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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. Veo 3GoogleRemove
  2. GPT-6 Astra ProOpenAIRemove
  3. GLM 5.3 FlashZ.AIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

veo3 vs gpt-6-astra-pro vs glm-5.3-flash vs muse-spark-1.3
AttributeVeo 3veo3GPT-6 Astra Progpt-6-astra-proGLM 5.3 Flashglm-5.3-flashMuse Spark 1.3muse-spark-1.3
Pricing
Request$1.26 / request
BillingPay Per Request
Cache Write (5m)Not applicable$10.00 / 1M$0.075 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$10.00 / 1M$0.075 / 1M$1.25 / 1M
Cache ReadNot applicable$10.00 / 1M$0.075 / 1M$1.25 / 1M
Input$10.00 / 1M$0.075 / 1M$1.25 / 1M
Output$50.00 / 1M$0.25 / 1M$4.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderGoogleOpenAIZ.AIMeta
Categoryvideochatchatchat
Charge typePay Per RequestPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.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.