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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 LunaOpenAIRemove
  2. GPT-6 Luna ProOpenAIRemove
  3. Gemma 4 31B (Free)GoogleRemove
gpt-6-luna vs gpt-6-luna-pro vs gemma-4-31b-it:free
AttributeGPT-6 Lunagpt-6-lunaGPT-6 Luna Progpt-6-luna-proGemma 4 31B (Free)gemma-4-31b-it:free
Pricing
Input$0.10 / 1M$0.10 / 1M$0 / 1M
Output$0.50 / 1M$0.50 / 1M$0 / 1M
Cache Write (5m)$0.10 / 1M$0.10 / 1M—
Cache Write (1h)$0.10 / 1M$0.10 / 1M—
Cache Read$0.10 / 1M$0.10 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1.1M1.1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIOpenAIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryGPT-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 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.Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks.