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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. Gemini 4 ArgonGoogleRemove
  2. GPT-6 LunaOpenAIRemove
  3. mai-ds-r1:freeNot in the catalogueRemove
gemini-4-argon vs gpt-6-luna vs mai-ds-r1:free
AttributeGemini 4 Argongemini-4-argonGPT-6 Lunagpt-6-lunamai-ds-r1:freeNot in the catalogue
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 context1M1.1M—
Max outputN/AN/A—
Capabilities
VisionYesYes—
Function CallingYesYes—
JSON ModeYesYes—
StreamingYesYes—
Catalogue
ProviderGoogleOpenAI—
Categorychatchat—
Charge typePay As You GoPay As You Go—
Released———
Description
SummaryGemini 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.—