Skip to content

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 4 ArgonGoogleRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
transcribe-1-pro vs gemini-4-argon vs nemotron-3.5-super-vl
AttributeTranscribe 1 Protranscribe-1-proGemini 4 Argongemini-4-argonNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input— Not priced per input token$2.00 / 1M$0.10 / 1M
Output— Not priced per output token$10.00 / 1M$0.40 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$0.10 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$0.10 / 1M
Cache ReadNot applicable$2.00 / 1M$0.10 / 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 AudioGoogleNVIDIA
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 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.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.