This page compares the source lines of code (SLOC) of ollama/ollama and ggml-org/llama.cpp using cached OctoCounts reports. Code size is not code quality: a larger count only means more source material, not a better or worse project.
As of 2026-08-26, ollama/ollama contains 759,946 total lines (500,593 code) across 1,076 files, while ggml-org/llama.cpp contains 912,569 total lines (704,348 code) across 2,863 files as of 2026-09-05. 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 | ggml-org/llama.cpp |
|---|---|---|
| Files | 1,076 | 2,863 |
| Total lines | 759,946 | 912,569 |
| Code lines | 500,593 | 704,348 |
| Comment lines | 184,281 | 72,044 |
| Blank lines | 75,072 | 136,177 |
| Languages counted | 12 | 12 |
Top languages in ollama/ollama: Go (54.1% of code), JSON (28.9% of code), C Header (11.1% of code). Top languages in ggml-org/llama.cpp: C++ (43.6% of code), C (8.2% of code), Python (6.8% 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 a773b7affa14) on 2026-08-26; ggml-org/llama.cpp was counted at ref b96806d96061049a5b574269b049bf6241d63d46 (commit b96806d96061) on 2026-09-05. See the counting methodology for ignored directories and analysis options.
Evidence and next steps:
Note: code size is not code quality. OctoCounts only reports reproducible line counts and makes no claim that either project is better.
ggml-org/llama.cpp has more code: 704,348 code lines versus 500,593 for ollama/ollama, about 1.4x as much, based on cached OctoCounts reports as of 2026-09-05.
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.
Methodology: both counts come from cached OctoCounts reports generated with tokei. ollama/ollama was counted at ref main (commit a773b7affa14) on 2026-08-26; ggml-org/llama.cpp was counted at ref b96806d96061049a5b574269b049bf6241d63d46 (commit b96806d96061) on 2026-09-05. See the counting methodology for ignored directories and analysis options.
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.