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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. MiMo-V2-FlashXiaomiRemove
gpt-6-luna vs gpt-6-luna-pro vs mimo-v2-flash
AttributeGPT-6 Lunagpt-6-lunaGPT-6 Luna Progpt-6-luna-proMiMo-V2-Flashmimo-v2-flash
Pricing
Input$0.10 / 1M$0.10 / 1M$0.09 / 1M
Output$0.50 / 1M$0.50 / 1M$0.29 / 1M
Cache Write (5m)$0.10 / 1M$0.10 / 1M$0.09 / 1M
Cache Write (1h)$0.10 / 1M$0.10 / 1M$0.09 / 1M
Cache Read$0.10 / 1M$0.10 / 1M$0.09 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.1M1.1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIOpenAIXiaomi
Categorychatchatchat
Charge typePay As You GoPay As You GoPay 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.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.MiMo-V2-Flash is an open-source Mixture-of-Experts (MoE) foundation model developed by Xiaomi, featuring 309B total parameters with 15B activated per token and a hybrid attention architecture. It supports a 256K context window and a hybrid thinking mode toggle, enabling flexible trade-offs between speed and reasoning depth. The model excels in reasoning, coding, and agentic workflows, ranking #1 globally among open-source models on benchmarks such as SWE-bench Verified and SWE-bench Multilingual. With performance comparable to leading proprietary models like Claude Sonnet 4.5 at a fraction of the cost, MiMo-V2-Flash is well suited for efficient, high-performance deployments.