# Ollama vs llama.cpp: source lines of code compared

The Ollama vs llama.cpp comparison on OctoCounts shows side-by-side source line counts for ollama/ollama and ggml-org/llama.cpp, counted with the same engine and analysis options on the dates shown in the methodology line: files, total lines, code lines, comment lines, blank lines, and per-language totals, pinned to the exact commits listed below.

As of 2026-09-12, ollama/ollama contains 763,172 total lines (504,127 code) across 1,079 files, while ggml-org/llama.cpp contains 934,298 total lines (722,120 code) across 2,924 files as of 2026-09-14. ggml-org/llama.cpp is about 1.4x the size of ollama/ollama by code lines. Code size is not code quality: a larger count only means more source material, not a better or worse project.

| Metric | [ollama/ollama](https://octocounts.com/github/ollama/ollama) | [ggml-org/llama.cpp](https://octocounts.com/github/ggml-org/llama.cpp) |
| --- | ---: | ---: |
| Files | 1,079 | 2,924 |
| Total lines | 763,172 | 934,298 |
| Code lines | 504,127 | 722,120 |
| Comment lines | 184,567 | 73,304 |
| Blank lines | 74,478 | 138,874 |
| Languages counted | 12 | 12 |

Top languages in ollama/ollama: Go (53.3% of code), JSON (28.7% of code), C Header (11.2% of code). Top languages in ggml-org/llama.cpp: C++ (43.6% of code), C (8.3% of code), Python (6.7% of code). C Header appears in both top language lists; Go, JSON, TSX, TypeScript appear only in ollama/ollama's top languages; C++, C, Python, OpenCL appear only in ggml-org/llama.cpp's top languages.

Methodology: both counts come from cached OctoCounts reports generated with tokei. ollama/ollama was counted at ref main (commit 53fed2611281) on 2026-09-12; ggml-org/llama.cpp was counted at ref 7cf1c54a96d4e950ffa614b94babf762803a8de7 (commit 7cf1c54a96d4) on 2026-09-14. See the [counting methodology](https://octocounts.com/docs/methodology) for ignored directories and analysis options.
## About this comparison

ollama/ollama counts the Ollama repository: the Go application that packages and serves local language models, built on top of the llama.cpp inference engine (a separate repository, not part of this count). ggml-org/llama.cpp counts the llama.cpp repository — the C/C++ inference engine, its build system, examples, and language bindings. The comparison is therefore an application-repository vs engine-repository: adjacent layers, counted on the dates above.

The per-language tables show the layer split directly: Ollama's counted code is predominantly Go, while llama.cpp's counted code is C and C++ — an application written in one language on top of a native engine written in another.

The llama.cpp tree includes example programs and bindings for many languages in the same repository, so a visible share of its count exists to demonstrate and expose the engine rather than to run inference itself.

_These two projects are not rivals in the counted code: Ollama builds on llama.cpp, so part of what Ollama does at runtime is implemented in the repository counted on the right. Also, neither count includes model weights, which dominate any real deployment's disk footprint._

Sources: [ollama/ollama repository (Go application for running local models)](https://github.com/ollama/ollama) · [ggml-org/llama.cpp repository (C/C++ inference engine)](https://github.com/ggml-org/llama.cpp). Statements verified 2026-09-16.

Evidence and next steps:

- [ollama/ollama SLOC report](https://octocounts.com/github/ollama/ollama)
- [ggml-org/llama.cpp SLOC report](https://octocounts.com/github/ggml-org/llama.cpp)
- [Compare ollama/ollama and ggml-org/llama.cpp interactively](https://octocounts.com/compare?left=https%3A%2F%2Fgithub.com%2Follama%2Follama&right=https%3A%2F%2Fgithub.com%2Fggml-org%2Fllama.cpp)

Note: code size is not code quality. OctoCounts only reports reproducible line counts and makes no claim that either project is better.

## Compare FAQ

### Which has more lines of code, ollama/ollama or ggml-org/llama.cpp?

ggml-org/llama.cpp has more code: 722,120 code lines versus 504,127 for ollama/ollama, about 1.4x as much, based on cached OctoCounts reports as of 2026-09-14.

### Does more source lines of code mean more complexity?

Not necessarily. SLOC measures size, not complexity, quality, or maintainability. A larger codebase can mean more features, more generated or vendored code, more verbose language idioms, or more tests — none of which imply the code is harder to work with. Use SLOC to gauge the scale of what you'd be reading or maintaining, not as a quality signal for ollama/ollama, ggml-org/llama.cpp, or any repository.

### How is this comparison calculated?

Methodology: both counts come from cached OctoCounts reports generated with tokei. ollama/ollama was counted at ref main (commit 53fed2611281) on 2026-09-12; ggml-org/llama.cpp was counted at ref 7cf1c54a96d4e950ffa614b94babf762803a8de7 (commit 7cf1c54a96d4) on 2026-09-14. See the counting methodology for ignored directories and analysis options.

### Can I compare a different branch, tag, or commit?

Yes. This page shows the default branch for each repository. Use the interactive comparison tool to pick any public GitHub repository, branch, tag, or commit SHA for both sides and get a fresh side-by-side report.

## Related OctoCounts pages

- [Interactive repository comparison](https://octocounts.com/compare)
- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [OctoCounts API docs](https://octocounts.com/docs/api)