Comparisons

Head-to-head matchups to help you choose the right model or tool

Can a free, open-weight model really compete with GPT-4? DeepSeek R1 challenges OpenAI's flagship on reasoning benchmarks — and you can run it locally.

Reasoning & Math: Tie General Knowledge: GPT-4 Cost: GPT-4

DeepSeek R1 70B VS Llama 3.3 70B

Two of the best open-weight 70B models compared. DeepSeek R1 brings chain-of-thought reasoning while Llama 3.3 offers balanced general intelligence.

Reasoning & Math: DeepSeek R1 70B General Chat: Llama 3.3 70B Coding: Tie

Gemma 2 9B VS Llama 3.2 3B

Google vs Meta in the small model arena. Gemma 2 offers research-grade quality while Llama 3.2 brings Meta's scale and community support.

Quality: Gemma 2 9B Resource Usage: Llama 3.2 3B Community & Ecosystem: Llama 3.2 3B

GPT4All VS Ollama

Two different approaches to local AI: GPT4All's offline-first desktop app vs Ollama's developer-friendly CLI. Which is right for you?

Ease of Use: GPT4All Developer Experience: Ollama Model Selection: Ollama

Jan VS LM Studio

Two polished desktop apps for running AI locally. Jan is the open-source newcomer; LM Studio is the established player. Which desktop experience wins?

User Interface: Tie Open Source: Jan Model Discovery: LM Studio

Mistral 7B VS Llama 3.2 3B

Two popular small models compared: Mistral's efficient 7B vs Meta's tiny-but-capable 3B. Which small model should you run locally?

Quality: Mistral 7B Speed: Llama 3.2 3B Resource Usage: Llama 3.2 3B

Ollama VS llama.cpp

Ollama is built on llama.cpp but adds model management and an API layer. Compare the user-friendly wrapper vs the raw inference engine.

Ease of Use: Ollama Performance Control: llama.cpp Flexibility: llama.cpp

Ollama VS LM Studio

Compare the two most popular ways to run AI models locally. Ollama offers CLI simplicity and API-first design, while LM Studio provides a polished desktop experience.

Ease of Setup: Ollama User Interface: LM Studio API & Integration: Ollama

Phi-4 VS Llama 3.2 3B

Microsoft's STEM-focused 14B vs Meta's lightweight 3B. Two different philosophies on small, efficient language models.

Quality: Phi-4 Efficiency: Llama 3.2 3B Math & STEM: Phi-4

Qwen 2.5 72B VS DeepSeek R1 70B

Two Chinese AI labs go head-to-head. Qwen 2.5's balanced capabilities vs DeepSeek R1's reasoning specialization — which open model wins?

Coding: Qwen 2.5 72B Reasoning: DeepSeek R1 70B Multilingual: Qwen 2.5 72B