The 2026 Model Library

Families that matter right now

Family	Maker	Sizes	Why care
Llama 3.x	Meta	1B–405B	Strong general-purpose; reliable tool use from 3.1+
Qwen 2.5 / 3 / 3.5	Alibaba	0.5B–235B	Multilingual; tools; Qwen 3 has hybrid thinking
Gemma 3	Google	270M–27B	Best quality-per-size; vision
DeepSeek-R1	DeepSeek	1.5B–671B	Reasoning model; competitive with O3 on math
Phi-4	Microsoft	14B	State-of-the-art for its size
Mistral / Mixtral	Mistral	7B–12B / 8x7B	Tool support; Nemo has 128K context
GPT-OSS	OpenAI	20B–120B	Open-weight; tools; thinking
Qwen3-Coder	Alibaba	30B–480B	Coding / agentic specialization

How to pick

Start with the default Llama or Qwen family at the size your hardware fits. They're well-tested, well-supported, and have reliable tool-calling.
Move to a specialist when the general one fails. Coding-heavy work → Qwen3-Coder. Hard math / reasoning → DeepSeek-R1. Vision → Gemma 3 or Qwen 2.5-VL.
Don't chase newest. Stable models that have been on the library for a few months have community Modelfiles, GGUF variants from bartowski, and vetted prompt templates.

The library is a moving target

This list will be partly out of date by the time you read it — new families land monthly. The shape of the decision (general vs specialist; size vs quant; tool capability) is more durable than any specific model name.

Code

Browse the library, then commit·bash

# Browse interactively
open https://ollama.com/library

# Filter by capability — vision-capable models
# (the library page lets you filter; we replicate locally with ollama show)
for m in llama3.2-vision gemma3:12b qwen2.5vl; do
  echo "=== $m ==="
  ollama show $m | grep -i capabilities
done

# Pick ONE general model + ONE specialist as your daily set
ollama pull qwen2.5:7b           # general
ollama pull qwen3-coder:7b       # coding specialist

Families that matter right now

How to pick

The library is a moving target

Code

External links

Exercise

Progress

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