How Many GitHub Stars Is Good? 2026 Benchmarks by Language and Age

October 18, 2026 · 14 min read

Ask five developers how many GitHub stars is good and you get five different answers, usually based on the last repository they admired. Most answers skip the part that matters: good compared to what?

The honest answer depends on three variables: the tier you are asking about, the language ecosystem the repository belongs to, and how old it is. So we measured all three. On 18 October 2026 we queried GitHub's search API for every public repository above a series of star thresholds, then repeated the query for the seven largest language ecosystems and for age cohorts.

A disclosure before the numbers: RepoBoost sells stars from aged accounts, so we benefit when a team decides to buy a starting base. That is exactly why the raw counts and query strings are below. Run them against GitHub yourself. A vendor's data deserves that much skepticism.

The short version: 100 stars is the first credibility threshold, and it puts a repository in the top 1.49% of all repositories with at least one star. 1,000 stars is top 0.20%. 10,000 stars is top 0.017%. 100,000 stars is 132 repositories in total.

What does the GitHub star distribution actually look like?

The distribution is more top-heavy than most people assume. Of the 32.6 million public repositories that have at least one star, only 8.9% reach 10, and fewer than 1.5% reach 100. Here is the full ladder, measured in one snapshot.

Two things stand out. First, single digits are the normal state of a GitHub repository, not a failure. Second, each tier is a filter, not a step. About 16.7% of repositories that reach 10 stars go on to reach 100. Then 13.5% of those reach 1,000. Only 8.6% of 1,000-star repositories reach 10,000. The higher you climb, the more the next tier is about distribution rather than code.

What does each tier actually mean?

10 stars is still invisible. It puts a repository ahead of about 91% of starred repositories, which says more about GitHub than about the project. Nobody browsing your category is impressed by 10 stars, but a repository with zero looks unmaintained next to one with ten.

100 stars is the first threshold that changes how strangers read a repository. It is the point where the question shifts from "does anyone use this?" to "does this fit my needs?". That is why 100 is the number most "how many stars is good" answers settle on. It is also where most repositories stop: 98.5% of starred repositories never get there.

1,000 stars is a strong signal in almost any category. It is top 0.20% of starred repositories, roughly one in 497. At that level a project is usually being cited in lists, comparison posts and internal team discussions. Only 13.5% of repositories with 100 stars ever cross this line.

10,000 stars is category-leader territory. There are 5,650 such repositories on GitHub, which is top 0.017%. In most language ecosystems the 10,000-star list is short enough to read in an afternoon.

100,000 stars is a different category of project. There are 132 of them. The 100th most-starred repository on GitHub has 118,357 stars. Past 10,000, the numbers stop being a marketing metric and start being a historical record.

The 100-star ceiling: 98.5% of starred repositories never reach it

This is the number to internalize. Of 32,621,661 repositories with at least one star, 484,723 have 100 or more. The other 32.1 million do not. Most of them are not bad projects. They are projects nobody saw, or projects that stopped before their first real audience arrived.

The good news is that the first tier is more accessible than it looks. About 23.6% of repositories with 100 or more stars were created in 2023 or later, which means a meaningful share of the 100-star class is young. A single well-prepared launch can move a repository from invisible to credible. The jump from 100 to 1,000 is where that stops being true.

Benchmarks by language: Python has the most, Go has the best odds

The language ecosystem changes the baseline. We ran the same thresholds for the seven largest language labels on GitHub. The first three columns are counts, the last two are conversion rates between tiers.

Python is the biggest pool by a wide margin: 96,497 repositories with 100 or more stars, and 1,039 with 10,000 or more. That volume is also the problem. More popular repositories means more competition for the same attention.

Go, TypeScript and Rust show the highest conversion from 100 to 1,000 stars: 18.6%, 17.5% and 17.1% respectively. Java and C++ convert the least at the top end. One plausible read is that newer or more focused ecosystems have a shorter path from "useful" to "widely used", while larger, older ecosystems have longer tails of abandoned projects. That is an interpretation, not a causal finding.

