Mullama vs llama-cpp-python in 2026 — which to pick?

llama-cpp-python if you want the most direct, Python-native access to llama.cpp. Mullama if you want the same engine but with native bindings for 5 other languages (Rust, Node, Go, PHP, C) so your non-Python code can call the same model the same way.

Can I use the same model with both?

Yes. Both read GGUF files. A model that works in llama-cpp-python works in Mullama and vice versa.

They use the same underlying engine (llama.cpp) so throughput is essentially identical. Mullama's Rust layer is ~1-3% faster in some workloads because it has a thinner FFI boundary. llama-cpp-python's Python overhead is negligible for most use cases.

Which has more features?

llama-cpp-python is more Python-native (generators, context managers, OpenAI-compatible server). Mullama matches on the OpenAI-compatible server and adds Anthropic-compatible plus 5 more language bindings.

Mullama vs llama-cpp-python in 2026

Both give you Python access to llama.cpp. The difference is in the architecture: llama-cpp-python is Python-first with a C extension, Mullama is Rust-first with Python as one of six native bindings. Pick based on your team’s language mix.

Overview

	Mullama	llama-cpp-python
Primary language	Rust	Python (with C extension)
First release	2025-08	2023
Architecture	Rust + PyO3 + 5 other bindings	Python C extension
Install	`pip install mullama`	`pip install llama-cpp-python`
Python import	`from mullama import Model, Context`	`from llama_cpp import Llama`
GGUF support	✓	✓
In-process inference	✓	✓
HTTP server	✓ (built-in, OpenAI + Anthropic)	✓ (llama-cpp-python[server])
Streaming	✓	✓
Function calling	✓	✓
Grammar-constrained JSON	✓	✓
LoRA hot-swap	✓	✓
Vision	✓ (LLaVA, moondream)	✓ (llama-cpp-python + llava-cpp-python)
Audio / VAD	✓ (built-in)	✗
Multimodal (vision + audio)	✓	partial
License	MIT	MIT

Code comparison

Mullama (Python)

from mullama import Model, Context

# Load model
model = Model.load("llama3.2-3b.gguf", n_gpu_layers=32)
ctx = Context(model, n_ctx=4096)

# Generate
print(ctx.generate("Explain GGUF in one paragraph."))

llama-cpp-python

from llama_cpp import Llama

# Load model
llm = Llama(
    model_path="llama3.2-3b.gguf",
    n_gpu_layers=32,
    n_ctx=4096,
)

# Generate
output = llm("Explain GGUF in one paragraph.", max_tokens=256)
print(output["choices"][0]["text"])

OpenAI-compatible client (works with both)

# With Mullama running on :11434
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="not-needed")
print(client.chat.completions.create(
    model="llama3.2:3b",
    messages=[{"role": "user", "content": "Explain GGUF in one paragraph."}],
).choices[0].message.content)

When to use each

Choose Mullama when…

You have a polyglot team (Python + Rust + Node + Go + PHP + C in the same project).
You want a single runtime that all your services call, regardless of language.
You need Anthropic-compatible API in addition to OpenAI.
You want built-in audio / VAD for voice assistant use cases.
You are migrating from Ollama and want a drop-in replacement with the same port.

Choose llama-cpp-python when…

You are Python-only and want the most idiomatic Python API.
You need the longest track record (3+ years of stable releases, large community).
You are doing research with frequent API changes — llama-cpp-python is more permissive about breaking changes than Mullama.
You use LangChain or LlamaIndex’s llama-cpp-python integration (well-tested).

Performance

Same model, same hardware — both are within 3% of each other because they share the llama.cpp engine. Mullama has a slight edge in some workloads due to its thinner FFI layer. For most real applications, the difference is negligible.

Migration between them

llama-cpp-python → Mullama

# Before
from llama_cpp import Llama
llm = Llama(model_path="model.gguf", n_gpu_layers=32)
output = llm(prompt, max_tokens=256)

# After
from mullama import Model, Context
model = Model.load("model.gguf", n_gpu_layers=32)
ctx = Context(model)
output = ctx.generate(prompt, 256)

API is different but the concepts map 1:1. The OpenAI-compatible server is a drop-in replacement.

Mullama → llama-cpp-python

Reverse of the above. The HTTP API works the same; the in-process Python API is different.

Decision matrix

Your situation	Use
Python only, want idiomatic API	llama-cpp-python
Polyglot team, multiple languages	Mullama
Need Anthropic-compatible API	Mullama
Need voice / audio / VAD	Mullama
LangChain or LlamaIndex integration	llama-cpp-python
3+ years of stable release history needed	llama-cpp-python
Want drop-in Ollama replacement	Mullama
Doing research, frequent API changes	llama-cpp-python
Embedded in an Electron / Tauri app	Mullama (C ABI)
Production server with strict ops	Mullama
One-off Python script	llama-cpp-python

Mullama vs llama-cpp-python: Python LLM Bindings in 2026