Leaderboard · #8 of 12 · Meta · Workhorse

Muse Spark 1.3

The fastest thing in the table by a distance, and the one to beat on the stress benchmark nobody else talks about.

Updated Oct 10, 2026 · kept by WrenJSON

Key facts

Score
72 / 100, rank #8
Price
$0.78 per 1M blended (Artificial Analysis estimate)
Blended price
$0.78 per 1M tokens (3 input : 1 output), estimated
Context window
Not verified
Open weights
Not verified
Best for
Latency-first products, Customer-facing chat, Meta-ecosystem teams

Meta does not publish a simple list price for Muse Spark 1.3; Artificial Analysis estimates a blended $0.78 per million tokens (three input to one output).

Independent evals

BenchmarkResultWho ran itRead
Artificial Analysis Intelligence Index (max)48Artificial Analysis LLM leaderboardOct 9, 2026
Arena text score (max)1494Arena text leaderboard (Oct 8, 2026)Oct 9, 2026
DistressBench (Scale)84.88Scale SEAL leaderboardsOct 9, 2026
  • Artificial Analysis output speed (max): 175 tokens/s (Artificial Analysis LLM leaderboard)
  • Artificial Analysis blended price (max): $0.78 per 1M (Artificial Analysis LLM leaderboard)

Questions people ask

How much does Muse Spark 1.3 cost?

Meta does not publish a simple list price for Muse Spark 1.3; Artificial Analysis estimates a blended $0.78 per million tokens (three input to one output).

How good is Muse Spark 1.3?

Muse Spark 1.3 ranks #8 of 12 on the Models at Work leaderboard with a score of 72 out of 100 (October 2026 edition). The fastest thing in the table by a distance, and the one to beat on the stress benchmark nobody else talks about.

What is Muse Spark 1.3 best for?

Muse Spark 1.3 is best for latency-first products, customer-facing chat, meta-ecosystem teams.

What are Muse Spark 1.3's benchmark scores?

Artificial Analysis Intelligence Index (max): 48 (Artificial Analysis LLM leaderboard); Arena text score (max): 1494 (Arena text leaderboard (Oct 8, 2026)); DistressBench (Scale): 84.88 (Scale SEAL leaderboards).

Compare Muse Spark 1.3

Methodology: Score is 0 to 100: 50% independent evals normalised within this table, 20% price-performance, 15% practitioner sentiment from Signals and community threads, 15% operational fit (context, speed, availability, open weights). Models within three points are a tie in practice. Vendor-published figures are labelled as such and never counted as independent.