Two caveats matter. These counts only include repositories GitHub classifies with that primary language, so unlabelled and multi-language projects are excluded. And language is a proxy for ecosystem size and age, not a difficulty setting. Together these seven languages account for 39,899 of the 65,691 repositories with 1,000 or more stars, or 60.7%.

Does repository age still matter? More than the top end suggests

Age is the variable most people ignore, and the data is blunt about it. Of the 65,691 repositories with 1,000 or more stars, 61.5% were created before 2020. Another 20.4% were created in 2023 or later, and 9.9% in 2025 or later. Star counts are cumulative, so age helps mechanically: a repository that has existed for ten years has had ten years to be discovered.

The top of the distribution is different. Of the 132 repositories with 100,000 or more stars, 48 were created in 2023 or later. The 2025 to 2026 cohort includes several AI and developer-tooling repositories that reached six figures in months. That is real, but it is an outlier phenomenon, not a benchmark. The median creation date of the top 100 most-starred repositories is May 2019, and 38 of the top 100 were created in 2023 or later.

The practical takeaway: a young repository can reach 100 stars quickly, and a small minority can go much further, much faster. But planning around the outlier cohort is how teams convince themselves that a viral launch is the default. It is not. For every repository that reaches 100,000 in a year, there are thousands that took years to reach 1,000.

Why crossing 1,000 is the real filter

If 100 stars is credibility, 1,000 is distribution. The conversion rate from 100 to 1,000 is 13.5%, which means roughly six out of seven repositories that clear the first threshold never clear the second. Something changes at that boundary: the project needs repeated exposure, not one good launch.

The research supports the distinction between a count and a curve. A 2018 study of 791 developers found that stars behave more like public bookmarks than votes of confidence: people star projects they intend to return to, often without returning. The same work identified four growth patterns across popular repositories, from slow to viral, which is another way of saying most projects stay in the low-growth bucket. Later research on six million suspected fake stars found that purchased spikes help for less than two months and then become a liability. The number matters, but the shape of the growth is what people and ranking systems actually read.

That shape is why star velocity is a better diagnostic than total count. A repository at 400 stars growing by 15 a week is in a different situation from one at 900 stars that has not moved in three months. If you want the mechanics, we wrote about how star velocity feeds Trending and search. The short version: repeated, paced attention beats a single burst, and the burst can be actively harmful.

How to benchmark your own repository in five minutes

You do not need a dashboard. Four checks will tell you where you stand:

1. Place your count on the ladder. Find the highest tier you clear in the table above. The conversion rate to the next tier is your base rate, not your destiny.
2. Compare within your language. Re-run the query with language:YourLanguage. Doubling a Python repository from 100 to 1,000 puts you in a different pool than doing it in Rust.
3. Compare within your age cohort. A 100-star repository that is six months old is tracking differently from a 100-star repository that is five years old.
4. Measure velocity, not just total. Record stars once a week for four weeks. The slope is the honest signal about whether your distribution is working.

The queries are simple enough to run in a terminal. GitHub's search API reports a total count for each query:

# Every Python repository with 1,000 or more stars
curl -s "https://api.github.com/search/repositories?q=stars:%3E%3D1000+language:Python&per_page=1" | jq '.total_count'

# 1,000+ star repositories created in 2023 or later
curl -s "https://api.github.com/search/repositories?q=stars:%3E%3D1000+created:%3E%3D2023-01-01&per_page=1" | jq '.total_count'

# 100+ star repositories in your own language
curl -s "https://api.github.com/search/repositories?q=stars:%3E%3D100+language:TypeScript&per_page=1" | jq '.total_count'

Uncounted caveat: GitHub's totals are best-effort counts, and they move as repositories are deleted, renamed or starred. Use one snapshot date for comparisons, as we did here.

So what number should you actually aim for?

Targets should match the stage of the project, not a generic aspiration. These are descriptive ranges from the distribution, not promises about any individual repository.

If you are early, the target is not 10,000. It is the next tier plus a distribution plan that can repeat. A repository that reaches 100 stars from one launch and then goes quiet has learned nothing about whether it can reach 1,000. A repository that adds 10 to 20 stars a week from several channels is building the only curve that compounds.

