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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. Gemini 3.8 FlashGoogleRemove
  3. Gemini 4 ArgonGoogleRemove
transcribe-1-pro vs gemini-3.8-flash vs gemini-4-argon
AttributeTranscribe 1 Protranscribe-1-proGemini 3.8 Flashgemini-3.8-flashGemini 4 Argongemini-4-argon
Pricing
Input— Not priced per input token$0.75 / 1M$2.00 / 1M
Output— Not priced per output token$3.75 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$0.75 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$0.75 / 1M$2.00 / 1M
Cache ReadNot applicable$0.75 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderFish AudioGoogleGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-24——
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.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.