Skip to content

Compare models

Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. mai-ds-r1:freeNot in the catalogueRemove
  2. Gemini 4 ArgonGoogleRemove
  3. GPT-6 LunaOpenAIRemove
mai-ds-r1:free vs gemini-4-argon vs gpt-6-luna
Attributemai-ds-r1:freeNot in the catalogueGemini 4 Argongemini-4-argonGPT-6 Lunagpt-6-luna
Pricing
Input—$2.00 / 1M$0.10 / 1M
Output—$10.00 / 1M$0.50 / 1M
Cache Write (5m)—$2.00 / 1M$0.10 / 1M
Cache Write (1h)—$2.00 / 1M$0.10 / 1M
Cache Read—$2.00 / 1M$0.10 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max context—1M1.1M
Max output—N/AN/A
Capabilities
Vision—YesYes
Function Calling—YesYes
JSON Mode—YesYes
Streaming—YesYes
Catalogue
Provider—GoogleOpenAI
Category—chatchat
Charge type—Pay As You GoPay As You Go
Released———
Description
Summary—Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.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.