Wichtigste Erkenntnisse
- Most downloaded: Llama 3.2 3B and Llama 3.1 8B — most tutorials, widest tool support.
- Best reasoning: DeepSeek-R1 7B and 14B — chain-of-thought reasoning model, significantly above standard models on math and logic.
- Best coding: Qwen2.5-Coder 7B and 32B — highest HumanEval scores at their size tiers.
- Best image understanding: Llama 3.2 Vision 11B and Gemma 3 9B (vision variant) — both support image input locally.
- As of April 2026, the Ollama library contains 200+ models. All are available via `ollama pull <name>`.
Which Models Are Most Popular on Ollama in 2026?
Popularity on Ollama is measured by download counts visible on each model's library page. As of April 2026, the top downloaded models are dominated by Meta's Llama family — Llama 3.2 3B is the most pulled model overall, largely due to its use as a first-install test model.
Qwen2.5 is the fastest-growing model family in the Ollama library, with Qwen2.5:7b overtaking Mistral 7B in monthly downloads in late 2025. DeepSeek-R1 saw a major spike in early 2025 following its release and remains highly downloaded for reasoning tasks.
Top Ollama Models by Use Case
- General chat (beginner): `ollama run llama3.2:3b` — most documentation, best-supported first model.
- General chat (quality): `ollama run llama3.1:8b` — best balance of quality and RAM for 8 GB machines.
- Coding: `ollama run qwen2.5-coder:7b` — 72% HumanEval, FIM support, 128K context.
- Reasoning and math: `ollama run deepseek-r1:7b` — chain-of-thought model, best local math performance at 7B.
- Multilingual: `ollama run qwen2.5:7b` — 29 native languages, strongest non-English support.
- Image understanding: `ollama run llama3.2-vision:11b` — process images with text prompts locally.
- Fast and lightweight: `ollama run gemma2:2b` — fastest CPU inference, 1.7 GB RAM.
- High quality (16 GB RAM): `ollama run mistral-small3.1` — near-70B quality at 14 GB RAM.
- Embedding generation: `ollama run nomic-embed-text` — 137M parameter embedding model for RAG pipelines.
- Document Q&A (RAG): `ollama run llama3.1:8b` with Open WebUI's RAG feature — best-supported combination.
What Is DeepSeek-R1 and Why Is It Different?
DeepSeek-R1 is a reasoning model — unlike standard chat models that generate answers directly, DeepSeek-R1 generates explicit chain-of-thought reasoning before its final answer. This significantly improves performance on math, logic puzzles, and step-by-step problem solving.
DeepSeek-R1 7B scores 52% on MATH (competition math) vs 28% for Mistral 7B at the same size. It is slower than standard models (more tokens per response) but significantly more accurate on tasks where reasoning matters.
# Pull and run DeepSeek-R1
ollama run deepseek-r1:7b
# Larger variants for better quality
ollama run deepseek-r1:14b # 10 GB RAM
ollama run deepseek-r1:32b # 20 GB RAMWhich Ollama Models Support Image Input?
As of April 2026, these models on Ollama support image input (multimodal):
| Model | RAM | Image Support | Ollama Command |
|---|---|---|---|
| llama3.2-vision:11b | ~8 GB | Yes | ollama run llama3.2-vision:11b |
| llama3.2-vision:90b | ~55 GB | Yes | ollama run llama3.2-vision:90b |
| gemma3:9b (vision) | ~6 GB | Yes | ollama run gemma3:9b |
| minicpm-v:8b | ~5.5 GB | Yes | ollama run minicpm-v |
Full Top 10 Open Source Ollama Models in 2026
| # | Model | Best For | RAM | HumanEval |
|---|---|---|---|---|
| 1 | llama3.2:3b | First model, general chat | 2.5 GB | 60% |
| 2 | llama3.1:8b | Quality general chat | 5.5 GB | 72% |
| 3 | qwen2.5:7b | Multilingual, coding | 4.7 GB | 72% |
| 4 | qwen2.5-coder:7b | Coding focus | 4.7 GB | 72% |
| 5 | deepseek-r1:7b | Reasoning, math | 5 GB | — |
| 6 | mistral:7b | EU use, efficient | 4.5 GB | 39% |
| 7 | mistral-small3.1 | Quality on 16 GB | 14 GB | 74% |
| 8 | gemma2:2b | Fast, low RAM | 1.7 GB | — |
| 9 | llama3.2-vision:11b | Image + text input | 8 GB | — |
| 10 | phi4-mini | Reasoning, 4 GB RAM | 2.5 GB | 70% |
How Do You Browse the Ollama Model Library?
