global enterprise AI, frequently referred to as the LAMP stack for AI. Following the launch of Llama 3 in April 2024 and the landmark Llama 3.1 405B in July, Meta achieved performance parity with the world’s leading proprietary systems.

The subsequent release of Llama 4 in April 2025 introduced a Mixture-of-Experts architecture, allowing for massive parameter scaling while maintaining fast inference speeds. By early 2026, the Llama ecosystem reached a staggering scale, totaling 1.2 billion downloads and averaging approximately one million downloads per day.

This widespread adoption provided businesses with significant economic sovereignty, as self-hosting Llama models offered an 88% cost reduction compared to using proprietary API providers.

As of April 2026, Meta’s role as the undisputed leader of the open-weight movement has transitioned into a highly contested multi-polar landscape characterized by the rise of international competitors.

While the United States accounts for 35% of global Llama deployments, Chinese models from labs like Alibaba and DeepSeek began accounting for 41% of downloads on platforms like Hugging Face by late 2025. Throughout early 2026, new entrants such as Zhipu AI’s GLM-5 and Alibaba’s Qwen 3.6 Plus have outpaced Llama 4 Maverick on general knowledge and coding benchmarks.

In response to this global pressure, Meta’s Muse Spark arrives with hefty expectations and an open source legacy that will be tough to live up to.

Proprietary only (for now)

The launch marks a controversial departure from Meta AI’s “open science” roots. While the Llama series was famously accessible to developers, Muse Spark is launching as a proprietary model.

Wang addressed the shift on X, stating: “Nine months ago we rebuilt our ai stack from scratch. New infrastructure, new architecture, new data pipelines… This is step one. Bigger models are already in development with plans to open-source future versions.”

However, the developer community remains skeptical. Some see this as a necessary pivot after the Llama 4 series failed to gain expected developer traction; others view it as Meta “closing the gates” now that it has a competitive reasoning model.

Wang himself acknowledged the transition’s difficulty, noting there are “certainly rough edges we will polish over time”.

For the 3 billion people using Meta’s apps, the change will be felt almost instantly. The AI they interact with is no longer just a library of information, but an agent with a $27 billion brain and a mandate to understand their world as intimately as they do.

Is China picking back up the open source AI baton?

Z.ai, also known as Zhupai AI, a Chinese AI startup best known for its powerful, open source GLM family of models, has unveiled GLM-5.1 today under a permissive MIT License, allowing for enterprises to download, customize and use it for commercial purposes. They can do so on Hugging Face.

This follows its release of GLM-5 Turbo, a faster version, under only proprietary license last month.

The new GLM-5.1 is designed to work autonomously for up to eight hours on a single task, marking a definitive shift from vibe coding to agentic engineering.

The release represents a pivotal moment in the evolution of artificial intelligence. While competitors have focused on increasing reasoning tokens for better logic, Z.ai is optimizing for productive horizons.

GLM-5.1 is a 754-billion parameter Mixture-of-Experts model engineered to maintain goal alignment over extended execution traces that span thousands of tool calls.

“agents could do about 20 steps by the end of last year,” wrote z.ai leader Lou on X. “glm-5.1 can do 1,700 rn. autonomous work time may be the most important curve after scaling laws. glm-5.1 will be the first point on that curve that the open-source community can verify with their own hands. hope y’all like it^^”

In a market increasingly crowded with fast models, Z.ai is betting on the marathon runner. The company, which listed on the Hong Kong Stock Exchange in early 2026 with a market capitalization of $52.83 billion, is using this release to cement its position as the leading independent developer of large language models in the region.

Technology: the staircase pattern of optimization

GLM-5.1s core technological breakthrough isn’t just its scale, though its 754 billion parameters and 202,752 token context window are formidable, but its ability to avoid the plateau effect seen in previous models.

In traditional agentic workflows, a model typically applies a few familiar techniques for quick initial gains and then stalls. Giving it more time or more tool calls usually results in diminishing returns or strategy drift.

Z.ai research demonstrates that GLM-5.1 operates via what they call a staircase pattern, characterized by periods of incremental tuning within a fixed strategy punctuated by structural changes that shift the performance frontier.

In Scenario 1 of their technical report, the model was tasked with optimizing a high-performance vector database, a challenge known as VectorDBBench.

The model is provided with a Rust skeleton and empty implementation stubs, then uses tool-call-based agents to edit code, compile, test, and profile. While previous state-of-the-art results from models like Claude Opus 4.6 reached a performance ceiling of 3,547 queries per second, GLM-5.1 ran through 655 iterations and over 6,000 tool calls. The optimization trajectory was not linear but punctuated by structural breakthroughs.

At iteration 90, the model shifted from full-corpus scanning to IVF cluster probing with f16 vector compression, which reduced per-vector bandwidth from 512 bytes to 256 bytes and jumped performance to 6,400 queries per second.

