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OpenAI on A2ZAI Latest official posts, live builder intel, and company context in one place. https://a2zai.ai/companies/openai
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Blog, newsroom, or RSS — first-party updates.
Explore lower GPT‑5.6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale.
A technical deep dive into the Codex agent loop, explaining how Codex CLI orchestrates models, tools, prompts, and performance using the Responses API.
GPT-5.6 improves AI efficiency across models, inference, and agentic workflows, helping deliver more useful intelligence per dollar.
How two API settings improved GPT-5.6 performance on ARC-AGI-3, boosting scores and efficiency by retaining reasoning and enabling compaction.
How OpenAI built an agent runtime using the Responses API, shell tool, and hosted containers to run secure, scalable agents with files, tools, and state.
Monitored signals — official and third-party, builder-relevant.
ARC-AGI-3 tests how well models can learn unfamiliar 2D games without instructions. The standard harness discarded GPT-5.6 Sol’s reasoning after each move and dropped earlier actions as the context filled up. The model had to keep starting over. https://t.co/cLtHYMNqiO
OpenAI
We implemented the harness with the Responses API and turned on: → Retained reasoning → Context compaction On the public set, GPT-5.6 Sol’s score rose 188% while using 6x fewer output tokens. https://t.co/uN1IrKEugu
OpenAI
We’re also upgrading Auto-review in the ChatGPT app and Codex CLI from GPT-5.4 to GPT-5.6 Luna. Combined with Luna’s new price, we expect Auto-review to cost about 10x less, making your agentic workflows more cost-efficient.
OpenAI
Participants will receive access to our frontier models, including our GPT-5.6 family of models. Each workspace includes business-grade privacy and security protections. Researcher data is not used to train our models by default, and participants can invite up to four
OpenAI
GPT-5.6 Sol has been used to solve open problems in mathematics. So why was it struggling with ARC-AGI-3, a benchmark of 2D puzzle games? We investigated. The harness was not letting it remember what it had learned. We found that enabling two API settings tripled our scores https://t.co/0mv05MnSn2
OpenAI
After deployment, we applied GPT-5.6 Sol to advance the frontier of efficiency by making itself more efficient to run. The results: - 20% lower serving costs from production GPU kernel improvements. - 15%+ better token-generation efficiency from improved speculative decoding.
OpenAI
A2ZAI summaries that explain what changed for builders.
We hope these experiments serve as a reminder that evals rarely measure models in isolation—they also measure a bundle of less visible choices about API settings, harness design, and prompting. If you’re an API developer trying to maximize performance, we recommend using the
We’re also upgrading Auto-review in the ChatGPT app and Codex CLI from GPT-5.4 to GPT-5.6 Luna. Combined with Luna’s new price, we expect Auto-review to cost about 10x less, making your agentic workflows more cost-efficient.
Explore lower GPT‑5.6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale.
A benchmark score reflects the model as well as the harness and settings used to run it. For long-running agents, retaining reasoning and compacting context lets the model build on what it has already learned. https://t.co/JBIKCKrUfw
We implemented the harness with the Responses API and turned on: → Retained reasoning → Context compaction On the public set, GPT-5.6 Sol’s score rose 188% while using 6x fewer output tokens. https://t.co/uN1IrKEugu
ARC-AGI-3 tests how well models can learn unfamiliar 2D games without instructions. The standard harness discarded GPT-5.6 Sol’s reasoning after each move and dropped earlier actions as the context filled up. The model had to keep starting over. https://t.co/cLtHYMNqiO
GPT-5.6 Sol has been used to solve open problems in mathematics. So why was it struggling with ARC-AGI-3, a benchmark of 2D puzzle games? We investigated. The harness was not letting it remember what it had learned. We found that enabling two API settings tripled our scores https://t.co/0mv05MnSn2
After deployment, we applied GPT-5.6 Sol to advance the frontier of efficiency by making itself more efficient to run. The results: - 20% lower serving costs from production GPU kernel improvements. - 15%+ better token-generation efficiency from improved speculative decoding.
Making advanced intelligence more abundant and affordable is central to our mission to ensure AGI benefits all of humanity. With the help of GPT-5.6 Sol, we have made leaps in efficiency. Today, we are passing those gains on in the API with lower prices for Luna and Terra, and https://t.co/TieMXHtxSd
Along with the price reduction on GPT-5.6 Luna and Terra, Fast mode for GPT-5.6 Sol in the API delivers up to 2.5x the speed of Standard processing at 2x the Standard price. Fast mode gives API customers faster access to GPT-5.6 Sol, with no change in intelligence.
We are committed to pushing the model frontier across cost efficiency, capability, and speed. Starting today, we are reducing prices for GPT-5.6 Luna by 80% and GPT-5.6 Terra by 20% , and offering a faster option for GPT-5.6 Sol in the API. Luna and Terra’s lower prices are https://t.co/rFhK7XKedp
These optimizations across our stack compound to unlock the most performant models at every point in the cost-intelligence curve. https://t.co/tcv8oo0z7G
Creator of ChatGPT and GPT-4, leading AI research lab
OpenAI closed a Growth, which signals where investors see momentum in Foundation Models. This matters because capital often precedes product velocity, hiring, and ecosystem pull. Backers include Thrive Capital, Microsoft, NVIDIA.