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Article 12
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What excites me most about these new 4figure/second token models is that you can essentially do multi-shot prompting (+ nudging) and the user doesn't even feel it, potentially fixing some of the weird hallucinatory/non-deterministic behavior we sometimes end up with.
Genuine question: what kinds of workloads benefit most from this speed? In my coding use, I still hit limitations even with stronger models, so I'm interested in where a much faster model changes the outcome rather than just reducing latency.
It could be interesting to do the metric of intelligence per second.
ie intelligence per token, and then tokens per second
My current feel is that if Sonnet 4.6 was 5x faster than Opus 4.6, I'd be primarily using Sonnet 4.6. But that wasn't true for me with prior model generations, in those generations the Sonnet class models didn't feel good enough compared to the Opus class models. And it might shift again when I'm doing things that feel more intelligence bottlenecked.
But fast responses have an advantage of their own, they give you faster iteration. Kind of like how I used to like OpenAI Deep Research, but then switched to o3-thinking with web search enabled after that came out because it was 80% of the thoroughness with 20% of the time, which tended to be better overall.
It seems like the chat demo is really suffering from the effect of everything going into a queue. You can't actually tell that it is fast at all. The latency is not good.
Assuming that's what is causing this. They might show some kind of feedback when it actually makes it out of the queue.
Please pre-render your website on the server. Client-side JS means that my agent cannot read the press-release and that reduces the chance I am going to read it myself. Also, day one OpenRouter increases the chance that someone will try it.
I can see some promise with diffusion LLMs, but getting them comparable to the frontier is going to require a ton of work and these closed source solutions probably won't really invigorate the field to find breakthroughs. It is too bad that they are following the path of OpenAI with closed models without details as far as I can tell.
Score Breakdown
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PreamblePreamble
No preamble-level content observable in provided HTML/CSS. Content is primarily technical scaffolding.
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Article 1Freedom, Equality, Brotherhood
No assertions of human dignity, freedom, or equality observable. Product announcement blog.
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Article 2Non-Discrimination
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Article 3Life, Liberty, Security
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Article 4No Slavery
No slavery or servitude implications. Product blog.
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Article 5No Torture
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Article 6Legal Personhood
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Article 7Equality Before Law
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Article 8Right to Remedy
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Article 9No Arbitrary Detention
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Article 10Fair Hearing
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Article 11Presumption of Innocence
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Article 12Privacy
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Structural
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SETL
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Combined
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Context Modifier
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Google Analytics (gtag) tracking configured. Framer infrastructure used. No visible opt-in consent mechanism, privacy notice, or user control over data collection. Tracking appears passive/automatic. Regressed from -0.4 to -0.35 due to ambiguity about user consent implementation (may exist but not visible in provided HTML).
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Article 13Freedom of Movement
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Article 14Asylum
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Article 15Nationality
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Article 16Marriage & Family
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Article 17Property
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Article 18Freedom of Thought
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Article 19Freedom of Expression
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Blog post format constitutes publication and information dissemination. Editorial choice to publish product announcement on public platform shows mild alignment with freedom of expression. No censorship or barriers to speech visible. Structural neutrality (standard web platform). Weak signal due to commercial context.
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Article 20Assembly & Association
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Article 23Work & Equal Pay
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Article 30No Destruction of Rights
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