What Is Muse Spark 1.1? Meta's Agentic Reasoning Model Explained

Muse Spark 1.1 is a proprietary multimodal reasoning model developed by Meta Superintelligence Labs, released on July 9, 2026, and built for agentic tasks with a 1-million-token context window. It runs in "Thinking" mode inside the Meta AI app and at meta.ai, and is available to developers through the new Meta Model API at $1.25 per million input tokens and $4.25 per million output tokens. It is Meta's second Superintelligence Labs model and the first to ship behind a paid developer API, positioning Meta alongside Anthropic and OpenAI in the frontier-tier API business.

Key Takeaways

• Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs, released July 9, 2026, upgrading the original Muse Spark that debuted in April 2026.
• The model has a 1-million-token context window and is trained to orchestrate multi-agent systems, delegate to subagents, and actively compact its own context.
• API pricing is $1.25 per million input tokens and $4.25 per million output tokens, with a $0.15 cache-hit input price and $20 in free credits for new developers.
• Muse Spark 1.1 is proprietary and closed-weight, unlike Meta's open Llama family — there is no local deployment path.
• It scores 51 on the Artificial Analysis Intelligence Index and 69 on the Coding Agent Index (Opencode harness), landing just below GPT-5.5 in Codex and above Claude Opus 4.8 in Claude Code.
• The launch also opened a public preview of the Meta Model API, Meta's first paid developer API for a proprietary frontier model.

What Is Muse Spark 1.1?

Muse Spark 1.1 is the second model from Meta Superintelligence Labs (MSL) and a direct upgrade of the original Muse Spark released in April 2026. Meta describes it as a multimodal reasoning model built for agentic tasks, with meaningful gains in tool use, computer use, coding, and multimodal understanding over the first release. It reached general availability on July 9, 2026, according to Meta's own announcement on ai.meta.com.

The model sits above Meta's open-weight Llama family in positioning but not in licensing: Muse Spark 1.1 is proprietary and closed-weight, distributed only through Meta's own consumer and developer surfaces. Meta is aiming it squarely at what it calls "personal agentic tasks" — planning and orchestrating work across external apps, services, and computer interfaces rather than answering single-turn questions.

Muse Spark 1.1 launched alongside a public preview of the Meta Model API, Meta's first paid API for a proprietary frontier model. That is the structural change the release signals: Meta is now in the same paid-API business as Anthropic and OpenAI, not only shipping open weights.

Muse Spark 1.1 at a Glance

Specification	Detail
Developer	Meta Superintelligence Labs
Model family	Muse Spark
Type	Multimodal reasoning model for agentic tasks
Release date	July 9, 2026
Predecessor	Muse Spark (April 2026)
Input modality	Text, image
Output modality	Text
Context window	1,000,000 tokens
Max output tokens	256,000 (per Vals AI)
Reasoning mode	Yes ("Thinking" mode; xhigh reasoning variant benchmarked)
API input price	$1.25 per 1M tokens
API output price	$4.25 per 1M tokens
Cache-hit input price	$0.15 per 1M tokens
Free credits	$20 for new developers
Consumer availability	Meta AI app, meta.ai ("Thinking" mode)
Developer availability	Meta Model API (public preview)
Third-party hosts	OpenCode, Vercel (per community reports)
License	Proprietary, closed-weight
Weights availability	Not released
Regional availability	Limited — "not available in all regions yet" per community reports

How Muse Spark 1.1 Works and What Makes It Different

The engineering pitch for Muse Spark 1.1 centers on four capabilities Meta groups together as Personal Agentic Tasks: planning, tool orchestration, computer use, and long-context management. Each has a concrete mechanism behind it.

Multi-Agent Orchestration. Meta trained Muse Spark 1.1 to run as either the main agent or a subagent. As the main agent it gathers context, produces a plan, and delegates execution to parallel subagents to reduce end-to-end latency. As a subagent it adheres to its assigned job, understands its available tools, and escalates back to the main agent when the task exceeds its scope. This is framed in Meta's launch post as the core reason Muse Spark 1.1 finishes complex projects meaningfully faster than the first Muse Spark.

Active Context Management. The 1-million-token window is not just larger — Meta says the model is trained to actively manage it. Muse Spark 1.1 remembers earlier actions, retrieves information from work done many steps back, and compacts intermediate state while preserving the critical steps it will need later. Meta calls this behavior context compaction.

Adaptive Computer Use. Rather than clicking through every desktop step, the model decides when to write a script for automation and when to interact with a UI directly. Meta trained it to generate batches of actions at each step and to adapt to unfamiliar interfaces with minimal human intervention.

