With Llama, Meta marked itself as an open alternative to OpenAI. With its new closed models, Meta now positions itself as a low-cost, high-value competitor.
What's new: Meta launched Muse Spark 1.1, a vision-language model trained for agentic tasks, and opened Meta Model API, the company's first paid access to its models.
- Input/output: Text, images, video in (up to 1,048,576 tokens), text out (up to 131,072 tokens, 119 tokens per second)
- Features: Tool use, prompt caching, adjustable reasoning (none, minimal, low, medium, high, xhigh)
- Performance: Tops tool use leaderboards MCP Atlas (Scale AI) and JobBench, tied with GPT-5.6 Luna and GLM-5.2 on Artificial Analysis' Intelligence Index at low cost per task
- Availability/price: Free via Meta AI app and meta.ai, API access via Meta Model API (public preview, United States only, waitlist, $20 worth of initial credits) $1.25/$0.15/$4.25 per million input/cached/output tokens, websearch $2.50 per 1,000 queries
- Undisclosed: Parameter count, architecture, training data and methods
How it works: Meta disclosed few details about how it built Muse Spark 1.1, which updates the initial Muse Spark, the model behind Meta AI since April. The company's training techniques emphasize managing context, operating computers, and coordinating other agents.
- Meta trained the model to work within multiple agentic coding harnesses, programs that connect a model to files, tools, and tests, supporting features like planning and subagent orchestration.
- The company trained the model for both directions of delegation. Leading a task, it divides the work and farms pieces out to subagents that run simultaneously, which cuts total completion time. Working under another agent, it keeps to the role it was given and returns control to the orchestration model when a decision would exceed its permissions.
- Meta says the model itself, rather than surrounding software, adjusts its input context over long jobs, retrieving details from earlier in a task and condensing exchanges while retaining what later steps will require.
- For computer use, Meta trained the model to choose between automating (writing scripts) and acting directly (clicking or entering text on a virtual keyboard), optimizing for whichever technique is faster or simpler. It also issues several actions at once rather than one action per turn, resulting in better speed and performance.
Performance: Muse Spark 1.1 stands out on benchmarks measuring agentic tool use, but is a tier below the top models in overall intelligence. It ranks just behind the leaders in blind human comparisons, but sits in the middle of the pack in agentic coding. It is particularly good at keeping costs per task low.
- On Artificial Analysis' Intelligence Index, a composite of nine evaluations of economically useful tasks, Muse Spark 1.1 set to xhigh reasoning achieved 51 points, tying Z.ai's GLM-5.2 and GPT-5.6 Luna (both set to max reasoning) and trailing Claude Sonnet 5 set to max reasoning (53 points). Muse Spark 1.1 cost $0.26 per Intelligence Index task, less than any other model at or above its score except GPT-5.6 Luna ($0.21 per task).
- On Arena.ai's Text Arena, which ranks models by blind head-to-head human comparisons, Muse Spark 1.1 ranked sixth (1,490 Elo), behind Claude Fable 5 (1,505 Elo) and four Claude Opus versions, and just ahead of GPT-5.6 Sol set to xhigh reasoning (1,486 Elo).
- On Artificial Analysis' Coding Agent Index, Muse Spark 1.1 set to xhigh reasoning achieved 71.3 points, behind GPT-5.6 Luna set to max reasoning (71.4) and ahead of Gemini 3.5 Flash set to medium reasoning (70.1). It cost roughly $1.40 per task, among the lowest of the coding agents compared, though it takes more time per task than others.
- On MCP Atlas, Scale AI's leaderboard for multi-step use of software tools via Model Context Protocol servers, Muse Spark 1.1 (88.1 percent pass rate) ranked ahead of Gemini-3.5-Flash set to high reasoning (83.6 percent) and Claude Fable 5 (83.3 percent). On JobBench, a test of professional tasks across white-collar occupations, Muse Spark 1.1 (54.7 percent) ranks second, ahead of Claude Opus 4.8 (48.4 percent) and behind Claude Fable 5 (57.4 percent).
Behind the news: Muse Spark 1.1 arrived in a crowded stretch of model launches. On the same day, OpenAI opened its GPT-5.6 family to the public; one day before, Grok 4.5 launched with a similar emphasis on low cost per task. (Two weeks later, Google unveiled Gemini 3.6 Flash and Gemini 3.5 Flash-Lite.) The same week, Meta also introduced the image generator Muse Image and announced the video generator Muse Video. Muse Spark 1.1 is also the first model available via Meta’s new Model API.
Why it matters: Token prices gate which applications are economical at scale; with Muse Spark 1.1, Meta moved the gate. The model's output tokens cost a fraction of near rivals' ($25, $30, and $50 per million tokens for Claude Opus 4.8, GPT-5.6 Sol, and Claude Fable 5 respectively), and per-task measurement confirms the discount is real: On Artificial Analysis' numbers, only one comparably intelligent model, GPT-5.6 Luna, costs less to run. Mark Zuckerberg framed the pricing as an attack on competitors’ economics, saying that other labs’ pricing “is very extreme and has very high margins.” Meta, like Google, can subsidize model development, training, and inference costs with advertising revenue, a structural edge over labs that live on API margins and subscriptions. If rivals feel forced to defend market share, the cost of running agents falls across the industry.
We're thinking: Capabilities keep migrating from scaffolding into weights. Instruction following began as prompt engineering, and tool use began as code wrapped around models; both became training objectives. Muse Spark 1.1 continues the pattern by absorbing behaviors that developers previously built into scaffolding like delegating and escalating to agents and managing context mid-task. The less external tooling developers must build by hand, the faster agentic applications will spread.