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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. Qwen3.8 2.4T A95BAlibabaRemove
  3. Claude Opus 5.5AnthropicRemove
gpt-6-luna vs qwen3.8-2.4t-a95b vs claude-opus-5.5
AttributeGPT-6 Lunagpt-6-lunaQwen3.8 2.4T A95Bqwen3.8-2.4t-a95bClaude Opus 5.5claude-opus-5.5
Pricing
Input$0.10 / 1M$1.80 / 1M$4.00 / 1M
Output$0.50 / 1M$5.40 / 1M$20.00 / 1M
Cache Write (5m)$0.10 / 1M$1.80 / 1M$5.00 / 1M
Cache Write (1h)$0.10 / 1M$1.80 / 1M$8.00 / 1M
Cache Read$0.10 / 1M$1.80 / 1M$0.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.1M262K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIAlibabaAnthropic
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.Qwen3.8 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.Claude Opus 5.5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows, succeeding Opus 5. It excels at multi-step changes across large codebases, code review and bug detection, financial and scientific analysis, and understanding dense charts, diagrams, and screenshots, with stronger grounding when reporting figures and citing sources. Compared with Opus 5, it completes comparable tasks with fewer steps and lower token usage while providing clearer, more concise progress reporting. With adaptive thinking and configurable effort levels, Opus 5.5 can balance reasoning depth, latency, and cost, making it well suited for both demanding autonomous workflows and latency-sensitive professional tasks.