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. Muse Spark 1.3MetaRemove
  2. Gemini 3.5 TranscribeGoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
muse-spark-1.3 vs gemini-3.5-transcribe vs glm-5.3-flash
AttributeMuse Spark 1.3muse-spark-1.3Gemini 3.5 Transcribegemini-3.5-transcribeGLM 5.3 Flashglm-5.3-flash
Pricing
Input$1.25 / 1M— Not priced per input token$0.075 / 1M
Output$4.25 / 1M— Not priced per output token$0.25 / 1M
Cache Write (5m)$1.25 / 1MNot applicable$0.075 / 1M
Cache Write (1h)$1.25 / 1MNot applicable$0.075 / 1M
Cache Read$1.25 / 1MNot applicable$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M98.3K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderMetaGoogleZ.AI
Categorychatvoicechat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-09-25—
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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.