GPT-6.1 Sol vs. GPT-5.6 Luna

GPT-6.1 Sol is OpenAI's upgraded Sol model for developers and professionals who need advanced reasoning and agentic capabilities without the higher cost of GPT-6 Astra. It is designed for coding, professional knowledge work, computer use, scientific research, factual question answering, and multi-step business workflows. OpenAI describes GPT-6.1 Sol as approaching GPT-6 Astra's intelligence across several important workloads while charging one-fifth of Astra's standard input and output token prices. On DeepSWE v1.1, which evaluates long-horizon software engineering in real codebases, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and exceeds GPT-6 Sol's best score by 6.4 percentage points. Its professional-work capabilities include understanding complex PDFs containing tables, charts, diagrams, and fine-print details across fields such as finance, healthcare, and legal work. On AutomationBench, GPT-6.1 Sol improves on GPT-6 Sol by 4.8 percentage points at the same reasoning setting and scores 2.2 points above Opus 5.5 at medium reasoning effort. Computer-use performance also advances significantly, with GPT-6.1 Sol outperforming GPT-6 Sol by seven percentage points on the OSWorld 2.0 offline set at maximum reasoning effort and coming within 2.1 points of GPT-6 Astra. For scientific research, the model can work with code and terminal tools on workflows involving data analysis, simulations, model fitting, and theorem proving, more than doubling GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort. OpenAI also reports improved factual accuracy, including a reduction in the factual-error rate from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol at low reasoning effort on its deliberately difficult factuality evaluation.

GPT-5.6 Luna is the lowest-cost model in OpenAI’s GPT-5.6 family, built for fast and affordable AI assistance across everyday and technical workflows. The GPT-5.6 lineup includes Sol as the flagship model, Terra as the balanced model for everyday work, and Luna as the efficient model for users who need strong capability at lower cost. Luna is intended for developers, businesses, and teams that need scalable AI for coding help, workflow automation, research support, analysis, customer-facing applications, and high-volume API usage. In the pasted preview text, Luna is presented as part of the same GPT-5.6 release process and benchmark set as Sol and Terra. It appears in evaluations for command-line coding workflows, long-horizon biology tasks, ExploitBench, and ExploitGym, indicating that it is designed to handle more than simple chat use cases. The model is priced at a lower per-token rate than Sol and Terra, making it more suitable for applications where cost efficiency is a major priority. GPT-5.6 Luna also supports the new GPT-5.6 prompt caching approach, including explicit cache breakpoints, a 30-minute minimum cache life, cache writes billed above the uncached input rate, and discounted cached-input reads. Like the rest of the GPT-5.6 family, Luna is developed with layered safeguards matched to model capability. These safeguards include trained refusals for prohibited cyber assistance, real-time misuse classifiers, paused generation for higher-risk cases, account-level review, monitoring, enforcement, automated red-teaming, and third-party human expert red-teaming. Luna is expected to support legitimate defensive and technical workflows such as code review, debugging, patch development, security education, and defensive testing while making prohibited misuse more difficult and detectable. GPT-5.6 Luna helps organizations deploy GPT-5.6-class AI where speed, affordability, scalability, and safe production use are the most important requirements.