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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. Nemotron 3.5 Super VLNVIDIARemove
  2. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  3. MiniMax M1 (Extended)MiniMaxRemove
nemotron-3.5-super-vl vs gemini-3.1-flash-image-vertex vs minimax-m1:extended
AttributeNemotron 3.5 Super VLnemotron-3.5-super-vlGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexMiniMax M1 (Extended)minimax-m1:extended
Pricing
Input$0.10 / 1M—$0.55 / 1M
Output$0.40 / 1M—$2.20 / 1M
Cache Write (5m)$0.10 / 1MNot applicable$0.55 / 1M
Cache Write (1h)$0.10 / 1MNot applicable$0.55 / 1M
Cache Read$0.10 / 1MNot applicable$0.55 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.067 / request—
Billing—Pay Per Request—
Context
Max context1MN/A128K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesNoNo
JSON ModeYesNoNo
StreamingYesNoNo
Catalogue
ProviderNVIDIAGoogleMiniMax
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released———
Description
SummaryNVIDIA 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.Gemini 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.—