Local LLMs 204: Why the Same Model Is Not Always the Same

Sun, 31 May 2026 00:00:00 +0000

You may see the same model name in Ollama, Hugging Face, LM Studio, OpenRouter, a benchmark table, and a coding tool. Then you try it in two places and it behaves differently.

This is normal. “Same model” often hides several layers of difference.

Mental model: name, checkpoint, format, runtime, provider

A model name is only the label at the top of the stack. Behavior comes from the whole stack:

Layer	Example difference
Family	Qwen, Llama, Gemma, Mistral, DeepSeek, Phi
Checkpoint	Base, instruct, coder, reasoning, distilled, updated release
File format	GGUF, safetensors, runtime-specific package
Quantization	Q4, Q5, Q8, full precision
Runtime	Ollama, llama.cpp, vLLM, MLX, transformers
Prompt template	ChatML, model-specific instruct format, custom system prompt
Provider	Local machine, OpenRouter route, direct vendor API
Parameters	Temperature, top_p, max tokens, reasoning settings, stop sequences

The "Same Model" Stack

A model name is just the top layer.

Local LLMs 203: Choosing a Model Without Guessing

Sat, 30 May 2026 00:00:00 +0000

Local model choice can turn into name collecting very quickly. Llama, Qwen, Gemma, Mistral, DeepSeek, Phi, gpt-oss, coder models, reasoning models, small models, giant models, quantized models, preview models: it is a lot.

The goal is not to memorize every release. The goal is to choose a model with a reason.

Mental model: model choice is task fit plus hardware fit

A useful model is the intersection of three things:

AI Models on Mohammad Movahedi

Local LLMs 204: Why the Same Model Is Not Always the Same

Mental model: name, checkpoint, format, runtime, provider

The "Same Model" Stack

Local LLMs 203: Choosing a Model Without Guessing

Mental model: model choice is task fit plus hardware fit