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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. Transcribe 1 ProFish AudioRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Gemini 3.5 TranscribeGoogleRemove
  4. GLM 5V TurboZ.AIRemove

4 is the maximum. Remove one to add another.

transcribe-1-pro vs perceptron-mk1.5 vs gemini-3.5-transcribe vs glm-5v-turbo
AttributeTranscribe 1 Protranscribe-1-proPerceptron Mk1.5perceptron-mk1.5Gemini 3.5 Transcribegemini-3.5-transcribeGLM 5V Turboglm-5v-turbo
Pricing
Input— Not priced per input token$0.15 / 1M— Not priced per input token$1.20 / 1M
Output— Not priced per output token$1.50 / 1M— Not priced per output token$4.00 / 1M
Cache Write (5m)Not applicable$0.15 / 1MNot applicable$1.20 / 1M
Cache Write (1h)Not applicable$0.15 / 1MNot applicable$1.20 / 1M
Cache ReadNot applicable$0.15 / 1MNot applicable$1.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A36.9K98.3K202.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoNoYesYes
Function CallingNoNoYesYes
JSON ModeNoNoNoYes
StreamingNoYesNoYes
Catalogue
ProviderFish AudioPerceptronGoogleZ.AI
Categoryvoicechatvoicechat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-242026-09-252026-09-25—
Description
SummaryFish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.GLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, designed for vision-based coding and agent-driven workflows. It natively supports image, video, and text inputs, enabling integrated multimodal reasoning and execution. The model excels at long-horizon planning, complex coding, and multi-step task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute”, making it well suited for advanced multimodal automation and real-world agent systems.