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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. Qwen3 14BAlibabaRemove
  2. GPT-6 AstraOpenAIRemove
  3. GLM 5.3 FlashZ.AIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

qwen3-14b vs gpt-6-astra vs glm-5.3-flash vs muse-spark-1.3
AttributeQwen3 14Bqwen3-14bGPT-6 Astragpt-6-astraGLM 5.3 Flashglm-5.3-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.08 / 1M$10.00 / 1M$0.075 / 1M$1.25 / 1M
Output$0.24 / 1M$50.00 / 1M$0.25 / 1M$4.25 / 1M
Cache Write (5m)$0.08 / 1M$10.00 / 1M$0.075 / 1M$1.25 / 1M
Cache Write (1h)$0.08 / 1M$10.00 / 1M$0.075 / 1M$1.25 / 1M
Cache Read$0.08 / 1M$10.00 / 1M$0.075 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context41.0K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderAlibabaOpenAIZ.AIMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-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-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.