OpenAI发布GPT-6 Luna模型
OpenAI新出的GPT-6 Luna性能提升明显，价格却便宜91%，性价比超高。
OpenAI的GPT-6 Luna (Max)在Agent Arena排行榜中排名第23位，净提升1.6%。
该模型每任务中位成本为0.05美元，比GPT-6 Sol (Max)低94%，比GPT-6 Astra (Max)低98%。
相比GPT-5.6 Luna (xHigh)，该模型排名上升6位，从第29位提升至第23位。
GPT-6 Luna (Max) by @OpenAI is #23 in Agent Arena with +1.6% net improvement across 8K real-world agentic sessions from our global community of users. Although GPT-6 Luna (Max) did not land on the Agent Arena Pareto frontier, it remains a cost-efficient model. Its $0.05 median cost per task is 94% lower than GPT-6 Sol (Max) at $0.82 and 98% lower than GPT-6 Astra (Max) at $2.59. Its net-improvement score also comes within 0.09 percentage points of #22 GPT 5.5, while costing 91% less than its $0.56 median cost per task. This release is a six-place point-rank move over GPT-5.6 Luna (xHigh), at -0.9% and #29 ! By signal, GPT-6 Luna’s clearest gains over GPT-5.6 Luna are in: - Confirmed Success: #17 (+4.6%) vs. #33 (-4.6%) - Bash Recovery: #21 (+4.2%) vs. #27 (+2.2%) Congrats to the @OpenAI team on this release! OpenAI @OpenAI Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include GPT-6, OpenAI, Agent Arena, reasoning models.
Source attribution: curated from lmarena.ai (@lmarena_ai). Cross-source corroboration: 11 independent sources have covered this development (including IT之家, PolymarketMoney, Tanishq Abraham (论文推介)), confirming industry-wide signal strength.
