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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 3.7 FlashGoogleRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. GPT-4o Mini TranscribeOpenAIRemove
gemini-3.7-flash vs glm-5.3-flash vs gpt-4o-mini-transcribe
AttributeGemini 3.7 Flashgemini-3.7-flashGLM 5.3 Flashglm-5.3-flashGPT-4o Mini Transcribegpt-4o-mini-transcribe
Pricing
Input$0.375 / 1M$0.075 / 1M$0.625 / 1M
Output$1.88 / 1M$0.25 / 1M$0.625 / 1M
Cache Write (5m)$0.375 / 1M$0.075 / 1MNot applicable
Cache Write (1h)$0.375 / 1M$0.075 / 1MNot applicable
Cache Read$0.375 / 1M$0.075 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M128K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesYesYes
StreamingYesYesNo
Catalogue
ProviderGoogleZ.AIOpenAI
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.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.GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments.