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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Gemini 4 ArgonGoogleRemove
  2. Nemotron 3 Nano Omni (Free)NVIDIARemove
gemini-4-argon vs nemotron-3-nano-omni-30b-a3b-reasoning:free
AttributeGemini 4 Argongemini-4-argonNemotron 3 Nano Omni (Free)nemotron-3-nano-omni-30b-a3b-reasoning:free
Pricing
Input$2.00 / 1M$0 / 1M
Output$10.00 / 1M$0 / 1M
Cache Write (5m)$2.00 / 1M—
Cache Write (1h)$2.00 / 1M—
Cache Read$2.00 / 1M$0 / 1M
Web Search$0 / 1M—
Cache Write—$0 / 1M
Context
Max context1M256K
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderGoogleNVIDIA
Categorychatchat
Charge typePay As You GoFree
Released——
Description
SummaryGemini 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 Nano Omni is an open 30B-A3B multimodal model designed as a perception and context sub-agent for enterprise agent systems. It supports text, image, video, and audio inputs with text output, enabling unified multimodal reasoning within a single inference loop. Built on a hybrid MoE Transformer–Mamba architecture with Conv3D video layers and Efficient Video Sampling (EVS), it delivers significantly improved efficiency for video reasoning—achieving ~2× higher throughput and 2.5× lower compute compared to separate pipelines. With up to 300K context length and extended thinking support, it is well suited for scalable, multimodal agent workflows.