<story><title>An In-depth Look at Gemini&apos;s Language Abilities</title><url>https://arxiv.org/abs/2312.11444</url></story><parent_chain><item><author>mkmk</author><text>Mixtral is a mystery to me. How in the world is that team on par with&amp;#x2F;beating GOOGLE, who presumably have all the resources in the world to throw at this?</text></item><item><author>paxys</author><text>Has anyone (outside of Google) gotten to play with Gemini Ultra yet? Been hearing a lot about Pro, but I&amp;#x27;d be interested in seeing whether Ultra is really close to as capable as they claim.&lt;p&gt;Also very interesting that Mixtral 8x7B ranks in the same neighborhood as Gemini Pro&amp;#x2F;GPT 3.5 Turbo&amp;#x2F;Claude 2.1 while being fully open source and Apache 2.0 licensed.</text></item></parent_chain><comment><author>n2d4</author><text>Mixtral is on-par with Gemini Pro, not Gemini Ultra (and even there it is further behind Gemini Pro than Gemini Pro is behind GPT 3.5). But to directly answer your question, they are quite well-funded, having raised over $700mil to date. I definitely wouldn&amp;#x27;t count them out.</text></comment>
<story><title>An In-depth Look at Gemini&apos;s Language Abilities</title><url>https://arxiv.org/abs/2312.11444</url></story><parent_chain><item><author>mkmk</author><text>Mixtral is a mystery to me. How in the world is that team on par with&amp;#x2F;beating GOOGLE, who presumably have all the resources in the world to throw at this?</text></item><item><author>paxys</author><text>Has anyone (outside of Google) gotten to play with Gemini Ultra yet? Been hearing a lot about Pro, but I&amp;#x27;d be interested in seeing whether Ultra is really close to as capable as they claim.&lt;p&gt;Also very interesting that Mixtral 8x7B ranks in the same neighborhood as Gemini Pro&amp;#x2F;GPT 3.5 Turbo&amp;#x2F;Claude 2.1 while being fully open source and Apache 2.0 licensed.</text></item></parent_chain><comment><author>carterschonwald</author><text>My understanding, however fuzzy, is that all the safety&amp;#x2F;politeness tuning results in models that are at times less likely to give accurate responses. That said, I suspect that either way both types of models largely give similar answers for soft questions aside from those politeness and safety things</text></comment>