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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. Hy4 previewTencentRemove
  3. mai-ds-r1:freeNot in the catalogueRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

gpt-6-luna vs hy4-preview vs mai-ds-r1:free vs gemini-4-argon
AttributeGPT-6 Lunagpt-6-lunaHy4 previewhy4-previewmai-ds-r1:freeNot in the catalogueGemini 4 Argongemini-4-argon
Pricing
Input$0.10 / 1M$0.834 / 1M—$2.00 / 1M
Output$0.50 / 1M$2.50 / 1M—$10.00 / 1M
Cache Write (5m)$0.10 / 1M$0.834 / 1M—$2.00 / 1M
Cache Write (1h)$0.10 / 1M$0.834 / 1M—$2.00 / 1M
Cache Read$0.10 / 1M$0.834 / 1M—$2.00 / 1M
Web Search$0 / 1M$0 / 1M—$0 / 1M
Context
Max context1.1M1M—1M
Max outputN/AN/A—N/A
Capabilities
VisionYesYes—Yes
Function CallingYesYes—Yes
JSON ModeYesYes—Yes
StreamingYesYes—Yes
Catalogue
ProviderOpenAITencent—Google
Categorychatchat—chat
Charge typePay As You GoPay As You Go—Pay As You Go
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.Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.—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.