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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 Luna ProOpenAIRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Nemotron 3.5 Lightning (Free)NVIDIARemove
gpt-6-luna-pro vs deepseek-v4.1-flash vs nemotron-3.5-lightning:free
AttributeGPT-6 Luna Progpt-6-luna-proDeepSeek V4.1 Flashdeepseek-v4.1-flashNemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free
Pricing
Input$0.10 / 1M$0.30 / 1M$0 / 1M
Output$0.50 / 1M$1.20 / 1M$0 / 1M
Cache Write (5m)$0.10 / 1M$0.30 / 1M—
Cache Write (1h)$0.10 / 1M$0.30 / 1M—
Cache Read$0.10 / 1M$0.30 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1.1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIDeepSeekNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryGPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.NVIDIA 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.