Claude Fable 5.2 Max is Anthropic’s reasoning model, released in November 2026, with text and image input, text output, and a one-million-token context window. It ranks first among 74 models on the Artificial Analysis Intelligence Index, scoring 65 against a comparable-model median of 40. The index combines nine evaluations covering areas such as agentic knowledge work, automation, coding, scientific workflows, factual reliability, professional documents, and difficult reasoning tasks. The score places Fable 5.2 among the strongest models assessed so far, though the available information does not provide its task completion time or comparisons on individual tests.
That capability comes with high usage costs and substantial token consumption. Input costs $8 per million tokens and output costs $40 per million, each four times the respective median; the estimated cost to run one index task is $26.54, placing it near the most expensive models assessed. The evaluation used 275 million output tokens, compared with a median of 72.8 million, indicating unusually verbose responses. A 98% cache discount is listed, but does not change the overall picture of a costly model. The page reports no publicly available API for independently comparing its performance or task costs.
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Researchers tested 16 LLMs on all 55 TIS-100 puzzles, giving each one attempt and time to refine its code. The models found 133 new performance tradeoffs across 18 puzzles and set three outright cycle records, though human players improved on most solutions within days. (tis-100.wincon.dev)
Australia is considering a new criminal offence for AI companies that fail to take reasonable steps to prevent harm caused by their models. The government is also weighing an AI safety watchdog with power to impose significant fines, following a hack of a Medicare statistics portal involving OpenAI. (abc.net.au)
AI is increasingly becoming a gateway through which people find information. Audiences still resist having AI produce their news. (niemanlab.org)
Nvidia holds a central position in the AI industry, making disruption to the company potentially catastrophic for the wider ecosystem. The race for advanced AI is also intensifying economic and geopolitical conflict, while concentrated corporate power and infrastructure face growing political resistance. (france24.com)
LLM inference can require reading about 26 GB of model weights to generate each token from a 13-billion-parameter model. The explanation shows how model size drives memory bandwidth demands during text generation. (youtube.com)
Deletion Tuning matches full fine-tuning across three LLM post-training methods by zeroing fewer than 0.05% of model weights. Its compact masks also preserve prior-task accuracy better than full fine-tuning, LoRA, and random sparse tuning. (arxiv.org)
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