Skip to content

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. GLM 5.3 FlashZ.AIRemove
  2. Claude Opus 4.5AnthropicRemove
  3. Muse Spark 1.3MetaRemove
glm-5.3-flash vs claude-opus-4-5-20251101 vs muse-spark-1.3
AttributeGLM 5.3 Flashglm-5.3-flashClaude Opus 4.5claude-opus-4-5-20251101Muse Spark 1.3muse-spark-1.3
Pricing
Input$0.075 / 1M$4.00 / 1M$1.25 / 1M
Output$0.25 / 1M$20.00 / 1M$4.25 / 1M
Cache Write (5m)$0.075 / 1M$5.00 / 1M$1.25 / 1M
Cache Write (1h)$0.075 / 1M$8.00 / 1M$1.25 / 1M
Cache Read$0.075 / 1M$0.40 / 1M$1.25 / 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
ProviderZ.AIAnthropicMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-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.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.Muse 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.