Muse Spark Open Launch

Muse Spark Open, an open-weight model for coding agents Use your OpenAI commitments on Baseten open models . Learn more

AI models

Muse Spark Open, an open-weight model for coding agents

Muse Spark Open is now available on Baseten. This 816B open-weight omnimodal model is optimized for coding agent workflows, with a 1M token context.

Authors

Albert Lee

Pavel Belevich

Marylise Tauzia

Published October 15, 2026

Share

Published October 15, 2026

TL;DR Today, Meta released the weights for Muse Spark Open, an 816B-parameter open-weight omnimodal MoE model co-trained with Muse Code for coding agent workflows. Its compressed MoE block and hybrid global/local attention cut inter-GPU traffic and KV cache memory for long agent sessions. It is the most capable US open-weights model to date. Muse Spark Open is available now on Baseten Model APIs and Dedicated Inference, served in NVFP4.

Meta released the weights for Muse Spark Open, and the open-weight release is available day 0 on Baseten Model APIs and Dedicated Inference. It is the most capable US open-weights model to date. Muse Spark Open has: 816B total parameters with 42.7B active during inference,

a 1M token context window, and

omnimodal input (text, image, video, audio), with text output.

Muse Spark Open was co-trained with Muse Code and is specifically designed for coding agent workflows. Its omnimodal input also enables developer workflows that text-only models can’t. For agents that support image, video, or audio input, you can take advantage of: Screenshot-driven UI debugging: The agent compares a screenshot of the rendered page against a design mockup or bug report image and fixes the layout code directly.

Video reproduction to test case: You record a screen capture of a bug. The agent watches it, extracts the steps, and writes a reproduction script or end-to-end test.

Voice-driven pair programming: You steer the agent by talking and typing at the same time, with audio taken as direct input rather than being transcribed first.

Competitive performance for coding agents In addition to Muse Code, Muse Spark Open can integrate with third-party coding agents such as OpenCode , Pi , and Deep Agents Code . Based on reported benchmark scores from Meta, Muse Spark Open is competitive with models from other frontier labs on agentic coding and knowledge work. On the agentic coding benchmarks, each current model was tested with its own lab's coding agent (Muse Spark with Muse Code, Claude Opus 5 with Claude Code, GPT-5.6 Terra with Codex) rather than a shared harness, so the scores reflect how each model performs in the tooling it was built for.

✕ Meta benchmarked Muse Spark Open against models from frontier labs. The comparison set includes each lab's flagship model, but it excludes the top-tier models above them (Claude Fable 5.1, GPT-6 Astra). Source: https://developer.meta.com/ai/models/muse-spark-1-2/ In routine agent loops (running commands, reading output, fixing what breaks), Muse Spark Open should feel close to the flagship closed models. On large, multi-file changes across many steps, expect it to need more guidance or retries than Claude Opus 5 or GPT-5.6 Terra, while holding up well against Grok 4.5 and Gemini 3.6 Flash. When an agent has to write up its work, it beats every model in the chart except Opus 5. A model architecture designed for agents Coding agent workflows demand both high throughput for iterative reasoning loops and memory efficiency for long context tracking. To address these requirements, Muse Spark Open utilizes specific model architecture designs: A compressed Mixture-of-Experts (MoE) block that boosts throughput by reducing parameter footprint and inter-GPU traffic.

A hybrid global/local attention schedule that shrinks KV-cache memory overhead by 3.7x at long context.

Improved throughput performance with compressed MoE block Coding agents read context, reason, generate, and re-read the result in a tight loop, requiring high throughput from the model. Running background subagents in parallel further pushes that throughput requirement. To boost throughput capacity, Muse Spark Open uses a compressed mixture-of-experts (MoE) block. A shared pooler compresses the input vector before passing it to the experts, whose combined output is projected back to the original dimension by a shared unpooler. The router, which selects experts for each token, still reads the full-width vector. ✕ Compressed MoE adds a shared pooler before the experts and a shared unpooler after the experts' outputs are combined. Operating on this compressed vector: Halves each expert's parameters, shrinking total model size by roughly 800B parameters compared to an uncompressed design.

Frees GPU memory for larger KV caches and concurrent sequences.

Reduces inter-GPU traffic in the MoE layer to directly boost throughput.

Serving long contexts efficiently with global/local attention Coding agents accumulate context on every step as they build up task traces, reasoning, and large parts of a codebase. Every token in that context takes up GPU memory as KV cache, and in a conventional model, every layer keeps its own KV cache. To manage GPU memory usage, Muse Spark Open uses a hybrid attention schedule. Of its 62 layers, there are 46 local attention layers that use a 2048-token sliding window with rotary position embeddings (RoPEs) to track word order. Additionally, there are 16 global attention layers that see the full sequence with no positional encoding (NoPE), relying on the word order information already captured by earlier layers. Conventional architecture vs. hybrid attention schedule ✕ Muse Spark Open interleaves 46 local-attention layers with 16 global-attention layers, reducing KV-cache memory from 31 GiB to 8.4 GiB per sequence at 128K context, a typical agent working set. The savings grow with context length. Each local attention layer only tracks the most recent 2048 tokens, and its stored KV cache data from earlier tokens can be overwritten. Full context from the old tokens isn’t forgotten by the model and is instead preserved in the KV cache of the global attention layers. This design caps the KV cache of local attention layers, reducing KV cache memory usage by roughly 3.7x at 128K token context compared to models with only global attention layers. From architecture to inference optimization A key serving-time cost for agent workloads is the memory footprint of the model weights, which caps...