Skip to content

Compare models

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. Whisper Large V3 TurboOpenAIRemove
  3. GLM 5.3Z.AIRemove
gemini-3.7-flash vs whisper-large-v3-turbo vs glm-5.3
AttributeGemini 3.7 Flashgemini-3.7-flashWhisper Large V3 Turbowhisper-large-v3-turboGLM 5.3glm-5.3
Pricing
Input$0.375 / 1M$3.33 / 1M$1.40 / 1M
Output$1.88 / 1M$0 / 1M$4.40 / 1M
Cache Write (5m)$0.375 / 1MNot applicable$1.40 / 1M
Cache Write (1h)$0.375 / 1MNot applicable$1.40 / 1M
Cache Read$0.375 / 1MNot applicable$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoNo
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderGoogleOpenAIZ.AI
Categorychatvoicechat
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.Whisper Large V3 Turbo is an optimized version of OpenAI's Whisper Large V3 speech recognition model, designed for high-speed and cost-efficient transcription. It supports 99+ languages and accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg. With a ~12% word error rate and real-time speed factors up to 216×, it delivers fast, scalable performance for latency-sensitive and high-throughput transcription workloads, making it ideal for real-time and large-scale speech processing applications.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.