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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. Qwen3 MaxAlibabaRemove
gemini-3.1-flash-lite-image-vertex vs gemini-4-argon vs qwen3-max
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGemini 4 Argongemini-4-argonQwen3 Maxqwen3-max
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$2.00 / 1M$1.20 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$1.20 / 1M
Cache ReadNot applicable$2.00 / 1M$1.20 / 1M
Input—$2.00 / 1M$1.20 / 1M
Output—$10.00 / 1M$6.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M256K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleGoogleAlibaba
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.Qwen3-Max is an updated Qwen3 model with significant gains in reasoning, instruction following, multilingual support, and long-tail knowledge. It improves accuracy in math, coding, logic, and science, reduces hallucinations, and delivers higher-quality open-ended and conversational responses in over 100 languages. Optimized for RAG and tool use, it focuses on reliable final answers rather than a dedicated “thinking” mode.