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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. Muse Spark 1.3MetaRemove
  3. GPT-4o Audio PreviewOpenAIRemove
glm-5.3 vs muse-spark-1.3 vs gpt-4o-audio-preview
AttributeGLM 5.3glm-5.3Muse Spark 1.3muse-spark-1.3GPT-4o Audio Previewgpt-4o-audio-preview
Pricing
Input$1.40 / 1M$1.25 / 1M$0.875 / 1M
Output$4.40 / 1M$4.25 / 1M$3.50 / 1M
Cache Write (5m)$1.40 / 1M$1.25 / 1MNot applicable
Cache Write (1h)$1.40 / 1M$1.25 / 1MNot applicable
Cache Read$1.40 / 1M$1.25 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M128K
Max outputN/AN/AN/A
Capabilities
VisionNoYesNo
Function CallingYesYesNo
JSON ModeYesYesNo
StreamingYesYesYes
Catalogue
ProviderZ.AIMetaOpenAI
Categorychatchatvoice
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.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.gpt-4o-audio-preview adds support for audio inputs, allowing the model to understand nuances in audio recordings and enrich responses. It currently does not generate audio outputs, and audio input is billed per million audio tokens.