Agentic Coding Harness Support. Muse Spark 1.1 is designed to slot into popular agentic coding harnesses, supporting planning mode, goal conditioning, subagent delegation, and context compaction. Meta demonstrates a debugging loop in OpenCode where the model builds a chat web app, takes screenshots to detect user-visible failures, traces them to code, implements fixes, and validates.

The differentiator is not raw benchmark leadership but the shape of the tradeoff: near-frontier intelligence at a lower cost per finished task, tuned specifically for agent workloads.

What You Can Do With Muse Spark 1.1

Real usage in the first week clusters around a few concrete workloads. Early independent testers and developer platforms describe the following patterns.

Coding agents inside OpenCode. Community post-mortems on the r/opencodeCLI subreddit confirm Muse Spark 1.1 shipped as an available model in the OpenCode CLI on release day, with a free $20 credit on Meta's site. Meta's own demo shows the model debugging a chat web app end-to-end through OpenCode.

Enterprise document-grounded work. Box ran Muse Spark 1.1 through its Box Complex Work Eval — a benchmark of realistic, multi-document analytical tasks — and reported the model holds its own against today's leading models, with notable wins on data analysis. In one cost-optimization task Muse Spark 1.1 outperformed the high-performing composite by nearly 30 points, and in a client-portfolio segmentation task it beat the composite by more than 25 points, according to Box's post-launch report.

Everyday reasoning and code as a Kimi K2.6 or GLM 5.2 substitute. Independent developer Max Weinbach posted that Muse Spark 1.1 "fills the gap" that Kimi K2.6 or GLM 5.2 would for him — "good enough for most things, pretty quick, very cheap (more efficient than those models)."

Cheap game and prototype generation. AI/ML API ran a three-prompt comparative test (Fruit Ninja clone, Angry Birds physics, Crossy Road clone) across Grok 4.5, GPT Sol, and Muse Spark 1.1. Muse Spark 1.1 was the cheapest of the three at $1.08 per run versus $1.63 for GPT Sol and $2.47 for Grok 4.5, though its Crossy Road output "lagged badly," suggesting the cost advantage does not translate to every prompt shape.

How Muse Spark 1.1 Compares

Independent benchmarks give the sharpest read on where Muse Spark 1.1 sits in the frontier. Two datasets from Artificial Analysis anchor most current comparisons.

Model	AA Intelligence Index	AA Coding Agent Index	Cost per Task
Claude Fable 5 (with fallback)	60	—	$2.75
GPT-5.6 Sol (max)	59	—	$1.04
Grok 4.5 (high)	54	—	$0.31
GLM-5.2 (max)	51	—	$0.38
Muse Spark 1.1 (xhigh)	51	69	~$0.26 (Intelligence Index) / ~$1.4 (Coding Agent)
Gemini 3.5 Flash	50	—	$0.59
GPT-5.5 (medium, Codex harness)	—	71	Not published
Claude Opus 4.8 (medium, Claude Code)	—	67	Not published
Kimi K2.6	44	—	Not published

Artificial Analysis summarizes the placement bluntly: "Muse Spark 1.1 (xhigh) is amongst the leading models in intelligence and well priced when comparing to other models of similar price." On the Coding Agent Index in the Opencode harness, the group says the model "offers a strong blend of performance and near frontier cost-efficiency."

Source: @ArtificialAnlys

Community observer Teortaxes added that Muse Spark 1.1 is "surprisingly close to Grok 4.5 on many high-signal evals" and flagged the model as "the current top on CritPT" — a claim from an individual analyst that has not been independently verified.

Availability: How to Access Muse Spark 1.1

There are three access paths as of publication, and all are live.

Consumer surfaces. Muse Spark 1.1 powers the "Thinking" mode inside the Meta AI mobile app and at meta.ai. There is no separate consumer subscription tier announced for it.

Meta Model API (public preview). Developers can request API keys at dev.meta.ai. Regional availability is limited — community reports note the API is "not available in all regions yet." New accounts receive $20 in free credits. Documentation lives at developer.meta.com under the Llama blog, at the "Build with Muse Spark" page.

Third-party harnesses and hosts. OpenCode added Muse Spark 1.1 as a first-party model on release day. Vercel is also serving the model, per community posts on r/opencodeCLI. Artificial Analysis benchmarks the xhigh reasoning variant through the Opencode harness.

There is no weights release. Muse Spark 1.1 cannot be run locally, self-hosted, or fine-tuned by end users. That is a hard break from Meta's Llama-era posture and is the single largest structural change for developers evaluating the model. For a fuller unpack of the release mechanics, see our Muse Spark 1.1 deep dive and our earlier coverage of Meta's Muse Image and Muse Video releases.

