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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 1.3MetaRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. Perceptron Mk1.5PerceptronRemove
muse-spark-1.3 vs glm-5.3-flash vs perceptron-mk1.5
AttributeMuse Spark 1.3muse-spark-1.3GLM 5.3 Flashglm-5.3-flashPerceptron Mk1.5perceptron-mk1.5
Pricing
Input$1.25 / 1M$0.075 / 1M$0.15 / 1M
Output$4.25 / 1M$0.25 / 1M$1.50 / 1M
Cache Write (5m)$1.25 / 1M$0.075 / 1M$0.15 / 1M
Cache Write (1h)$1.25 / 1M$0.075 / 1M$0.15 / 1M
Cache Read$1.25 / 1M$0.075 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M36.9K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesYesNo
StreamingYesYesYes
Catalogue
ProviderMetaZ.AIPerceptron
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released——2026-09-25
Description
SummaryMuse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.