Muse Spark 1.1

Original Article ↗ Hacker News Discussion ↗

Pricing and Economic Positioning

  • Many see Muse Spark 1.1 pricing as aggressively low vs other “frontier” models (e.g., Grok 4.5, Claude, GLM 5.2), especially cached input at $0.15/Mtok.
  • Others argue it’s still expensive compared to Chinese models and open-weight options; some feel $10+ per 1M output tokens is hard to justify for consumers.
  • Several comments frame this as a response to competitive pressure from Chinese labs and DeepSeek/GLM pricing.
  • For professional use (coding, contracts), higher prices are seen as acceptable relative to human labor; for entertainment, less so.

Model Quality and Benchmarks

  • Early testers report quality “below Sonnet” and not impressive on DeepSWE; some say it trades blows with GPT 5.5 / Opus 4.8 on Meta’s own charts.
  • Tool calling and terminal/agent performance are highlighted as strengths; coding and multimodal are “pretty good,” with debate on how much high tool-call success matters in practice.
  • Significant controversy over Terminal-Bench 2.1 results: critics say Meta exceeded CPU/RAM limits, effectively “cheating”; defenders argue resource limits are recommendations, impact may be small, and infra issues complicate strict adherence.
  • Broader skepticism toward “trust-me” benchmarks and selective metric reporting; calls for independent evaluation.

Trust, Privacy, and Data Retention

  • Multiple commenters say they won’t use Meta due to past privacy issues and lack of explicit data-retention policy for the paid API.
  • Some note that not all LLM use involves personal data, but unease remains, especially vs open-weight models.

Open vs Closed Models

  • Disappointment that Muse is not open-weights; seen as a strategic shift away from Meta’s earlier open-source leadership.
  • Several argue that releasing strong open-weight models (and even training data, though viewed as legally risky) would let Meta “commoditize” frontier models and pressure competitors’ revenue.

Ecosystem, Access, and Competition

  • Model is not yet on OpenRouter, making evaluation harder; people want a single-key aggregator to test it.
  • Region restrictions (e.g., Canada, Argentina, Vietnam) frustrate developers.
  • Overall sentiment: more competition (Meta, xAI, Chinese labs) is good for prices and diversity, but many remain wary of Meta’s brand and the closed nature of the release.