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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. Claude Fable 5.1AnthropicRemove
  3. Nemotron 3.5 LightningNVIDIARemove
glm-5.3-flash vs claude-fable-5.1 vs nemotron-3.5-lightning
AttributeGLM 5.3 Flashglm-5.3-flashClaude Fable 5.1claude-fable-5.1Nemotron 3.5 Lightningnemotron-3.5-lightning
Pricing
Input$0.075 / 1M$10.00 / 1M$0 / 1M
Output$0.25 / 1M$50.00 / 1M$0 / 1M
Cache Write (5m)$0.075 / 1M$12.50 / 1M$0.00 / 1M
Cache Write (1h)$0.075 / 1M$20.00 / 1M$0.00 / 1M
Cache Read$0.075 / 1M$1.00 / 1M$0.00 / 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.AIAnthropicNVIDIA
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 Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications.NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.