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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. Devstral SmallMistral AIRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. Muse Spark 1.3MetaRemove
devstral-small vs glm-5.3-flash vs muse-spark-1.3
AttributeDevstral Smalldevstral-smallGLM 5.3 Flashglm-5.3-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.09 / 1M$0.075 / 1M$1.25 / 1M
Output$0.30 / 1M$0.25 / 1M$4.25 / 1M
Cache Write (5m)$0.09 / 1M$0.075 / 1M$1.25 / 1M
Cache Write (1h)$0.09 / 1M$0.075 / 1M$1.25 / 1M
Cache Read$0.09 / 1M$0.075 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context131.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderMistral AIZ.AIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
Summary—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.