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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. Perceptron Mk1.5PerceptronRemove
  2. Muse Spark OpenMetaRemove
  3. Muse Glimmer 30BMetaRemove
perceptron-mk1.5 vs muse-spark-open vs muse-glimmer-30b
AttributePerceptron Mk1.5perceptron-mk1.5Muse Spark Openmuse-spark-openMuse Glimmer 30Bmuse-glimmer-30b
Pricing
Input$0.15 / 1M$1.25 / 1M$0.35 / 1M
Output$1.50 / 1M$4.25 / 1M$1.50 / 1M
Cache Write (5m)$0.15 / 1M$1.25 / 1M$0.35 / 1M
Cache Write (1h)$0.15 / 1M$1.25 / 1M$0.35 / 1M
Cache Read$0.15 / 1M$1.25 / 1M$0.35 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K1M131K
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingYesYesYes
Catalogue
ProviderPerceptronMetaMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Muse 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.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.