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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 1.3MetaRemove
  2. Qwen3.8 27BAlibabaRemove
  3. Veo 3 Pro FramesGoogleRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

muse-spark-1.3 vs qwen3.8-27b vs veo3-pro-frames vs glm-5.3
AttributeMuse Spark 1.3muse-spark-1.3Qwen3.8 27Bqwen3.8-27bVeo 3 Pro Framesveo3-pro-framesGLM 5.3glm-5.3
Pricing
Input$1.25 / 1M$0.45 / 1M$1.40 / 1M
Output$4.25 / 1M$3.20 / 1M$4.40 / 1M
Cache Write (5m)$1.25 / 1M$0.45 / 1MNot applicable$1.40 / 1M
Cache Write (1h)$1.25 / 1M$0.45 / 1MNot applicable$1.40 / 1M
Cache Read$1.25 / 1M$0.45 / 1MNot applicable$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Request$5.60 / request
BillingPay Per Request
Context
Max context1M262KN/A1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoNo
Function CallingYesYesNoYes
JSON ModeYesYesNoYes
StreamingYesYesNoYes
Catalogue
ProviderMetaAlibabaGoogleZ.AI
Categorychatchatvideochat
Charge typePay As You GoPay 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.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.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.