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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. GPT-6 Astra ProOpenAIRemove
  2. Qwen TurboAlibabaRemove
  3. Muse Spark 1.3MetaRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

gpt-6-astra-pro vs qwen-turbo vs muse-spark-1.3 vs glm-5.3
AttributeGPT-6 Astra Progpt-6-astra-proQwen Turboqwen-turboMuse Spark 1.3muse-spark-1.3GLM 5.3glm-5.3
Pricing
Input$10.00 / 1M$0.05 / 1M$1.25 / 1M$1.40 / 1M
Output$50.00 / 1M$0.20 / 1M$4.25 / 1M$4.40 / 1M
Cache Write (5m)$10.00 / 1M$0.05 / 1M$1.25 / 1M$1.40 / 1M
Cache Write (1h)$10.00 / 1M$0.05 / 1M$1.25 / 1M$1.40 / 1M
Cache Read$10.00 / 1M$0.05 / 1M$1.25 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesNo
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderOpenAIAlibabaMetaZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay 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.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.GLM-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.