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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. Pixtral Large 2411Mistral AIRemove
  3. GLM 5.3 FlashZ.AIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

glm-5.3 vs pixtral-large-2411 vs glm-5.3-flash vs muse-spark-1.3
AttributeGLM 5.3glm-5.3Pixtral Large 2411pixtral-large-2411GLM 5.3 Flashglm-5.3-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$1.40 / 1M$2.00 / 1M$0.075 / 1M$1.25 / 1M
Output$4.40 / 1M$6.00 / 1M$0.25 / 1M$4.25 / 1M
Cache Write (5m)$1.40 / 1M$2.00 / 1M$0.075 / 1M$1.25 / 1M
Cache Write (1h)$1.40 / 1M$2.00 / 1M$0.075 / 1M$1.25 / 1M
Cache Read$1.40 / 1M$2.00 / 1M$0.075 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M131.1K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderZ.AIMistral AIZ.AIMeta
Categorychatchatchatchat
Charge typePay As You GoPay 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.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.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.