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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. Claude Opus 4.5AnthropicRemove
  3. GLM 5.3 FlashZ.AIRemove
muse-spark-1.3 vs claude-opus-4-5-20251101 vs glm-5.3-flash
AttributeMuse Spark 1.3muse-spark-1.3Claude Opus 4.5claude-opus-4-5-20251101GLM 5.3 Flashglm-5.3-flash
Pricing
Input$1.25 / 1M$4.00 / 1M$0.075 / 1M
Output$4.25 / 1M$20.00 / 1M$0.25 / 1M
Cache Write (5m)$1.25 / 1M$5.00 / 1M$0.075 / 1M
Cache Write (1h)$1.25 / 1M$8.00 / 1M$0.075 / 1M
Cache Read$1.25 / 1M$0.40 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M200K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaAnthropicZ.AI
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.Claude Opus 4.5 is Anthropic's frontier reasoning model, built for complex engineering, agent workflows, and long computer-use tasks. It offers strong multimodal skills, better security against prompt injection, and flexible effort controls — including a Verbosity setting to trade speed vs. depth and token use. With advanced tool use, long-context handling, and support for coordinated multi-agent setups, it excels at research, debugging, multi-step planning, and UI/spreadsheet automation while improving reliability, alignment, and efficiency over earlier Opus versions.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.