Comparing AI models — who builds what and how to choose
The major model families in 2026 at a glance. Who builds Claude, GPT, Gemini, Llama, Mistral, DeepSeek, Qwen — and which model to pick when.
in AI Models
Meta's open Llama models — with freely available weights, the foundation of many open-weight projects, fine-tunes, and local deployments.
Code Llama is Metas coding-specialized Llama 2 variant from August 2023 — open-weights, in several sizes and with base, Python and Instruct flavors.
Llama 2 is Metas open-weights language model from July 2023 — the first Llama with a commercially usable license, in 7B, 13B and 70B sizes with a 4K context.
Meta's open model generation, released on 18 April 2024. It launched in the 8B and 70B sizes (each in base and Instruct variants) under the Llama 3 community license; freely downloadable, for research and commercial use.
Llama 3.1 (July 2024) is Metas open-weight family in 8B, 70B and 405B with a 128K context and multilinguality — the 405B was the first open model on par with GPT-4o and Claude 3.5 Sonnet.
Llama 3.2 is Meta's model generation from September 2024 — the first with vision support (11B/90B) and lightweight edge models (1B/3B) for mobile and on-device use.
Llama 3.3 (December 2024) is Metas open 70B instruct model — it reaches 405B-class quality at 70B cost, with a 128K context and open weights.
Llama-3-SauerkrautLM-70b is a German-language DPO fine-tune of Meta Llama 3 70B, developed by VAGOsolutions and Hyperspace.ai.
Llama 4 Maverick (April 2025) is Meta's large Mixture-of-Experts variant in the Llama 4 lineup — 17B active out of 400B parameters across 128 experts, 1M-token context.
Llama 4 Scout (April 2025) is Meta's smaller Mixture-of-Experts variant in the Llama 4 lineup — 17B active out of 109B parameters, 10M-token context, multimodal.
The major model families in 2026 at a glance. Who builds Claude, GPT, Gemini, Llama, Mistral, DeepSeek, Qwen — and which model to pick when.
Claude, GPT, Gemini, Llama & co. — who builds what, where each family shines, and how to pick the right model for your own use case.