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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. Perceptron Mk1.5PerceptronRemove
  3. Nemotron 3.5 Lightning (Free)NVIDIARemove
gemini-4-argon vs perceptron-mk1.5 vs nemotron-3.5-lightning:free
AttributeGemini 4 Argongemini-4-argonPerceptron Mk1.5perceptron-mk1.5Nemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free
Pricing
Input$2.00 / 1M$0.15 / 1M$0 / 1M
Output$10.00 / 1M$1.50 / 1M$0 / 1M
Cache Write (5m)$2.00 / 1M$0.15 / 1M—
Cache Write (1h)$2.00 / 1M$0.15 / 1M—
Cache Read$2.00 / 1M$0.15 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1M36.9K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesYesYes
Catalogue
ProviderGooglePerceptronNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released—2026-09-25—
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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.