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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. Gemini 3.8 FlashGoogleRemove
  2. Claude Opus 4.7AnthropicRemove
  3. GLM 5.3 FlashZ.AIRemove
gemini-3.8-flash vs claude-opus-4-7 vs glm-5.3-flash
AttributeGemini 3.8 Flashgemini-3.8-flashClaude Opus 4.7claude-opus-4-7GLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.75 / 1M$4.00 / 1M$0.075 / 1M
Output$3.75 / 1M$20.00 / 1M$0.25 / 1M
Cache Write (5m)$0.75 / 1M$5.00 / 1M$0.075 / 1M
Cache Write (1h)$0.75 / 1M$8.00 / 1M$0.075 / 1M
Cache Read$0.75 / 1M$0.40 / 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
ProviderGoogleAnthropicZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.Opus 4.7 is the next generation of Anthropic's Opus family, designed for long-running, asynchronous agent workflows. Building on Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable execution across extended pipelines such as large codebases, multi-stage debugging, and end-to-end project orchestration. Beyond coding, Opus 4.7 enhances knowledge work capabilities, including document drafting, presentation creation, and data analysis. With strong coherence over long outputs and sessions, it is well suited for tasks requiring persistence, judgment, and sustained execution.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.