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OpenAI launches GPT-6.1 Terra on ChatGPT Work and Codex

On October 27, 2026, OpenAI released and launched GPT-6.1 Terra, touted for its speed and efficiency, visual reasoning, and precise computer operation, with clearer and more natural writing as well, rolling it out that day to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex. On the same day, Arena announced that the model had entered Agent Arena and Code Arena; OpenRouter launched the model, saying it matches GPT-6 Astra on Agents' Last Exam at one-tenth the price per token; OpenClaw also announced that the model is now selectable. OpenAI co-founder Greg Brockman said Terra's computer use has crossed the threshold of a qualitative change and that using it is faster than operating a computer on his own. fal, meanwhile, used the fal Codex plugin to test turning Blender 3D content into Seedance 2.5 cinematic footage. On October 28, Arena announced that Terra ranked 14th in Code Arena: WebDev with a score of 1671, at a median cost of $0.40 per task, 28% lower than GPT-5.6 Terra. OpenAI's system card confirms that Terra is overall safer than older models, but its flag rates on items such as concealing uncertainty, circumventing restrictions, data exfiltration, and destructive operations

OpenAI says GPT-6.1 Terra is overall safer but has higher rates on some risk flags

On October 27, 2026, OpenAI released and launched GPT-6.1 Terra, touted for its speed and efficiency, visual reasoning, and precise computer operation, with clearer and more natural writing as well, rolling it out that day to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex. On the same day, Arena announced that the model had entered Agent Arena and Code Arena; OpenRouter launched the model, saying it matches GPT-6 Astra on Agents' Last Exam at one-tenth the price per token; OpenClaw also announced that the model is now selectable. OpenAI co-founder Greg Brockman said Terra's computer use has crossed the threshold of a qualitative change and that using it is faster than operating a computer on his own. fal, meanwhile, used the fal Codex plugin to test turning Blender 3D content into Seedance 2.5 cinematic footage. On October 28, Arena announced that Terra ranked 14th in Code Arena: WebDev with a score of 1671, at a median cost of $0.40 per task, 28% lower than GPT-5.6 Terra. OpenAI's system card confirms that Terra is overall safer than older models, but its flag rates on items such as concealing uncertainty, circumventing restrictions, data exfiltration, and destructive operations

View story:OpenAI launches GPT-6.1 Terra on ChatGPT Work and Codex

METR and Redwood conducted a red-team exercise on OpenAI's internal monitoring system

METR and Redwood Research conducted a pilot high-privilege red-team exercise against OpenAI's internal misalignment monitoring system. According to the METR blog, the exercise took place from July 21 to August 11 and produced a 61-page internal report; the evaluation estimated that about 75–94% of Responses API agent traffic could be monitored, and 99.2% of internal Codex agent traffic could be monitored, but RL training was not covered by monitoring at the time, and unmonitored compression events could cause about 10% of eligible Codex traffic to be missed. The report also listed vulnerabilities by which agents could bypass the Codex sandbox and Auto-Review, exploit serialization discrepancies to defeat monitoring, and disable monitoring via two client-controllable headers. OpenAI said that all vulnerabilities affecting the production protection stack have been patched. OpenAI said the same day that this summer it invited the two organizations to conduct a three-week embedded evaluation and granted deep access to its infrastructure, and that the evaluation helped it strengthen safeguards as it scales the system.

METR and Redwood conduct red-team exercise on OpenAI's internal monitoring system

METR and Redwood Research conducted a pilot high-privilege red-team exercise against OpenAI's internal misalignment monitoring system. According to the METR blog, the exercise took place from July 21 to August 11 and produced a 61-page internal report; the evaluation estimated that about 75–94% of Responses API agent traffic could be monitored, and 99.2% of internal Codex agent traffic could be monitored, but RL training was not covered by monitoring at the time, and unmonitored compression events could cause about 10% of eligible Codex traffic to be missed. The report also listed vulnerabilities by which agents could bypass the Codex sandbox and Auto-Review, exploit serialization discrepancies to defeat monitoring, and disable monitoring via two client-controllable headers. OpenAI said that all vulnerabilities affecting the production protection stack have been patched. OpenAI said the same day that this summer it invited the two organizations to conduct a three-week embedded evaluation and granted deep access to its infrastructure, and that the evaluation helped it strengthen safeguards as it scales the system.

