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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. GLM 5.3 FlashZ.AIRemove
  2. Qwen3.8 27BAlibabaRemove
  3. ERNIE Speed 128KBaiduRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

glm-5.3-flash vs qwen3.8-27b vs ERNIE-Speed-128K vs muse-spark-1.3
AttributeGLM 5.3 Flashglm-5.3-flashQwen3.8 27Bqwen3.8-27bERNIE Speed 128KERNIE-Speed-128KMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.075 / 1M$0.45 / 1M$1.46 / 1M$1.25 / 1M
Output$0.25 / 1M$3.20 / 1M$2.92 / 1M$4.25 / 1M
Cache Write (5m)$0.075 / 1M$0.45 / 1M$1.457 / 1M$1.25 / 1M
Cache Write (1h)$0.075 / 1M$0.45 / 1M$1.457 / 1M$1.25 / 1M
Cache Read$0.075 / 1M$0.45 / 1M$1.457 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M262K131.1K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesNoYes
JSON ModeYesYesNoYes
StreamingYesYesNoYes
Catalogue
ProviderZ.AIAlibabaBaiduMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.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.