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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. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  2. Gemini 4 ArgonGoogleRemove
  3. Kimi K2Moonshot AIRemove
gemini-3.1-flash-lite-image-vertex vs gemini-4-argon vs kimi-k2
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGemini 4 Argongemini-4-argonKimi K2kimi-k2
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$2.00 / 1M$0.34 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$0.34 / 1M
Cache ReadNot applicable$2.00 / 1M$0.34 / 1M
Input—$2.00 / 1M$0.34 / 1M
Output—$10.00 / 1M$1.50 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M63K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleGoogleMoonshot AI
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. 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 1K. A request for a larger size returns a 1K 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.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.Kimi K2-Instruct-0905 is the newest and strongest Kimi K2 model — a trillion-parameter MoE system with 32B active parameters. It delivers major gains in agentic coding, better frontend development results, and an expanded 256K context window for longer tasks.