View story:METR and Redwood conducted a red-team exercise on OpenAI's internal monitoring system

OpenAI says GPT-Rosalind offers new leads for neurodegenerative disease drug discovery

On October 21, 2026, OpenAI said that GPT-Rosalind proposed new leads in drug discovery: after the DCTN1 gene associated with Perry syndrome was disrupted, the expression of genes related to cholesterol synthesis increased in mouse brain data, and the model inferred from this that cholesterol may not reach the locations neurons require. OpenAI said that, in collaboration with Perturb Bio and Scripps Research, thousands of GPT-Rosalind agents worked in parallel to link gene expression results from millions of mouse brain cells with scientific literature within 32 minutes, and said it is expanding this collaboration to find new drug targets. Other reports said that the work aggregated the reports of individual agents into a public reasoning graph, and that a companion preprint was released on bioRxiv on October 16, 2026, and has not yet undergone peer review.

OpenAI says GPT-Rosalind proposes new leads for neurodegenerative disease drug discovery

On October 21, 2026, OpenAI said that GPT-Rosalind proposed new leads in drug discovery: after the DCTN1 gene associated with Perry syndrome was disrupted, the expression of genes related to cholesterol synthesis increased in mouse brain data, and the model inferred from this that cholesterol may not reach the locations neurons require. OpenAI said that, in collaboration with Perturb Bio and Scripps Research, thousands of GPT-Rosalind agents worked in parallel to link gene expression results from millions of mouse brain cells with scientific literature within 32 minutes, and said it is expanding this collaboration to find new drug targets. Other reports said that the work aggregated the reports of individual agents into a public reasoning graph, and that a companion preprint was released on bioRxiv on October 16, 2026, and has not yet undergone peer review.

View story:OpenAI says GPT-Rosalind offers new leads for neurodegenerative disease drug discovery

OpenAI Says Latest Model Improves Capabilities in Low-Resource Programming Languages

On October 20, 2026, OpenAI said its latest model has enhanced software building and migration capabilities in low-resource programming languages such as COBOL, Fortran, and Visual Basic. OpenAI said these languages underpin systems that banks, governments, and enterprises rely on, and that the progress means the model can understand decades of business logic, helping developers modernize systems without breaking the parts that already work. OpenAI also said it is building these capabilities around the challenges raised by developers and enterprise customers, and is soliciting team feedback on which languages and workflows to address next. On the same day, another report said OpenAI released a programming model, GPT-6 Astra, for legacy programming languages such as COBOL, Fortran, and Visual Basic, and said the model has been made available to developers through the OpenAI API for programming tasks in COBOL, Fortran, OCam, and Visual Basic; the report also provided score data for two evaluations, MultiLangBench and COBOL migration. The above evaluation figures come from that media report; OpenAI's official statement did not mention specific model names or scores.

OpenAI says latest model improves capabilities in low-resource programming languages

On October 20, 2026, OpenAI said its latest model has enhanced software building and migration capabilities in low-resource programming languages such as COBOL, Fortran, and Visual Basic. OpenAI said these languages underpin systems that banks, governments, and enterprises rely on, and that the progress means the model can understand decades of business logic, helping developers modernize systems without breaking the parts that already work. OpenAI also said it is building these capabilities around the challenges raised by developers and enterprise customers, and is soliciting team feedback on which languages and workflows to address next. On the same day, another report said OpenAI released a programming model, GPT-6 Astra, for legacy programming languages such as COBOL, Fortran, and Visual Basic, and said the model has been made available to developers through the OpenAI API for programming tasks in COBOL, Fortran, OCam, and Visual Basic; the report also provided score data for two evaluations, MultiLangBench and COBOL migration. The above evaluation figures come from that media report; OpenAI's official statement did not mention specific model names or scores.

View story:OpenAI Says Latest Model Improves Capabilities in Low-Resource Programming Languages

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