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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. Claude Sonnet 5.5AnthropicRemove
  4. Gemini 3.5 FlashGoogleRemove

4 is the maximum. Remove one to add another.

transcribe-1-pro vs perceptron-mk1.5 vs claude-sonnet-5.5 vs gemini-3.5-flash
AttributeTranscribe 1 Protranscribe-1-proPerceptron Mk1.5perceptron-mk1.5Claude Sonnet 5.5claude-sonnet-5.5Gemini 3.5 Flashgemini-3.5-flash
Pricing
Input$0 / 1M$0.15 / 1M$2.00 / 1M$1.50 / 1M
Output$0 / 1M$1.50 / 1M$10.00 / 1M$9.00 / 1M
Cache Write (5m)Not applicable$0.15 / 1M$2.50 / 1M$1.50 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$4.00 / 1M$1.50 / 1M
Cache ReadNot applicable$0.15 / 1M$0.20 / 1M$1.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A36.9K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoNoYesYes
Function CallingNoNoYesYes
JSON ModeNoNoYesYes
StreamingNoYesYesYes
Catalogue
ProviderFish AudioPerceptronAnthropicGoogle
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-242026-09-25——
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.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.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.