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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. GLM 5.3 FlashZ.AIRemove
  3. Claude Opus 4.6AnthropicRemove
muse-spark-1.3 vs glm-5.3-flash vs claude-opus-4-6
AttributeMuse Spark 1.3muse-spark-1.3GLM 5.3 Flashglm-5.3-flashClaude Opus 4.6claude-opus-4-6
Pricing
Input$1.25 / 1M$0.075 / 1M$4.00 / 1M
Output$4.25 / 1M$0.25 / 1M$20.00 / 1M
Cache Write (5m)$1.25 / 1M$0.075 / 1M$5.00 / 1M
Cache Write (1h)$1.25 / 1M$0.075 / 1M$8.00 / 1M
Cache Read$1.25 / 1M$0.075 / 1M$0.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaZ.AIAnthropic
Categorychatchatchat
Charge typePay As You GoPay As You GoPay 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.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.Opus 4.6 is Anthropic's most capable model for coding and long-running professional workflows, designed for agents that operate across entire workflows rather than single prompts. It demonstrates strong performance on large codebases, complex refactoring, and multi-step debugging, with improved contextual understanding, deeper problem decomposition, and higher reliability on challenging engineering tasks compared to earlier generations. Beyond software development, Opus 4.6 excels at sustained knowledge work, producing near production-ready documents, technical plans, and analyses in a single pass while maintaining coherence across long outputs and extended sessions. Its strength in persistence, judgment, and structured execution makes it well suited for technical design, migration planning, and end-to-end project execution.