Gemma 4 and what makes an open model succeed

Gemma 4 and what makes an open model succeed

From Interconnects by Nathan Lambert

April 3, 2026 · 9 min

About this episode

The episode discusses the challenges and opportunities in the evolving landscape of open AI models compared to closed models.

Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex , not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a…

People in this episode

Host: Nathan Lambert

Topics covered

  • open models
  • AI development
  • machine learning
  • technology trends
  • research community
  • agentic AI

Keywords

  • open models
  • AI
  • machine learning
  • Llama
  • Qwen
  • Claude
  • Codex
  • agentic AI

Mentioned in this episode

Products: Gemma 4, Llama 3, Llama 2, Qwen 3, Qwen 2.5, Llama 4, Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5

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