Meta's Muse Spark 1.1 scores 51 on the Artificial Analysis Intelligence Index and is cost and token efficient compared to its peers

Muse Spark 1.1 (xhigh) improves 8 points over Muse Spark 1.0 (43) in three months. It is effectively tied with GLM-5.2 (max), GPT-5.4 (xhigh), and GPT-5.6 Luna (max) at 51, three points behind Grok 4.5 (high, 54), with the leading edge at Claude Fable 5 (60), GPT-5.6 Sol (max, 59), and Claude Opus 4.8 (max, 56).

The gains concentrate in Scientific Reasoning, coding, and knowledge; agentic knowledge work lags on GDPval-AA v2.

@AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release!

Key Takeaways:
➤ Muse Spark 1.1 gains substantially on the first Muse Spark release. This was driven in particular by gains in agentic knowledge work (GDPval-AA v2) and coding (SciCode, TerminalBench). On Humanity's Last Exam, it reaches 45%, within a point of Claude Opus 4.8 (max, 46%) and ahead of GPT-5.5 (44%) and Grok 4.5 (high, 40%)
➤ The most token-efficient of the models effectively tied at 51 and among the cheaper models to run. Muse Spark 1.1 used 94M output tokens to run the Intelligence Index, fewer than GPT-5.4 (xhigh, 109M), GPT-5.6 Luna (max, 125M), and GLM-5.2 (max, 141M). We estimate ~$0.26 per Intelligence Index task at Meta's $1.25/$4.25 pricing - below GLM-5.2 ($0.37) and roughly 3x below GPT-5.4 ($0.89)
➤ The AA-Omniscience gain is driven by abstention rather than accuracy. The score more than quadrupled from 4 to 18 as the hallucination rate fell 35 points (73% to 38%), with the attempt rate down from 95% to 82% and accuracy roughly flat (45% to 41%)

Other model details:
➤ Context window: 1M tokens, up from 262k for Muse Spark 1.0
➤ Pricing: $1.25/$4.25 per 1M input/output tokens; cache hits discounted to $0.15 per 1M
➤ Output speed: ~114 tokens/s median on Meta's first-party API, with a ~21s time to first answer token
➤ Availability: Meta's first-party API at launch