Today many teams run their own LLM stack with open weights or pay‑per‑token APIs that charge high rates for reasoning. Muse Spark 1.1 changes the equation by offering a hosted, multimodal reasoning model through the Meta Model API. The model thinks before it answers, handles text, image, video and document input, and can call tools in parallel. Its context window holds over one million tokens and it actively compacts that window while delegating work to sub‑agents.
Pricing is straightforward: $1.25 per million input tokens and $4.25 per million output tokens. New accounts receive $20 in free credits, enough for a few hundred agent runs. Because the API is OpenAI‑compatible, moving to Muse Spark 1.1 requires only a base‑URL change and swapping the model ID—no rewrite of existing client code.
Typical workloads where it shines include automating marketplace listings from smartphone video, debugging web apps by tracing screenshots back to source code, and adjusting plans mid‑workflow when new information arrives. Coding assistants also benefit from built‑in planning mode, goal conditioning and subagent delegation.
If your job is primarily tool orchestration, needs long context, or is sensitive to per‑token cost, Muse Spark 1.1 is a strong candidate. Run a pilot on the free credit, measure cost per completed run, and compare it to your current default. Keep the model for accuracy‑critical coding or non‑US deployments only after the preview expands beyond the United States.
Start by setting the base URL to https://api.meta.ai/v1, providing your MODEL_API_KEY, and calling the chat completions endpoint with model muse-spark-1.1. Evaluate the results, iterate, and scale when the numbers add up.
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