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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. Gemini 3.5 FlashGoogleRemove

4 is the maximum. Remove one to add another.

transcribe-1-pro vs claude-sonnet-5.5 vs perceptron-mk1.5 vs gemini-3.5-flash
AttributeTranscribe 1 Protranscribe-1-proClaude Sonnet 5.5claude-sonnet-5.5Perceptron Mk1.5perceptron-mk1.5Gemini 3.5 Flashgemini-3.5-flash
Pricing
Input$0 / 1M$2.00 / 1M$0.15 / 1M$1.50 / 1M
Output$0 / 1M$10.00 / 1M$1.50 / 1M$9.00 / 1M
Cache Write (5m)Not applicable$2.50 / 1M$0.15 / 1M$1.50 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$0.15 / 1M$1.50 / 1M
Cache ReadNot applicable$0.20 / 1M$0.15 / 1M$1.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M36.9K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesNoYes
Function CallingNoYesNoYes
JSON ModeNoYesNoYes
StreamingNoYesYesYes
Catalogue
ProviderFish AudioAnthropicPerceptronGoogle
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.Gemini 3.5 Flash is Google's high-efficiency multimodal model, delivering near-Pro level performance in coding and reasoning at Flash-tier speed and cost. It supports text, image, video, audio, and PDF inputs, making it well suited for diverse multimodal workflows. Optimized for coding proficiency and parallel agentic execution, the model defaults to medium thinking effort for faster, cost-efficient responses while supporting configurable thinking levels (minimal, low, medium, high) for fine-grained cost–performance control.