<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mixture-of-Experts on Best of AI</title><link>https://bestofai.io/tags/mixture-of-experts/</link><description>Recent content in Mixture-of-Experts on Best of AI</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Thu, 16 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://bestofai.io/tags/mixture-of-experts/index.xml" rel="self" type="application/rss+xml"/><item><title>DBRX</title><link>https://bestofai.io/models/dbrx/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/dbrx/</guid><description>&lt;p&gt;DBRX is Databricks&amp;rsquo; open-weight large language model, released in March 2024 as the base model behind the instruction-tuned DBRX Instruct. It is a fine-grained mixture-of-experts model with 132 billion total parameters and 36 billion active parameters per token, trained from scratch on 12 trillion tokens of text and code using Databricks&amp;rsquo; own Mosaic AI training stack. At release, Databricks reported it outperforming other open models of the time, including Llama 2 70B and Mixtral, on benchmarks such as MMLU and HumanEval, and it was pitched as evidence that an enterprise-focused company could train a competitive foundation model without relying on a big-lab research budget. It has a 32,000-token context window and is released under the Databricks Open Model License, with weights available on Hugging Face for self-hosting or through Databricks&amp;rsquo; own serving infrastructure.&lt;/p&gt;</description></item><item><title>DeepSeek V3</title><link>https://bestofai.io/models/deepseek-v3/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/deepseek-v3/</guid><description/></item><item><title>DeepSeek V4-Pro</title><link>https://bestofai.io/models/deepseek-v4-pro/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/deepseek-v4-pro/</guid><description/></item><item><title>DeepSeek-MoE-16B</title><link>https://bestofai.io/models/deepseek-moe-16b/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/deepseek-moe-16b/</guid><description>&lt;p&gt;DeepSeek-MoE-16B, released in January 2024, was DeepSeek&amp;rsquo;s early attempt at a fine-grained mixture-of-experts architecture, and its design choices carried forward into the company&amp;rsquo;s later V2 and V3 models. It has 16.4 billion total parameters but only about 2.8 billion active per token, achieved through two techniques described in its paper: splitting experts into smaller, more specialized units, and isolating a set of shared experts that always fire to capture common knowledge. Trained from scratch on 2 trillion English and Chinese tokens, it reached performance comparable to the dense DeepSeek 7B and Llama 2 7B models while using only around 40% of the compute those models require at inference. It has a 4,096-token context window and was released under DeepSeek&amp;rsquo;s open license for self-hosted use, mainly as a research demonstration of expert specialization rather than a production-focused release.&lt;/p&gt;</description></item><item><title>DeepSeek-V2</title><link>https://bestofai.io/models/deepseek-v2/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/deepseek-v2/</guid><description/></item><item><title>Hunyuan Large</title><link>https://bestofai.io/models/hunyuan-large/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/hunyuan-large/</guid><description/></item><item><title>Hunyuan-A13B</title><link>https://bestofai.io/models/hunyuan-a13b/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/hunyuan-a13b/</guid><description/></item><item><title>Kimi K2</title><link>https://bestofai.io/models/kimi-k2/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/kimi-k2/</guid><description/></item><item><title>Kimi K2.6</title><link>https://bestofai.io/models/kimi-k2-6/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/kimi-k2-6/</guid><description/></item><item><title>Kimi K2.7 Code</title><link>https://bestofai.io/models/kimi-k2-7-code/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/kimi-k2-7-code/</guid><description/></item><item><title>Llama 4 Maverick</title><link>https://bestofai.io/models/llama-4-maverick/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/llama-4-maverick/</guid><description/></item><item><title>MiniMax-01</title><link>https://bestofai.io/models/minimax-01/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/minimax-01/</guid><description/></item><item><title>Mixtral 8x22B</title><link>https://bestofai.io/models/mixtral-8x22b/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/mixtral-8x22b/</guid><description/></item><item><title>Mixtral 8x7B</title><link>https://bestofai.io/models/mixtral-8x7b/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/mixtral-8x7b/</guid><description/></item><item><title>OLMoE</title><link>https://bestofai.io/models/olmoe/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/olmoe/</guid><description/></item><item><title>PanGu-Sigma</title><link>https://bestofai.io/models/pangu-sigma/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/pangu-sigma/</guid><description/></item><item><title>Qwen2.5-Max</title><link>https://bestofai.io/models/qwen2-5-max/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/qwen2-5-max/</guid><description/></item><item><title>Qwen3-235B-A22B</title><link>https://bestofai.io/models/qwen3-235b-a22b/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/qwen3-235b-a22b/</guid><description/></item><item><title>Qwen3-Coder</title><link>https://bestofai.io/models/qwen3-coder/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/qwen3-coder/</guid><description/></item><item><title>Samba-1</title><link>https://bestofai.io/models/samba-1/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/samba-1/</guid><description/></item><item><title>Zephyr 141B</title><link>https://bestofai.io/models/zephyr-141b/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>https://bestofai.io/models/zephyr-141b/</guid><description/></item></channel></rss>