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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. Claude Sonnet 5.5AnthropicRemove
  3. Perceptron Mk1.5PerceptronRemove
  4. GLM 5V TurboZ.AIRemove

4 is the maximum. Remove one to add another.

transcribe-1-pro vs claude-sonnet-5.5 vs perceptron-mk1.5 vs glm-5v-turbo
AttributeTranscribe 1 Protranscribe-1-proClaude Sonnet 5.5claude-sonnet-5.5Perceptron Mk1.5perceptron-mk1.5GLM 5V Turboglm-5v-turbo
Pricing
Input— Not priced per input token$2.00 / 1M$0.15 / 1M$1.20 / 1M
Output— Not priced per output token$10.00 / 1M$1.50 / 1M$4.00 / 1M
Cache Write (5m)Not applicable$2.50 / 1M$0.15 / 1M$1.20 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$0.15 / 1M$1.20 / 1M
Cache ReadNot applicable$0.20 / 1M$0.15 / 1M$1.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M36.9K202.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesNoYes
Function CallingNoYesNoYes
JSON ModeNoYesNoYes
StreamingNoYesYesYes
Catalogue
ProviderFish AudioAnthropicPerceptronZ.AI
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-24—2026-09-25—
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.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per 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.