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Meta's Muse Spark Open and Qwen3.8-Flash-Next benchmark scores revealed

By PulseAugur Editorial · [1 sources] ·

A comparison of open-weight large language models reveals performance metrics against their size. Meta's 816B Muse Spark Open model achieved a score of 39 on the Artificial Analysis benchmark, while the 180B Qwen3.8-Flash-Next model scored 40. The analysis also noted that the MiMo-V2.6-Pro (1T parameters) model achieved the highest score of 46, followed by DeepSeek V4.1 Flash 1017 (552B) and GLM-5.3 (753B) at 45. AI

IMPACT Provides comparative performance data for open-weight LLMs, aiding researchers and developers in model selection.

RANK_REASON The cluster discusses benchmark scores for open-weight large language models, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Meta's Muse Spark Open and Qwen3.8-Flash-Next benchmark scores revealed

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The cluster discusses benchmark scores for open-weight large language models, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/BTA_Labs ·

    Meta's 816B Muse Spark Open scores 39 on Artificial Analysis, the 180B Qwen3.8-Flash-Next scores 40. Score vs size for 11 open-weight models

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xcthj8/metas_816b_muse_spark_open_scores_39_on/"> <img alt="Meta's 816B Muse Spark Open scores 39 on Artificial Analysis, the 180B Qwen3.8-Flash-Next scores 40. Score vs size for 11 open-weight models" src="h…

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