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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 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  2. Gemini 4 ArgonGoogleRemove
  3. Nemotron 3.5 Lightning (Free)NVIDIARemove
gemini-3.1-flash-image-vertex vs gemini-4-argon vs nemotron-3.5-lightning:free
AttributeGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexGemini 4 Argongemini-4-argonNemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free
Pricing
Request$0.067 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$2.00 / 1M—
Cache Write (1h)Not applicable$2.00 / 1M—
Cache ReadNot applicable$2.00 / 1M$0 / 1M
Input—$2.00 / 1M$0 / 1M
Output—$10.00 / 1M$0 / 1M
Web Search—$0 / 1M—
Cache Write——$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleGoogleNVIDIA
Categoryimagechatchat
Charge typePay Per RequestPay As You GoFree
Released———
Description
SummaryGemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048.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 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.