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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 LightningNVIDIARemove
  2. GPT-6 LunaOpenAIRemove
  3. GPT-6 AstraOpenAIRemove
nemotron-3.5-lightning vs gpt-6-luna vs gpt-6-astra
AttributeNemotron 3.5 Lightningnemotron-3.5-lightningGPT-6 Lunagpt-6-lunaGPT-6 Astragpt-6-astra
Pricing
Input$0 / 1M$0.10 / 1M$10.00 / 1M
Output$0 / 1M$0.50 / 1M$50.00 / 1M
Cache Write (5m)$0.00 / 1M$0.10 / 1M$10.00 / 1M
Cache Write (1h)$0.00 / 1M$0.10 / 1M$10.00 / 1M
Cache Read$0.00 / 1M$0.10 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1.1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderNVIDIAOpenAIOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryNVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability.GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.