Skip to content

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. Gemini 3.8 FlashGoogleRemove
  3. Muse Spark 1.3MetaRemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

glm-5.3-flash vs gemini-3.8-flash vs muse-spark-1.3 vs qwen3.8-27b
AttributeGLM 5.3 Flashglm-5.3-flashGemini 3.8 Flashgemini-3.8-flashMuse Spark 1.3muse-spark-1.3Qwen3.8 27Bqwen3.8-27b
Pricing
Input$0.075 / 1M$0.75 / 1M$1.25 / 1M$0.45 / 1M
Output$0.25 / 1M$3.75 / 1M$4.25 / 1M$3.20 / 1M
Cache Write (5m)$0.075 / 1M$0.75 / 1M$1.25 / 1M$0.45 / 1M
Cache Write (1h)$0.075 / 1M$0.75 / 1M$1.25 / 1M$0.45 / 1M
Cache Read$0.075 / 1M$0.75 / 1M$1.25 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderZ.AIGoogleMetaAlibaba
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.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.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.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.