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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. Muse Spark 1.3MetaRemove
  2. QwQ 32BAlibabaRemove
  3. GPT-6 AstraOpenAIRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

muse-spark-1.3 vs qwq-32b vs gpt-6-astra vs glm-5.3
AttributeMuse Spark 1.3muse-spark-1.3QwQ 32Bqwq-32bGPT-6 Astragpt-6-astraGLM 5.3glm-5.3
Pricing
Input$1.25 / 1M$0.29 / 1M$10.00 / 1M$1.40 / 1M
Output$4.25 / 1M$0.39 / 1M$50.00 / 1M$4.40 / 1M
Cache Write (5m)$1.25 / 1M$0.29 / 1M$10.00 / 1M$1.40 / 1M
Cache Write (1h)$1.25 / 1M$0.29 / 1M$10.00 / 1M$1.40 / 1M
Cache Read$1.25 / 1M$0.29 / 1M$10.00 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M41.0K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesNo
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderMetaAlibabaOpenAIZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryMuse 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-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.