Tag
A discussion about the existence of trustworthy rankings comparing closed and open large language models, and whether models in the 70B–350B parameter range are worth the cost.
Analyzes the gap between open weights and closed source LLMs using the Artificial Analysis Intelligence Index and other benchmarks, finding that the gap is shrinking on some metrics but stable on others.
Fenng explained the positioning difference between Tencent's WeLM (closed-source large model) and Hunyuan (open-source large model), pointing out that the closed-source model will not disclose technical details or participate in evaluations, and suggested understanding it through product experience.
Elie Bakouch critiques Sakana AI's Fugu system as a closed-source orchestration layer over closed-source models, arguing it lacks transparency and true AI sovereignty, with technical limitations in routing and cost efficiency.
After firing Junyang Lin, Qwen has locked down its large models and is no longer releasing open source models, while other Chinese AI labs continue to open source their latest models. Rumors suggest the small model team is gone and Qwen 3.6/3.7 may be the last open source models.
Elena Rossini investigates claims that European microblogging platform W Social may have switched to closed source, highlighting discrepancies between its public image and actual practices, including migration of EU officials' accounts.
The article argues that without open-source LLM competition, closed-source companies like Anthropic will become arrogant and overcharge customers, as exemplified by a $200/month subscription.
Papers With Code announced a new feature supporting closed-source evaluations, using the dense Microsoft MAI Thinking 1 tech report as an example with a special 'closed' tag.
Commentary on the emergence of paid, subscription-based skill marketplaces for closed-source skills, noting how quickly this model has appeared after an era of free GitHub repos.
Vitalik Buterin argues against the narratives that bugs are inevitable and that AI bug-finding necessitates closed-source, stating that writing secure code has become harder but not impossible.
Article argues that AI-generated code and closed-source software are inherently less secure, and that LLMs like Anthropic’s Mythos will exacerbate vulnerabilities, making open-source projects the only trustworthy option.