The honest shortcut: can you buy past the filter?

Buying stars is the obvious question once you see how steep the ladder is. The research is not kind to the naïve version. The ICSE 2026 fake-star study found that purchased spikes produce a promotion effect for less than two months and become a burden after that, while leaving patterns that detection systems can read: new accounts, synchronized windows, activity that does not match normal use. A spike from accounts with no history is a signature, not a strategy.

A starting base can still make sense if the rest of the plan exists. The version that does not fight the data looks like this: accounts with real history, delivery spread across days rather than minutes, several signals growing together instead of one number jumping alone, and no claim that any of it is permanent. That is the only version we sell, and it is still not a substitute for distribution. The stars are the starting condition; the curve is the outcome.

RepoBoost at a glance

What does the research say about buying GitHub stars?

If you are considering paid growth, three studies are directly relevant, and it is worth reading them before you decide:

• Carnegie Mellon University, 2025. Researchers counted six million suspected fake stars on GitHub between July 2019 and December 2024. At the July 2024 peak, more than 16% of GitHub repositories were associated with fake-star campaigns. Most campaigns were tied to malicious repositories, and simple popularity boosts rarely lasted. Their underlying paper was accepted at ICSE 2026.
• ICSE 2026, He et al. The measurement study found fake-star activity surging in 2024, with highly trivial account patterns: empty profiles and groups starring in synchronized windows. Purchased stars promoted a repository for under two months and became a liability afterwards.
• Journal of Systems and Software, 2018, Borges and Valente. A survey of 791 developers found that three in four check a project's star count before using or contributing to it. The same work found four star-growth patterns across popular repositories and concluded that stars behave more like public bookmarks than votes of confidence.

What the research does not do matters too. These studies measure manipulation and perception; they do not evaluate paced delivery from aged accounts, and they make no promise about any ranking outcome. If you are weighing a purchase, read the safety guide first: buying stars is a risk decision, not a yes-or-no answer.

Use this dataset in your research or AI evaluation

The numbers behind this article are published as a versioned open dataset: github-star-benchmarks, release v1.0.0. It includes four CSVs, light and dark charts, a data dictionary, a CITATION.cff and a reproduce.py that reruns every query. Use it as a baseline for empirical software engineering or scientometrics, or as reference data to check whether an AI-generated answer about GitHub stars matches a dated public snapshot.

Data CC BY 4.0, code MIT. If you use it, cite: RepoBoost, GitHub Star Benchmarks (October 2026), https://github.com/repoboost-hq/github-star-benchmarks

FAQ

Methodology, data and sources

Snapshot: 18 October 2026. Source: GitHub REST search API (/search/repositories), reading total_count for each query. Tier queries: stars:>=N for N in 1, 10, 100, 1,000, 5,000, 10,000, 50,000 and 100,000. Language queries: language:X stars:>=N for Python, JavaScript, TypeScript, Java, C++, Go and Rust. Age queries: stars:>=N created:>=YYYY-MM-DD and created:<2020-01-01. Top-100 table: stars:>1000 sort=stars order=desc, first 100 results.

Caveats: GitHub's reported totals are approximate and change as repositories are created, deleted or renamed. Language labels only cover repositories GitHub classifies with that primary language. Conversion rates are ratios of these cohorts, not causal effects. Percentages are rounded; "1 in N" figures are derived from the same snapshot. The raw counts are in the CSV file that ships with this page's source, and the full snapshot plus the script that produced it are published in our open dataset: github-star-benchmarks. Re-run it rather than trusting us.

Sources

• GitHub REST API: Search repositories
• GitHub Docs: Saving repositories with stars
• Borges, Valente. What's in a GitHub Star? Journal of Systems and Software, 2018
• He et al. Six Million (Suspected) Fake Stars in GitHub. ICSE 2026
• RepoBoost open dataset: GitHub star benchmarks, October 2026

Reproducibility note: this article uses a single snapshot, taken on one day, with the exact queries listed above. If you re-run them, you will get slightly different numbers. That is the point of a live dataset: the method holds even when the counts move.