[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f1UDm-LSHcBsfnTpc4EjjFUsAG2lQFk1-2he19BZHVHo":3},{"article":4,"related":18},{"id":5,"slug":6,"title":7,"seo_title":7,"description":8,"keywords":9,"content":10,"category":11,"image_url":12,"source_guid":13,"published_at":14,"created_at":14,"updated_at":15,"source_url":16,"source_name":17},1367,"gpt-61-terra-cuts-api-costs-widening-the-budget-for-revisions","GPT-6.1 Terra cuts API costs, widening the budget for revisions","OpenAI released GPT-6.1 Terra with lower API rates. A worked cost comparison shows the budget for extra processing and the limits of benchmark claims.","[\"GPT-6.1 Terra\",\"OpenAI\",\"API pricing\",\"AI coding\",\"workflow automation\"]","\u003Cp>OpenAI has released GPT-6.1 Terra, a mid-range model aimed at everyday office work and automated software control, \u003Ca href=\"https:\u002F\u002Fthe-decoder.com\u002Fgpt-6-1-terra-halves-openais-api-prices-and-still-gains-ground-on-its-larger-models\u002F\" rel=\"noopener noreferrer\">according to the-decoder.com\u003C\u002Fa>. Its API rates are $1 per million input tokens and $5 per million output tokens, compared with $2 and $12 for GPT-5.6 Terra. OpenAI says its intended tasks include drafting proposals, interpreting technical drawings and filling out forms.\u003C\u002Fp>\n\n\u003Cp>For teams budgeting a drafting workflow, the useful comparison is how much processing those rates buy. \u003Cstrong>Hypothetical calculation:\u003C\u002Fstrong> a batch consuming 10 million uncached input tokens and 2 million output tokens would cost (10 × $1) + (2 × $5) = \u003Cstrong>$20\u003C\u002Fstrong> on GPT-6.1 Terra. The same token volumes on GPT-5.6 Terra would cost (10 × $2) + (2 × $12) = \u003Cstrong>$44\u003C\u002Fstrong>. These are token-charge calculations, not measured task costs.\u003C\u002Fp>\n\n\u003Cp>That creates a concrete budget choice: two Terra passes at those hypothetical volumes would cost $40, still below one predecessor pass at $44. A team could therefore consider a drafting-and-revision workflow within its previous token budget. The condition matters: this assumes each pass consumes the stated volumes. Longer revision prompts, additional output or further retries change the total, and a second pass does not establish that the result is correct. The decision is whether the revised output meets the workflow’s acceptance criteria within that $44 ceiling.\u003C\u002Fp>\n\n\u003Cp>The reported benchmarks support considering Terra for more work, but do not settle which tasks to move. Artificial Analysis reports a Coding Agent Index v2.0 score of 48 for Terra, below GPT-6.1 Sol’s 54. OpenAI still recommends Sol and GPT-6 Astra for the most demanding coding tasks. Taken together, those facts suggest a selective routing decision: consider Terra where the lower processing budget is useful and acceptable output can be established, while retaining the larger models for demanding coding work. The calculation establishes budget headroom; it does not establish equivalent quality.\u003C\u002Fp>","AI & Machine Learning","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793060294518-lr9cmf5c9.webp","814cbd4d71346f4ebbfc48ae0bdaaae179cd43d7c40bec111169391efe5d68bf","2026-10-27T00:18:14.995Z",null,"https:\u002F\u002Fthe-decoder.com\u002Fgpt-6-1-terra-halves-openais-api-prices-and-still-gains-ground-on-its-larger-models\u002F","the-decoder.com",[19,26,33,40],{"id":20,"slug":21,"title":22,"description":23,"category":11,"image_url":24,"published_at":25},1375,"study-finds-llm-research-claims-age-unevenly-across-models","Study finds LLM research claims age unevenly across models","A study found 44 percent of 50 LLM findings had expired. Its model comparisons show why research age alone cannot guide model selection or workflow safeguards.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793751185684-b9dptvj7d45.webp","2026-11-04T00:13:05.978Z",{"id":27,"slug":28,"title":29,"description":30,"category":11,"image_url":31,"published_at":32},1374,"athenahealth-links-patient-records-to-paid-claude-accounts","Athenahealth links patient records to paid Claude accounts","Athenahealth's new connection lets paying Claude users query their records. We examine subscription costs, access controls and the limits on medical guidance.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793664800530-ef6pcsfgu6o.webp","2026-11-03T00:13:20.909Z",{"id":34,"slug":35,"title":36,"description":37,"category":11,"image_url":38,"published_at":39},1373,"adobes-editagent-improves-image-edits-at-a-compute-cost","Adobe’s EditAgent improves image edits at a compute cost","Adobe’s EditAgent combines specialist tools with measured image checks. Its reported gains suggest a role in training data, with a compute tradeoff to weigh.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793578401524-t2vuwnn0u8a.webp","2026-11-02T00:13:21.752Z",{"id":41,"slug":42,"title":43,"description":44,"category":11,"image_url":45,"published_at":46},1372,"anthropic-tests-expose-limits-of-screening-fine-tuning-data","Anthropic tests expose limits of screening fine-tuning data","Anthropic trained research models on benign-looking fiction that encouraged sabotage. The findings inform how teams should evaluate fine-tuning releases.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793507192508-kf4jz2sjc.webp","2026-11-01T04:26:32.789Z"]