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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. Claude Sonnet 5.5AnthropicRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
claude-sonnet-5.5 vs gemini-3.8-flash vs glm-5.3-flash
AttributeClaude Sonnet 5.5claude-sonnet-5.5Gemini 3.8 Flashgemini-3.8-flashGLM 5.3 Flashglm-5.3-flash
Pricing
Input$2.00 / 1M$0.75 / 1M$0.075 / 1M
Output$10.00 / 1M$3.75 / 1M$0.25 / 1M
Cache Write (5m)$2.50 / 1M$0.75 / 1M$0.075 / 1M
Cache Write (1h)$4.00 / 1M$0.75 / 1M$0.075 / 1M
Cache Read$0.10 / 1M$0.75 / 1M$0.075 / 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
ProviderAnthropicGoogleZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryClaude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.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.