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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. GLM 5.3Z.AIRemove
  2. Grok 4.6xAIRemove
  3. Muse Spark 1.3MetaRemove
glm-5.3 vs grok-4.6 vs muse-spark-1.3
AttributeGLM 5.3glm-5.3Grok 4.6grok-4.6Muse Spark 1.3muse-spark-1.3
Pricing
Input$1.40 / 1M$2.00 / 1M$1.25 / 1M
Output$4.40 / 1M$6.00 / 1M$4.25 / 1M
Cache Write (5m)$1.40 / 1M$2.00 / 1M$1.25 / 1M
Cache Write (1h)$1.40 / 1M$2.00 / 1M$1.25 / 1M
Cache Read$1.40 / 1M$2.00 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M500K1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIxAIMeta
Categorychatchatchat
Charge typePay As You GoPay 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.Grok 4.6 is SpaceXAI's smartest frontier model, delivering top-tier performance across coding, knowledge work, and STEM reasoning. It is designed for demanding technical and professional workloads that require strong problem solving, accurate instruction following, and reliable execution. Optimized for software engineering, scientific analysis, and complex knowledge tasks, Grok 4.6 is well suited for advanced coding, research, and agentic workflows where high capability and reasoning quality are critical.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.