What We Don't Know Yet

Meta has released a launch post and an evaluation report, but several material facts remain unpublished or contested.

• Parameter count and architecture. Meta has not disclosed model size, whether Muse Spark 1.1 is dense or mixture-of-experts, or its training compute budget.
• Rate limits, latency SLAs, and enterprise pricing. The public rate card covers per-token pricing but not concurrency limits, tiered pricing, or enterprise contract terms.
• Full regional rollout schedule. The API is gated by region at launch; Meta has not published a target date for global availability.
• Terminal-Bench methodology dispute. A Hacker News commenter noted that Meta's Terminal-Bench-2.1 evaluation used resource caps of 6 CPU cores and 8 GB RAM, higher than the official per-task limits on most tasks. The observation is unresolved and Muse Spark 1.1 does not currently appear on the official Terminal-Bench leaderboard.
• Long-horizon agentic performance ceiling. DataCamp's early write-up flags long-horizon agentic work as a weakness relative to GPT-5.5 and Claude Opus 4.8 — a claim consistent with the mixed multi-step results Box reported, but not yet quantified in a standard public benchmark.
• Next-generation roadmap. A community post on r/singularity mentions a larger variant codenamed "Watermelon" said to be arriving; this is community rumor, unconfirmed by Meta.

This section will be updated as Meta and independent evaluators publish more.

Frequently Asked Questions

What is Muse Spark 1.1?

Muse Spark 1.1 is a proprietary multimodal reasoning model developed by Meta Superintelligence Labs and released on July 9, 2026. It is built for agentic tasks such as tool use, computer use, coding, and multimodal understanding, and ships with a 1-million-token context window. It runs in "Thinking" mode inside the Meta AI app and is available to developers through the Meta Model API public preview.

Who makes Muse Spark 1.1?

Muse Spark 1.1 is made by Meta Superintelligence Labs, the same group behind the original Muse Spark and the Muse Image generative model that shipped two days before it. Mark Zuckerberg announced the release on Threads on July 9, 2026, calling it "a strong agentic and coding model at a very low price."

How much does Muse Spark 1.1 cost?

Muse Spark 1.1 costs $1.25 per million input tokens and $4.25 per million output tokens through the Meta Model API, with a cache-hit input price of $0.15 per million tokens. New developers receive $20 in free credits. Artificial Analysis estimates the weighted cost per Intelligence Index task at approximately $0.26.

Is Muse Spark 1.1 open source?

No, Muse Spark 1.1 is not open source. Unlike Meta's open-weight Llama family, Muse Spark 1.1 is proprietary and closed-weight, available only through the Meta Model API and consumer Meta AI surfaces. There is no local deployment path, no weights release, and no community fine-tuning.

What is the context window of Muse Spark 1.1?

Muse Spark 1.1 has a 1-million-token context window. Meta says the model actively manages this context by compacting earlier work while preserving the critical steps needed for later stages of long-running agentic tasks. That is roughly 1,573 A4 pages of 12-point Arial text, per Artificial Analysis's conversion.

How does Muse Spark 1.1 compare to GPT-5.5 and Claude Opus 4.8?

Muse Spark 1.1 (xhigh) scores 69 on the Artificial Analysis Coding Agent Index in the Opencode harness. That lands just below GPT-5.5 (medium) at 71 in the Codex harness and above Claude Opus 4.8 (medium) at 67 in Claude Code. On the broader Artificial Analysis Intelligence Index, Muse Spark 1.1 scores 51, versus 59 for GPT-5.6 Sol and 54 for Grok 4.5.

How do I access Muse Spark 1.1?

Muse Spark 1.1 is available in "Thinking" mode inside the Meta AI mobile app and at meta.ai for consumers, and through the Meta Model API public preview for developers who request access at dev.meta.ai. OpenCode and Vercel also serve the model to developers. API availability is currently limited by region.

What to Watch Next

Three signals will define whether Muse Spark 1.1 sticks as a frontier-tier default. First, the Terminal-Bench methodology dispute — whether Meta rebenchmarks under the standard resource caps and appears on the official leaderboard. Second, the arrival of the rumored larger "Watermelon" variant and its pricing curve relative to this release. Third, real-world cost-per-finished-task numbers from teams running Muse Spark 1.1 in production coding and agent harnesses over the next 90 days, since the API pricing advantage only matters if agent step counts stay reasonable.

Building similar agentic coding or long-context workflows? On kie.ai you can try GPT-5.6, Claude Opus 4.8, and Grok 4.5.