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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 V3OpenAIRemove
  3. GLM 5.3Z.AIRemove
gemini-3.7-flash vs whisper-large-v3 vs glm-5.3
AttributeGemini 3.7 Flashgemini-3.7-flashWhisper Large V3whisper-large-v3GLM 5.3glm-5.3
Pricing
Input$0.375 / 1M$9.25 / 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 ModeYesYesYes
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 is OpenAI's advanced open-source automatic speech recognition (ASR) model, supporting both audio transcription and translation across 99+ languages. It accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg, and delivers strong performance in noisy, real-world conditions. With 1.55B parameters and a low 10.3% word error rate, it provides accurate, multilingual transcription with support for word- and segment-level timestamps, making it well suited for high-quality, noise-robust 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.