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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. DeepSeek V4.1 Flash 1017DeepSeekRemove
  3. GPT Image 2OpenAIRemove
nemotron-3.5-super-vl:free vs deepseek-v4.1-flash-1017 vs gpt-image-2
AttributeNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017GPT Image 2gpt-image-2
Pricing
Input$0 / 1M$0.10 / 1M—
Output$0 / 1M$0.50 / 1M—
Cache Write$0 / 1M——
Cache Read$0 / 1M$0.10 / 1MNot applicable
Cache Write (5m)—$0.10 / 1MNot applicable
Cache Write (1h)—$0.10 / 1MNot applicable
Web Search—$0 / 1M—
Request——$0.04 / request
Billing——Pay Per Request
Context
Max context1M1M272K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesNo
JSON ModeYesYesNo
StreamingYesYesNo
Catalogue
ProviderNVIDIADeepSeekOpenAI
Categorychatchatimage
Charge typeFreePay As You GoPay Per Request
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.DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.GPT 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.