Simon Willison October 26, 2026

GPT-6.1 Terra

OpenAI has released GPT‑6.1 Terra, a fast, efficient model that emphasizes detailed visual reasoning and precise computer use while offering clearer, more natural writing. It is positioned as a mid‑range option—cheaper than Sol but smarter than Luna—suitable for tasks like drafting proposals, interpreting technical drawings, filling out forms, and editing videos. The model is priced at $1 per million input tokens and $5 per million output tokens, roughly half the cost of its predecessor GPT‑5.6 Terra.

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OpenAI Blog

Oct 26

Introducing GPT-6.1 Terra

Bringing fast and capable computer use to everyone

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OpenAI Blog

Jul 30

Advancing the price-performance frontier with GPT-5.6

Explore lower GPT‑5. 6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale.

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Simon Willison

Sep 3

GPT‑6 Astra

GPT‑6 Astra is a new OpenAI model rolling out today to a limited set of organizations and soon to all ChatGPT Plus, Pro, Business, Enterprise users, and via the OpenAI API and AWS. It is priced at $10/million input and $50/million output, matching Claude Fable 5/5.1, and outperforms Fable on most OpenAI self‑reported benchmarks, achieving 99.9% on the ARC‑AGI 3 benchmark with a custom Provider Adapter harness. Astra excels in security tasks—scoring 100% on ExploitBench, 42.4% on ExploitGym, and 99.2% on SRE‑Bench—and handles long context well, hitting 100% on OpenAI’s eight‑needle benchmark at 256K–512K tokens and 96.3% at 512K–1M tokens, though it remains behind Fable on the Intelligence Index and Meta’s Muse Spark 1.3.

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arXiv AI

Jul 16

Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs

arXiv:2607. 13080v1 Announce Type: cross Abstract: Autonomous coding agents force engineering organizations to choose between API-based frontier models -- strong reasoning at high token cost -- and on-premise quantized open-weights models, which promise low-marginal-cost scaling and data sovereignty at some loss of reasoning fidelity.

By Sheng-Wei Peng, Yi-Hsun Lin, Yi-Pei Lee

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arXiv Computation and Language

Sep 14

Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents

arXiv:2609.12533v1 Announce Type: cross Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare da...

By Zhutao Lv, Chenhao Dang, Yi Feng, Yanpei Gong, Xiaolei Wang, Junyan Ye, Conghui He, Weijia Li

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arXiv AI

Jul 24

Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation

arXiv:2607. 20478v1 Announce Type: cross Abstract: Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational policy constraints, not merely producing syntactically plausible configurations.

By Mohamed Jouini

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