The Ollama library is at ollama.com/library. Each model page shows available tags (size variants and quantizations), download counts, and supported capabilities.
# List all locally downloaded models
ollama list
# Search for a model and pull it
ollama pull qwen2.5-coder:32b
# See all available tags for a model
ollama show qwen2.5
# Remove a model to free disk space
ollama rm llama3.2:3bWhat Are the Common Mistakes When Choosing Ollama Models?
Pulling the largest model tag by default without checking RAM
Running `ollama pull llama3.3` without specifying a tag downloads the default variant, which is typically the largest standard quantization. On a machine with 8 GB RAM, pulling llama3.3 (70B at ~40 GB) will fail or cause severe swap usage. Always specify the variant: `ollama pull llama3.2:3b` for 8 GB machines.
Using a general model when a task-specific model exists
For coding tasks, `qwen2.5-coder:7b` scores 72% HumanEval while the general `qwen2.5:7b` also scores 72% — but `qwen2.5-coder` includes FIM support for code completion. For reasoning/math, `deepseek-r1:7b` scores 52% MATH vs 28% for `mistral:7b`. Task-specific models exist in the Ollama library for a reason.
Not verifying a model is available before building a workflow
The Ollama library changes over time — models are added and occasionally removed. Before building a production pipeline around a specific model, confirm it is in the library (`ollama list` locally, or check ollama.com/library). Pin specific model versions in production workflows: `ollama pull llama3.1:8b-instruct-q4_K_M`.
Common Questions About Open Source Models on Ollama
How many models are in the Ollama library?
As of April 2026, the Ollama library contains approximately 200+ curated models with official support. Hugging Face hosts thousands of additional GGUF models that can be loaded via Ollama using custom Modelfiles.
Can I use models from Hugging Face directly in Ollama?
Yes. Download a GGUF file from Hugging Face and create a Modelfile: `FROM ./model.gguf`. Then run `ollama create mymodel -f Modelfile`. This works for any GGUF file including fine-tunes and models not in the official Ollama library.
Which Ollama model is best for building a local chatbot?
For a general-purpose local chatbot: `llama3.1:8b` on 8 GB RAM machines, `mistral-small3.1` on 16 GB RAM. For a coding assistant chatbot: `qwen2.5-coder:7b`. Pair with Open WebUI for a web-based interface that connects to Ollama's API at localhost:11434.
Are all Ollama models truly open source?
Not all. The Ollama library includes models with varying licences. Llama 3.x uses the Meta Llama Community Licence (not OSI-approved open source — restricts commercial use above 700M monthly active users). Mistral 7B, Qwen2.5, and Gemma 3 are Apache 2.0 (fully open source). Always check the licence before commercial deployment.
Which embedding model should I use with Ollama for RAG?
`nomic-embed-text` is the standard choice — a 137M parameter model that generates 768-dimensional embeddings, runs at milliseconds per document, and is specifically designed for retrieval tasks. Pull it with `ollama pull nomic-embed-text`. Use with Open WebUI's built-in RAG, LangChain's OllamaEmbeddings, or LlamaIndex.
How often does the Ollama library get updated with new models?
The Ollama team adds new models within days to weeks of major releases. Meta Llama 3.3 appeared in the Ollama library within 3 days of its December 2025 release. Follow the Ollama GitHub repository (github.com/ollama/ollama) or the Ollama Twitter/X account for new model announcements.
Sources
- Ollama Model Library — ollama.com/library
- DeepSeek-R1 Technical Report — github.com/deepseek-ai/DeepSeek-R1
- Llama 3.2 Vision Model Card — huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct
- Ollama GitHub — github.com/ollama/ollama