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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 2.5 Flash Image PreviewGoogleRemove
  3. GLM 5.3Z.AIRemove
muse-spark-1.3 vs gemini-2.5-flash-image-preview vs glm-5.3
AttributeMuse Spark 1.3muse-spark-1.3Gemini 2.5 Flash Image Previewgemini-2.5-flash-image-previewGLM 5.3glm-5.3
Pricing
Input$1.25 / 1M$1.40 / 1M
Output$4.25 / 1M$4.40 / 1M
Cache Write (5m)$1.25 / 1MNot applicable$1.40 / 1M
Cache Write (1h)$1.25 / 1MNot applicable$1.40 / 1M
Cache Read$1.25 / 1MNot applicable$1.40 / 1M
Web Search$0 / 1M$0 / 1M
Request$0.075 / request
BillingPay Per Request
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoNo
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesYesYes
Catalogue
ProviderMetaGoogleZ.AI
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released
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.Gemini 2.5 Flash Image Preview (“Nano Banana”) is a cutting-edge image generation model with strong contextual understanding. It can create and edit images and supports multi-turn conversational workflows around visuals.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.