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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. GPT Image 2OpenAIRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
gpt-image-2 vs kimi-k3.1-pro vs nemotron-3.5-super-vl:free
AttributeGPT Image 2gpt-image-2Kimi K3.1 Prokimi-k3.1-proNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Request$0.04 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$2.00 / 1M—
Cache Write (1h)Not applicable$2.00 / 1M—
Cache ReadNot applicable$2.00 / 1M$0 / 1M
Input—$2.00 / 1M$0 / 1M
Output—$10.00 / 1M$0 / 1M
Web Search—$0 / 1M—
Cache Write——$0 / 1M
Context
Max context272K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIMoonShot AINVIDIA
Categoryimagechatchat
Charge typePay Per RequestPay As You GoFree
Released———
Description
SummaryGPT Image 2 combines OpenAI's GPT-5.4 with advanced image generation capabilities from GPT Image 2, enabling fully integrated multimodal workflows. It allows users to seamlessly transition between reasoning, coding, and visual generation within a single interaction, making it well suited for creative, development, and agent-driven applications that require both intelligence and visual output.Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.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.