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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 Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  2. MiniMax M1 (Extended)MiniMaxRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
gemini-3.1-flash-image-vertex vs minimax-m1:extended vs nemotron-3.5-super-vl
AttributeGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexMiniMax M1 (Extended)minimax-m1:extendedNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Request$0.067 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.55 / 1M$0.10 / 1M
Cache Write (1h)Not applicable$0.55 / 1M$0.10 / 1M
Cache ReadNot applicable$0.55 / 1M$0.10 / 1M
Input—$0.55 / 1M$0.10 / 1M
Output—$2.20 / 1M$0.40 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A128K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingNoNoYes
JSON ModeNoNoYes
StreamingNoNoYes
Catalogue
ProviderGoogleMiniMaxNVIDIA
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
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.—NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.