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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. GLM 5.3 FlashZ.AIRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Claude Opus 5AnthropicRemove
glm-5.3-flash vs deepseek-v4.1-flash vs claude-opus-5
AttributeGLM 5.3 Flashglm-5.3-flashDeepSeek V4.1 Flashdeepseek-v4.1-flashClaude Opus 5claude-opus-5
Pricing
Input$0.075 / 1M$0.30 / 1M$5.00 / 1M
Output$0.25 / 1M$1.20 / 1M$25.00 / 1M
Cache Write (5m)$0.075 / 1M$0.30 / 1M$6.25 / 1M
Cache Write (1h)$0.075 / 1M$0.30 / 1M$10.00 / 1M
Cache Read$0.075 / 1M$0.30 / 1M$0.50 / 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
ProviderZ.AIDeepSeekAnthropic
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.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.Claude Opus 5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows. It excels at end-to-end software engineering, code review, bug detection, visual analysis of charts and documents, complex office deliverables, and parallel subagent coordination. The model maintains reliable instruction following and tool use across extended tasks, while remaining effective at lower reasoning-effort settings for workloads that prioritize latency and token efficiency.