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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 OpenMetaRemove
  2. Gemini 3.5 TranscribeGoogleRemove
  3. Muse WonderMetaRemove
  4. GLM 5.3 FlashZ.AIRemove

4 is the maximum. Remove one to add another.

muse-spark-open vs gemini-3.5-transcribe vs muse-wonder vs glm-5.3-flash
AttributeMuse Spark Openmuse-spark-openGemini 3.5 Transcribegemini-3.5-transcribeMuse Wondermuse-wonderGLM 5.3 Flashglm-5.3-flash
Pricing
Input$1.25 / 1M— Not priced per input token$4.00 / 1M$0.075 / 1M
Output$4.25 / 1M— Not priced per output token$20.00 / 1M$0.25 / 1M
Cache Write (5m)$1.25 / 1MNot applicable$4.00 / 1M$0.075 / 1M
Cache Write (1h)$1.25 / 1MNot applicable$4.00 / 1M$0.075 / 1M
Cache Read$1.25 / 1MNot applicable$4.00 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M98.3K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderMetaGoogleMetaZ.AI
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-25——
Description
SummaryMuse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.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.