感谢用GPT-6搓3D的！燃烧自己的token换来一次全体重置
GPT-6现在能直接生成3D模型了，虽然费token但不用学Blender了
GPT-6新增3D模型生成功能，用户可通过文本描述创建3D模型。该功能消耗大量token，每次生成相当于一次模型重置。Blender等传统3D建模软件可能因此受到冲击。目前该功能处于测试阶段。
感谢用GPT-6搓3D的！燃烧自己的token换来一次全体重置
Blender也不用学了
Vals AI 测了 GPT-6 和 DeepSeek V4.1 的网络攻击能力，开源模型花十分之一的钱就追到只差 8 个百分点，做安全的得看看。
The gain came with fewer reasoning tokens, so the benchmark improvement reflects better raw-data reading rather than longer thinking. Rubrics that reward partial credit and error-spotting make this a more realistic measure of lab readiness than pass/fail tests. Teams evaluating agents for scientific work can use it to compare GPT-6 Astra against GPT-5.6 Sol on messy real-world inputs.
同一道题，Qwen3.8 27B 能写一千词，GPT-6 Sol 只回一百多，这份字数榜把各家模型的啰嗦程度排得明明白白。
一个演讲视频同时做了语音克隆配音和幻灯片翻译，用了 ElevenLabs 和 GPT-6 Astra，还用上了 YouTube 新的备用音轨功能，做法挺值得看看。
The 29× cost gap between frontier models with a 3-point score spread changes the economics of choosing a model for scientific agent workloads. Teams building lab-automation or biotech analysis tools can use the per-task cost and turn counts to pick models that fit their budgets. The 19-point deficit of open weights models like Kimi K3.1 Pro also marks a concrete ceiling for on-premise deployments.
Routing thousands of micro-decisions to a pick-only model could cut agent orchestration costs by orders of magnitude where the answer space is a finite option list, not free text. Builders running multi-step agent pipelines can plug it in as a cheap decision layer while keeping a general model for generation. The gap on HLE shows it is a narrow tool, not a replacement for reasoning models.
