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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. GLM 5.3Z.AIRemove
  2. Muse Spark 1.2MetaRemove
glm-5.3 vs muse-spark-1.2
AttributeGLM 5.3glm-5.3Muse Spark 1.2muse-spark-1.2
Pricing
Input$1.40 / 1M$1.25 / 1M
Output$4.40 / 1M$4.25 / 1M
Cache Write (5m)$1.40 / 1M$1.25 / 1M
Cache Write (1h)$1.40 / 1M$1.25 / 1M
Cache Read$1.40 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M1M
Max outputN/AN/A
Capabilities
VisionNoYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderZ.AIMeta
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryGLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.Muse Spark 1.2 is Meta's multimodal reasoning model designed for complex agentic and software engineering workflows. It supports text, image, video, audio, and PDF inputs with text output, and features a 1M-token context window for sustained reasoning across large, multi-stage tasks. Built for flexible multi-agent execution, Muse Spark 1.2 can serve as either a coordinating main agent or a parallel task-focused subagent. With configurable reasoning effort, structured outputs, parallel function calling, and broad coding-harness compatibility, it is well suited for multi-file refactoring, extended debugging, whole-repository generation, and long-horizon development workflows.