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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 VL (Free)NVIDIARemove
  2. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  3. Qwen3.8 27BAlibabaRemove
nemotron-3.5-super-vl:free vs gemini-3.1-flash-image-vertex vs qwen3.8-27b
AttributeNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexQwen3.8 27Bqwen3.8-27b
Pricing
Input$0 / 1M—$0.45 / 1M
Output$0 / 1M—$3.20 / 1M
Cache Write$0 / 1M——
Cache Read$0 / 1MNot applicable$0.45 / 1M
Request—$0.067 / request—
Billing—Pay Per Request—
Cache Write (5m)—Not applicable$0.45 / 1M
Cache Write (1h)—Not applicable$0.45 / 1M
Web Search——$0 / 1M
Context
Max context1MN/A262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderNVIDIAGoogleAlibaba
Categorychatimagechat
Charge typeFreePay 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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.