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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Muse Spark OpenMetaRemove
  2. Universal-3.5 ProAssemblyAIRemove
  3. Muse Glimmer 30BMetaRemove
muse-spark-open vs universal-3-5-pro vs muse-glimmer-30b
AttributeMuse Spark Openmuse-spark-openUniversal-3.5 Prouniversal-3-5-proMuse Glimmer 30Bmuse-glimmer-30b
Pricing
Input$1.25 / 1M— Not priced per input token$0.35 / 1M
Output$4.25 / 1M— Not priced per output token$1.50 / 1M
Cache Write (5m)$1.25 / 1MNot applicable$0.35 / 1M
Cache Write (1h)$1.25 / 1MNot applicable$0.35 / 1M
Cache Read$1.25 / 1MNot applicable$0.35 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A131K
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderMetaAssemblyAIMeta
Categorychatvoicechat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-09-22—
Description
SummaryMuse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It combines strong multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across 100+ languages. Designed for long-horizon agentic and coding workflows, Muse Glimmer 30B offers a practical balance of capability and deployment efficiency, making it well suited for local coding assistants, multimodal agents, and production workflows that require sustained autonomous execution.