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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 Lite Image (Google AI Studio)GoogleEnterprise & partner accessRemove
  2. MiniMax M3MiniMaxRemove
  3. Gemini 4 ArgonGoogleRemove
gemini-3.1-flash-lite-image-aistudio vs minimax-m3 vs gemini-4-argon
AttributeGemini 3.1 Flash Lite Image (Google AI Studio)gemini-3.1-flash-lite-image-aistudioMiniMax M3minimax-m3Gemini 4 Argongemini-4-argon
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.30 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$0.30 / 1M$2.00 / 1M
Cache ReadNot applicable$0.30 / 1M$2.00 / 1M
Input—$0.30 / 1M$2.00 / 1M
Output—$1.20 / 1M$10.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMiniMaxGoogle
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3.1 Flash Lite Image served from Google AI Studio. 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.MiniMax-M3 is a multimodal foundation model from MiniMax, supporting text, image, and video inputs with text output and a 1M-token context window. It is designed for long-horizon agentic workflows, coding, and tool-driven task execution, enabling sustained reasoning across complex tasks. Built on MiniMax Sparse Attention (MSA), the model dramatically improves long-context efficiency by replacing full attention with KV-block selection, reducing compute costs at 1M-token contexts while maintaining strong performance. Trained as a native multimodal model and optimized for multi-turn, production-style collaboration, MiniMax-M3 excels at extended, multi-step workflows rather than single-turn interactions.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.