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Compare models

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

  1. o4 Mini HighOpenAIRemove
  2. Muse Spark 1.3MetaRemove
  3. GLM 5.3 FlashZ.AIRemove
o4-mini-high vs muse-spark-1.3 vs glm-5.3-flash
Attributeo4 Mini Higho4-mini-highMuse Spark 1.3muse-spark-1.3GLM 5.3 Flashglm-5.3-flash
Pricing
Input$1.10 / 1M$1.25 / 1M$0.075 / 1M
Output$4.40 / 1M$4.25 / 1M$0.25 / 1M
Cache Write (5m)$1.10 / 1M$1.25 / 1M$0.075 / 1M
Cache Write (1h)$1.10 / 1M$1.25 / 1M$0.075 / 1M
Cache Read$1.10 / 1M$1.25 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context200K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIMetaZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
Summary—Muse 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.