I’ve recently received a request to put Meta’s new Muse Spark 1.1 model through the agentic game creation test, to see how it would perform. If I am remembering right, Muse Spark 1.1 was released recently, in early July.
Meta’s blog post states “Muse Spark 1.1 is a multimodal reasoning model built for agentic tasks, with major gains in tool and computer use, coding, and multimodal understanding.”
Among other things (coding for one), Muse Spark 1.1 was trained specifically for orchestration and delegation to sub-agents. The blog states: “[Muse Spark 1.1] tackles complex projects significantly faster than Muse Spark, as it is trained to orchestrate multi-agent systems to optimize end-to-end latency. As the main agent, it can gather context, make a plan, and delegate execution across parallel subagents. As a subagent, it adheres to its job, understands available tools, and knows when to escalate back to the main agent.”
The blog post also states that “[Muse Spark 1.1] zero-shot generalizes to new native tools, MCP servers, and custom skills.”
Did someone say zero-shot? Have I got a prompt for you, Muse Spark 1.1…
A reminder, my one-shot autonomous agent game generation prompt is an offshoot of my older, explicit orchestrator/sub-agent framework, and a model that can do the orchestration itself and delegate to sub-agents should do well at the task.
So, I tried it out…and it did quite well! Using Muse Spark 1.1 served from Open Router, and in the Visual Studio Code GitHub Copilot plugin, it designed, coded, and tested a nice and competent game. Its token cost was among the more reasonable, only about $25, significantly less than Opus and Fable, and more on par with Grok 4.5.
An interesting side note: it spent a significant amount of time and tokens on what seemed to be the initial implementation and feature design; however, once the design was set, it seemed to speed-run the implementation of the 25 new features. My guess is that the features were all being orchestrated and implemented in parallel (when possible) by subagents the LLM spooled up; however, this is just a guess.
Another side note: this was probably the most context heavy run of all the models. Most of the models finished the task with a context in the mid to high 200K token range; Muse Spark 1.1 finished with a context of 437K tokens, significantly more than most of the other models. I’d be interested in looking into why this might be.
All in all, for this autonomous workload, I would put Muse Spark 1.1 in the same (subjective) neighborhood as Grok 4.5, GPT-5.5 and GPT-5.6 Sol, Sonnet 5, and Kimi K3.
You can play the Muse Spark 1.1 generated game here, in your web browser; just look for the Muse Spark 1.1 game card: https://rgm-llm-asteroids-arcade.netlify.app
As always, the controls are <c> to insert your coin, <1> to start a 1 player game, arrow keys for turning and thrusting, <space> to fire, and <h> for hyperspace.
Meta AI’s blog post announcing Muse Spark 1.1 : https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/