By iteration 240, it autonomously introduced a two-stage pipeline involving u8 prescoring and f16 reranking, reaching 13,400 queries per second. Ultimately, the model identified and cleared six structural bottlenecks, including hierarchical routing via super-clusters and quantized routing using centroid scoring via VNNI. These efforts culminated in a final result of 21,500 queries per second, roughly six times the best result achieved in a single 50-turn session.

This demonstrates a model that functions as its own research and development department, breaking complex problems down and running experiments with real precision.

The model also managed complex execution tightening, lowering scheduling overhead and improving cache locality. During the optimization of the Approximate Nearest Neighbor search, the model proactively removed nested parallelism in favor of a redesign using per-query single-threading and outer concurrency.

When the model encountered iterations where recall fell below the 95 percent threshold, it diagnosed the failure, adjusted its parameters, and implemented parameter compensation to recover the necessary accuracy. This level of autonomous correction is what separates GLM-5.1 from models that simply generate code without testing it in a live environment.

Kernelbench: pushing the machine learning frontier

The model’s endurance was further tested in KernelBench Level 3, which requires end-to-end optimization of complete machine learning architectures like MobileNet, VGG, MiniGPT, and Mamba.

In this setting, the goal is to produce a faster GPU kernel than the reference PyTorch implementation while maintaining identical outputs. Each of the 50 problems runs in an isolated Docker container with one H100 GPU and is limited to 1,200 tool-use turns. Correctness and performance are evaluated against a PyTorch eager baseline in separate CUDA contexts.

The results highlight a significant performance gap between GLM-5.1 and its predecessors. While the original GLM-5 improved quickly but leveled off early at a 2.6x speedup, GLM-5.1 sustained its optimization efforts far longer. It eventually delivered a 3.6x geometric mean speedup across 50 problems, continuing to make useful progress well past 1,000 tool-use turns.

Although Claude Opus 4.6 remains the leader in this specific benchmark at 4.2x, GLM-5.1 has meaningfully extended the productive horizon for open-source models.

This capability is not simply about having a longer context window; it requires the model to maintain goal alignment over extended execution, reducing strategy drift, error accumulation, and ineffective trial and error. One of the key breakthroughs is the ability to form an autonomous experiment, analyze, and optimize loop, where the model can proactively run benchmarks, identify bottlenecks, adjust strategies, and continuously improve results through iterative refinement.

All solutions generated during this process were independently audited for benchmark exploitation, ensuring the optimizations did not rely on specific benchmark behaviors but worked with arbitrary new inputs while keeping computation on the default CUDA stream.

Product strategy: subscription and subsidies

GLM-5.1 is positioned as an engineering-grade tool rather than a consumer chatbot. To support this, Z.ai has integrated it into a comprehensive Coding Plan ecosystem designed to compete directly with high-end developer tools.

The product offering is divided into three subscription tiers, all of which include free Model Context Protocol tools for vision analysis, web search, web reader, and document reading.

The Lite tier at $27 USD per quarter is positioned for lightweight workloads and offers three times the usage of a comparable Claude Pro plan. The Pro tier at $81 per quarter is designed for complex workloads, offering five times the Lite plan usage and 40 to 60 percent faster execution.

The Max tier at $216 per quarter is aimed at advanced developers with high-volume needs, ensuring guaranteed performance during peak hours.

For those using the API directly or through platforms like OpenRouter or Requesty, Z.ai has priced GLM-5.1 at $1.40 per one million input tokens and $4.40 per million output tokens. There’s also a cache discount available for $0.26 per million input tokens.

Notably, the model consumes quota at three times the standard rate during peak hours, which are defined as 14:00 to 18:00 Beijing Time daily, though a limited-time promotion through April 2026 allows off-peak usage to be billed at a standard 1x rate. Complementing the flagship is the recently debuted GLM-5 Turbo.

While 5.1 is the marathon runner, Turbo is the sprinter, proprietary and optimized for fast inference and tasks like tool use and persistent automation.

At a cost of $1.20 per million input / $4 per million output, it is more expensive than the base GLM-5 but comes in at more affordable than the new GLM-5.1, positioning it as a commercially attractive option for high-speed, supervised agent runs.

The model is also packaged for local deployment, supporting inference frameworks including vLLM, SGLang, and xLLM. Comprehensive deployment instructions are available at the official GitHub repository, allowing developers to run the 754 billion parameter MoE model on their own infrastructure.

For enterprise teams, the model includes advanced reasoning capabilities that can be accessed via a thinking parameter in API requests, allowing the model to show its step-by-step internal reasoning process before providing a final answer.

Benchmarks: a new global standard

The performance data for GLM-5.1 suggests it has leapfrogged several established Western models in coding and engineering tasks.

