# Demis Hassabis (slim) > Demis Hassabis is co-founder of Google DeepMind. DiffusionGemma 26B A4B IT, EmbeddingGemma 300M, Gemma 4 12B IT, Gemma 4 26B A4B IT, Gemma 4 31B IT, Gemma 4 E2B IT and Gemma 4 E4B IT on Anchor Terminal, with grades, an about and public profiles. - Full: https://www.anchorterminal.com/people/demis-hassabis/index.md (~1,900 tokens) · this version ~1,580 tokens · JSON https://www.anchorterminal.com/people/demis-hassabis/index.json · canonical https://www.anchorterminal.com/people/demis-hassabis/ - Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-10-28 - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) Co-founder, Google DeepMind. Demis Hassabis co-founded DeepMind in 2010 with Shane Legg and Mustafa Suleyman and leads Google DeepMind. ## What Demis builds - [DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md), graded D, 50/100, in Open-weight models. DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0. Co-founder, Google DeepMind, researched 2026-10-28. - [EmbeddingGemma 300M](https://www.anchorterminal.com/tools/embeddinggemma-300m.md), graded D, 50.7/100, in Embeddings & rerankers. EmbeddingGemma 300M is Google DeepMind's open-weight text embedding model of 308M parameters, returning 768-dimension vectors that truncate to 512, 256 or 128 from up to 2,048 tokens in over 100 languages, gated under the Gemma Terms of Use. Co-founder, Google DeepMind, researched 2026-10-28. - [Gemma 4 12B IT](https://www.anchorterminal.com/tools/gemma-4-12b-it.md), graded BB, 71.3/100, in Open-weight models. Gemma 4 12B IT is Google DeepMind's open-weight instruction-tuned dense model, 11.95B parameters in an encoder-free design, taking text, images, video frames and audio with a 262,144-token context, published on Hugging Face under Apache 2.0. Co-founder, Google DeepMind, researched 2026-10-28. - [Gemma 4 26B A4B IT](https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md), graded BB, 72.2/100, in Open-weight models. Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0. Co-founder, Google DeepMind, researched 2026-10-28. - [Gemma 4 31B IT](https://www.anchorterminal.com/tools/gemma-4-31b-it.md), graded BB, 74.1/100, in Open-weight models. Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0. Co-founder, Google DeepMind, researched 2026-10-28. - [Gemma 4 E2B IT](https://www.anchorterminal.com/tools/gemma-4-e2b-it.md), graded C, 60.4/100, in Open-weight models. Gemma 4 E2B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 2.3B effective parameters (5.1B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0. Co-founder, Google DeepMind, researched 2026-10-28. - [Gemma 4 E4B IT](https://www.anchorterminal.com/tools/gemma-4-e4b-it.md), graded B, 63.2/100, in Open-weight models. Gemma 4 E4B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 4.5B effective parameters (8B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0. Co-founder, Google DeepMind, researched 2026-10-28. ## Works with - [Brendan O’Donoghue](https://www.anchorterminal.com/people/brendan-o-donoghue/), Director of Research; DiffusionGemma / Gemini Diffusion lead - [Gus Martins](https://www.anchorterminal.com/people/gus-martins/), Gemma Product Manager - [Min Choi](https://www.anchorterminal.com/people/min-choi/), EmbeddingGemma launch product manager; current Gemini models PM - [Mustafa Suleyman](https://www.anchorterminal.com/people/mustafa-suleyman/), Co-founder, DeepMind - [Sahil Dua](https://www.anchorterminal.com/people/sahil-dua/), EmbeddingGemma research lead; launch lead research engineer - [Sebastian Flennerhag](https://www.anchorterminal.com/people/sebastian-flennerhag/), Research Scientist; DiffusionGemma research lead - [Shane Legg](https://www.anchorterminal.com/people/shane-legg/), Co-founder and Chief AGI Scientist, Google DeepMind ## Questions ### Who is Demis Hassabis? Demis Hassabis co-founded DeepMind in 2010 with Shane Legg and Mustafa Suleyman and leads Google DeepMind. ### What does Google DeepMind make? Anchor Terminal lists DiffusionGemma 26B A4B IT, EmbeddingGemma 300M, Gemma 4 12B IT, Gemma 4 26B A4B IT, Gemma 4 31B IT, Gemma 4 E2B IT and Gemma 4 E4B IT from Google DeepMind. ### How are Demis's tools graded? DiffusionGemma 26B A4B IT is graded D, 50 out of 100; EmbeddingGemma 300M is graded D, 50.7 out of 100; Gemma 4 12B IT is graded BB, 71.3 out of 100; Gemma 4 26B A4B IT is graded BB, 72.2 out of 100; Gemma 4 31B IT is graded BB, 74.1 out of 100; Gemma 4 E2B IT is graded C, 60.4 out of 100; Gemma 4 E4B IT is graded B, 63.2 out of 100, on Anchor Terminal's benchmark, which scores every listing against the same checklist. ## Sources - https://uk.linkedin.com/in/lgusmartins - https://x.com/gusthema - https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/ - https://arxiv.org/html/2608.00146v1 - https://x.com/flennerhag - https://x.com/bodonoghue85 - https://gemma.ai/ - https://x.com/Google - https://deepmind.google/ - https://ai.google.dev/gemma/docs/discord - https://gemma.ai/resources/ - https://en.wikipedia.org/wiki/Google_DeepMind - https://deepmind.google/about/ - https://en.wikipedia.org/wiki/Demis_Hassabis - https://en.wikipedia.org/wiki/Shane_Legg - https://en.wikipedia.org/wiki/Mustafa_Suleyman - https://developers.googleblog.com/en/introducing-embeddinggemma/ - https://arxiv.org/abs/2509.20354 - https://x.com/sahildua2305 - https://www.linkedin.com/in/minschoi - https://x.com/iohcsnim - https://huggingface.co/google/embeddinggemma-300m These are the people building the tools we list, named for their public role in the company or project behind a listing, from public pages, with the date it was researched.