# Gin vs Echo: source lines of code compared

The Gin vs Echo comparison on OctoCounts shows side-by-side source line counts for gin-gonic/gin and labstack/echo, 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 2026-10-11, gin-gonic/gin contains 26,078 total lines (18,311 code) across 112 files, while labstack/echo contains 46,734 total lines (35,229 code) across 127 files as of 2026-10-07. labstack/echo is about 1.9x the size of gin-gonic/gin by code lines. Code size is not code quality: a larger count only means more source material, not a better or worse project.

| Metric | [gin-gonic/gin](https://octocounts.com/github/gin-gonic/gin) | [labstack/echo](https://octocounts.com/github/labstack/echo) |
| --- | ---: | ---: |
| Files | 112 | 127 |
| Total lines | 26,078 | 46,734 |
| Code lines | 18,311 | 35,229 |
| Comment lines | 3,805 | 6,067 |
| Blank lines | 3,962 | 5,438 |
| Languages counted | 7 | 7 |

Top languages in gin-gonic/gin: Go (99.4% of code), Makefile (0.5% of code), YAML (0.1% of code). Top languages in labstack/echo: Go (99.6% of code), JSON (0.3% of code), Makefile (0.1% of code). Go, Makefile, YAML appear in both top language lists; Protocol Buffers, Templ appear only in gin-gonic/gin's top languages; JSON, HTML appear only in labstack/echo's top languages.

Methodology: both counts come from cached OctoCounts reports generated with tokei. gin-gonic/gin was counted at ref master (commit 0f09c3a9b462) on 2026-10-11; labstack/echo was counted at ref master (commit 3882266a3641) on 2026-10-07. See the [counting methodology](https://octocounts.com/docs/methodology) for ignored directories and analysis options.
## About this comparison

gin-gonic/gin counts the Gin repository: the Go web framework, its router and middleware stack, tests, and examples. labstack/echo counts the Echo repository: the Go web framework with its server, middleware, and test suites. Both are single-language Go framework trees — among the more directly comparable pairs in this registry — counted with the same engine on the dates above.

The per-language tables on both pages are essentially pure Go, so unlike most pairs on this site there is no language-mix asymmetry to account for when reading the totals.

Both repositories include extensive benchmark, example, and test code alongside the framework core; the code/comment/test breakdown above shows how much of each count exists to demonstrate and verify routing behavior rather than to execute in production apps.

_Framework source size does not predict request throughput, latency, or memory use for your handlers — those depend on routing and middleware design and on application code, none of which is measured here._

Sources: [gin-gonic/gin repository](https://github.com/gin-gonic/gin) · [labstack/echo repository](https://github.com/labstack/echo). Statements verified 2026-09-16.

Evidence and next steps:

- [gin-gonic/gin SLOC report](https://octocounts.com/github/gin-gonic/gin)
- [labstack/echo SLOC report](https://octocounts.com/github/labstack/echo)
- [Compare gin-gonic/gin and labstack/echo interactively](https://octocounts.com/compare?left=https%3A%2F%2Fgithub.com%2Fgin-gonic%2Fgin&right=https%3A%2F%2Fgithub.com%2Flabstack%2Fecho)

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, gin-gonic/gin or labstack/echo?

labstack/echo has more code: 35,229 code lines versus 18,311 for gin-gonic/gin, about 1.9x as much, based on cached OctoCounts reports as of 2026-10-11.

### 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 gin-gonic/gin, labstack/echo, or any repository.

### How is this comparison calculated?

Methodology: both counts come from cached OctoCounts reports generated with tokei. gin-gonic/gin was counted at ref master (commit 0f09c3a9b462) on 2026-10-11; labstack/echo was counted at ref master (commit 3882266a3641) on 2026-10-07. 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)