Qwen3.8-Flash-Next edges out Meta's huge Muse Spark Open at a fraction of the size

What this is about

On r/LocalLLaMA, a forum about running language models locally, user BTA_Labs posted a chart comparing 11 open-weight models (models whose weights you can download freely). The X-axis shows total parameters (log scale, as listed by Artificial Analysis, or AA for short). The Y-axis shows the AA Index score. Parameters are, roughly speaking, the knobs a model has learned during training. Models released in October 2026 are marked in orange. The data comes from AA's model pages, after Muse Spark Open and Beam received their scores last week. According to the author, a table with active parameters, licenses and links is in the comments and isn't included here.

The key numbers

The source doesn't name the other three of the 11 models. For mixture-of-experts (MoE) models, "active parameters" means the share of parameters that actually compute for each token. That mainly determines speed, while total size determines memory needs.

Size isn't quality

The standout comparison is Meta's Muse Spark Open against Qwen3.8-Flash-Next. 816B total and 42B active parameters get you 39 points. Qwen, with just 180B total and 6B active, scores 40. Qwen3.8 27B (34 points) also beats Beam at 501B (30) and clearly outscores Nemotron 3.5 Bolt at 74B (21).

What you need to run them locally

The Unsloth quantizations in GGUF format (UD-Q4_K_XL, roughly 4-bit, meaning numbers are stored more coarsely to save memory) weigh about 506 GB for Muse Spark Open and 111 GB for Qwen3.8-Flash-Next. For local use, Meta recommends two M5 Ultra Mac Studios with 512 GB of memory each for Muse Spark Open. For Qwen3.8 27B, Unsloth lists 16 to 19 GB of memory for its 4-bit variants. That's doable on a single high-end GPU or a Mac with enough RAM.

Our take

The chart shows clearly that total size and index score only loosely track each other. For local users, smaller models often offer the better ratio of effort to performance. The author adds a caveat: the index is a single aggregated number from AA's own test runs. It says nothing about speed, long context, image understanding or how a model performs on your own tasks. The post doesn't say whether the results have been independently reproduced. The details on Meta's hardware recommendation and the file sizes come from the Reddit post and haven't been verified here.