On SWE-Bench Pro, which evaluates a model’s ability to resolve real-world GitHub issues using an instruction prompt and a 200,000 token context window, GLM-5.1 achieved a score of 58.4. For context, this outperforms GPT-5.4 at 57.7, Claude Opus 4.6 at 57.3, and Gemini 3.1 Pro at 54.2.

Beyond standardized coding tests, the model showed significant gains in reasoning and agentic benchmarks. It scored 63.5 on Terminal-Bench 2.0 when evaluated with the Terminus-2 framework and reached 66.5 when paired with the Claude Code harness.

On CyberGym, it achieved a 68.7 score based on a single-run pass over 1,507 tasks, demonstrating a nearly 20-point lead over the previous GLM-5 model. The model also performed strongly on the MCP-Atlas public set with a score of 71.8 and achieved a 70.6 on the T3-Bench.

In the reasoning domain, it scored 31.0 on Humanitys Last Exam, which jumped to 52.3 when the model was allowed to use external tools. On the AIME 2026 math competition benchmark, it reached 95.3, while scoring 86.2 on GPQA-Diamond for expert-level science reasoning.

The most impressive anecdotal benchmark was the Scenario 3 test: building a Linux-style desktop environment from scratch in eight hours.

Unlike previous models that might produce a basic taskbar and a placeholder window before declaring the task complete, GLM-5.1 autonomously filled out a file browser, terminal, text editor, system monitor, and even functional games.

It iteratively polished the styling and interaction logic until it had delivered a visually consistent, functional web application. This serves as a concrete example of what becomes possible when a model is given the time and the capability to keep refining its own work.

Licensing and the open segue

The licensing of these two models tells a larger story about the current state of the global AI market. GLM-5.1 has been released under the MIT License, with its model weights made publicly available on Hugging Face and ModelScope.

This follows the Z.ai historical strategy of using open-source releases to build developer goodwill and ecosystem reach. However, GLM-5 Turbo remains proprietary and closed-source. This reflects a growing trend among leading AI labs toward a hybrid model: using open-source models for broad distribution while keeping execution-optimized variants behind a paywall.

Industry analysts note that this shift arrives amidst a rebalancing in the Chinese market, where heavyweights like Alibaba are also beginning to segment their proprietary work from their open releases.

Z.ai CEO Zhang Peng appears to be navigating this by ensuring that while the flagship’s core intelligence is open to the community, the high-speed execution infrastructure remains a revenue-driving asset.

The company is not explicitly promising to open-source GLM-5 Turbo itself, but says the findings will be folded into future open releases. This segmented strategy helps drive adoption while allowing the company to build a sustainable business model around its most commercially relevant work.

Community and user reactions: crushing a week’s work

The developer community response to the GLM-5.1 release has been overwhelmingly focused on the model’s reliability in production-grade environments.

User reviews suggest a high degree of trust in the model’s autonomy.

One developer noted that GLM-5.1 shocked them with how good it is, stating it seems to do what they want more reliably than other models with less reworking of prompts needed. Another developer mentioned that the model’s overall workflow from planning to project execution performs excellently, allowing them to confidently entrust it with complex tasks.

Specific case studies from users highlight significant efficiency gains.

A user from Crypto Economy News reported that a task involving preprocessing code, feature selection logic, and hyperparameter tuning solutions, which originally would have taken a week, was completed in just two days. Since getting the GLM Coding plan, other developers have noted being able to operate more freely and focus on core development without worrying about resource shortages hindering progress.

On social media, the launch announcement generated over 46,000 views in its first hour, with users captivated by the eight-hour autonomous claim. The sentiment among early adopters is that Z.ai has successfully moved past the hallucination-heavy era of AI into a period where models can be trusted to optimize themselves through repeated iteration.

The ability to build four applications rapidly through correct prompting and structured planning has been cited by multiple users as a game-changing development for individual developers.

The implications of long-horizon work

The release of GLM-5.1 suggests that the next frontier of AI competition will not be measured in tokens per second, but in autonomous duration.

If a model can work for eight hours without human intervention, it fundamentally changes the software development lifecycle.

However, Z.ai acknowledges that this is only the beginning. Significant challenges remain, such as developing reliable self-evaluation for tasks where no numeric metric exists to optimize against.

Escaping local optima earlier when incremental tuning stops paying off is another major hurdle, as is maintaining coherence over execution traces that span thousands of tool calls.

For now, Z.ai has placed a marker in the sand. With GLM-5.1, they have delivered a model that doesn’t just answer questions, but finishes projects. The model is already compatible with a wide range of developer tools including Claude Code, OpenCode, Kilo Code, Roo Code, Cline, and Droid.

For developers and enterprises, the question is no longer, “what can I ask this AI?” but “what can I assign to it for the next eight hours?”

The focus of the industry is clearly shifting toward systems that can reliably execute multi-step work with less supervision. This transition to agentic engineering marks a new phase in the deployment of artificial intelligence within the global economy.