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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. GPT-6 SolOpenAIRemove
  2. Nemotron 3.5 Super VL (Free)NVIDIARemove
gpt-6-sol vs nemotron-3.5-super-vl:free
AttributeGPT-6 Solgpt-6-solNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$2.00 / 1M$0 / 1M
Output$10.00 / 1M$0 / 1M
Cache Write (5m)$2.00 / 1M—
Cache Write (1h)$2.00 / 1M—
Cache Read$2.00 / 1M$0 / 1M
Web Search$0 / 1M—
Cache Write—$0 / 1M
Context
Max context1.1M1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderOpenAINVIDIA
Categorychatchat
Charge typePay As You GoFree
Released——
Description
SummaryGPT-6 Sol is OpenAI's cost-efficient high-end model in the GPT-6 series, positioned between the flagship GPT-6 Astra and the fast GPT-6 Luna tier. It is designed for professional knowledge work, agentic coding, business workflow automation, and computer-use tasks, with particular strength in long-horizon software engineering across real-world codebases. GPT-6 Sol approaches Astra-level factual reliability at a significantly lower cost, while sharing its clear and concise communication style. This balance of capability, reliability, and efficiency makes it well suited for production agents, complex engineering workflows, and scalable professional applicationsNVIDIA 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.