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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 3.5 TranscribeGoogleRemove
  2. Claude Fable 5.1AnthropicRemove
  3. GLM 5.3 FlashZ.AIRemove
gemini-3.5-transcribe vs claude-fable-5.1 vs glm-5.3-flash
AttributeGemini 3.5 Transcribegemini-3.5-transcribeClaude Fable 5.1claude-fable-5.1GLM 5.3 Flashglm-5.3-flash
Pricing
Input— Not priced per input token$10.00 / 1M$0.075 / 1M
Output— Not priced per output token$50.00 / 1M$0.25 / 1M
Cache Write (5m)Not applicable$12.50 / 1M$0.075 / 1M
Cache Write (1h)Not applicable$20.00 / 1M$0.075 / 1M
Cache ReadNot applicable$0.25 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleAnthropicZ.AI
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.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.