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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 Luna ProOpenAIRemove
  2. GPT-6 SolOpenAIRemove
  3. Qwen3.8 2.4T A95BAlibabaRemove
gpt-6-luna-pro vs gpt-6-sol vs qwen3.8-2.4t-a95b
AttributeGPT-6 Luna Progpt-6-luna-proGPT-6 Solgpt-6-solQwen3.8 2.4T A95Bqwen3.8-2.4t-a95b
Pricing
Input$0.10 / 1M$2.00 / 1M$1.80 / 1M
Output$0.50 / 1M$10.00 / 1M$5.40 / 1M
Cache Write (5m)$0.10 / 1M$2.00 / 1M$1.80 / 1M
Cache Write (1h)$0.10 / 1M$2.00 / 1M$1.80 / 1M
Cache Read$0.10 / 1M$2.00 / 1M$1.80 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.1M1.1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIOpenAIAlibaba
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGPT-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.GPT-6 Sol is OpenAI's cost-efficient high-end model in the GPT-6 series, positioned between the flagship GPT-6 Astra and the fast GPT-6 Luna tier. It is designed for professional knowledge work, agentic coding, business workflow automation, and computer-use tasks, with particular strength in long-horizon software engineering across real-world codebases. GPT-6 Sol approaches Astra-level factual reliability at a significantly lower cost, while sharing its clear and concise communication style. This balance of capability, reliability, and efficiency makes it well suited for production agents, complex engineering workflows, and scalable professional applicationsQwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.