Fastest Growing Coding Languages: Growth Data Compared

Planning to learn a coding language that is gaining momentum? The fastest growing coding languages show the strongest measured increases in developer activity, adoption or other defined indicators over a stated period. 

This article compares recent evidence, explains why rankings differ and clarifies what the statistics actually reveal. Programming Languages covers the broader concepts, classifications, uses and development ecosystem behind modern coding. 

This article focuses specifically on measurable growth and adoption trends. See our complete Programming Languages guide → see the full guide here.

Key Takeaways

• Rust recorded 162% growth in monthly participating accounts on GitHub from September 2025 to September 2026.

• TypeScript grew by 134% and Python by 118% on the same measure.

• TypeScript added 3.62 million monthly contributors, while Python added 3.23 million.

• Percentage growth and absolute contributor gains measure different things.

• Survey responses, repository activity and hiring demand should not be treated as interchangeable evidence.

• A credible ranking needs a clear metric, reporting period, source and limitations.

What Counts as a Fastest Growing Coding Language?

A fastest growing coding language is one that records a strong increase in a clearly defined indicator over a specified period. That indicator might be developer contributions, repository activity, survey participation or another measurable signal.

There is no single universal ranking because research organisations measure different aspects of programming activity. A defensible comparison identifies the metric, baseline, timeframe and original source before naming a leader.

How Is Language Growth Different From Popularity?

Popularity describes an existing level of use or interest while growth measures how that level changes over time. A widely used language can remain popular while growing more slowly than a smaller language.

For example GitHub's 2026 Octoverse report reports that Rust doubled over the preceding year among its leading languages, making it the fastest growing by percentage in that group. 

This is evidence of rapid growth within GitHub's ecosystem not proof that Rust has the largest user base overall.

Which Growth Metrics Can Be Compared Fairly?

Four useful measures are:

• Percentage growth: The relative increase against a starting value.

• Absolute growth: The numerical increase in contributors, users or repositories.

• Year over year growth: The change compared with the corresponding period a year earlier.

• Multi year growth: The change across a longer interval which can reveal sustained development.

Compare percentages directly only when their definitions and observation periods are sufficiently consistent. Repository growth for example should not be ranked as though it were equivalent to growth in individual developers using a language.

Which Coding Languages Show the Strongest Growth in Recent Data?

Recent GitHub data identifies Rust as the fastest growing language by percentage among its leading languages while TypeScript and Python recorded the largest absolute contributor gains in the previous annual comparison. 

These are distinct findings so the ranking depends on whether you prioritise relative or absolute growth. The latest evidence available at the time of writing includes GitHub's September 2025 September 2026 comparison and its 2025 annual language figures. 

Each should retain its own measurement period rather than being combined into one artificial league table.

TypeScript and Rust: Evidence of Growing Developer Adoption

TypeScript showed substantial growth in GitHub's 2025 figures. GitHub reported more than one million additional contributors and approximately 66% year over year growth comparing August 2025 with August 2024.

Rust stands out in the newer 2026 report which says its contributor count doubled over the preceding year and identifies it as the fastest growing language by percentage among the leading languages tracked. 

These findings come from different reporting periods, so they should not be presented as a direct head to head contest.

Go, Kotlin, Dart and Other Evidence Backed Candidates

Go, Kotlin and Dart are worth monitoring but their presence in developer surveys does not automatically establish that they are among the fastest growing languages.

Python offers stronger documented evidence in GitHub's 2025 comparison it added roughly 850,000 contributors an increase of about 48% year over year. 

JavaScript added approximately 427,000 contributors growing by about 25%. These figures demonstrate that established languages can continue expanding even when another language grows faster by percentage.

How Do the Leading Coding Languages Compare by Growth?

GitHub's 2026 Octoverse report provides a useful like for like comparison of annual growth in monthly participating accounts. 

From September 2025 to September 2026 Rust grew fastest by percentage while TypeScript recorded the largest absolute contributor gain among the three leading languages highlighted below.

GitHub Octoverse 2026. The percentage figures measure growth in monthly participating accounts; contributor gains are absolute changes. These figures should not be treated as interchangeable rankings.

The table illustrates why both measures matter. Rust leads by relative growth while TypeScript adds more contributors in absolute terms. These findings describe activity within GitHub not the entire global programming market.

What Evidence Explains Why These Languages Are Growing?

Growth figures establish what changed but they do not prove why it changed. GitHub's 2026 report connects strong language community growth with activity involving AI assisted development although this association alone cannot establish that AI caused the increases.

The report identifies TypeScript, Python and Rust among the fastest growing communities and finds substantial AI involvement in their merged pull requests. These findings provide context for the growth patterns not a complete explanation of every developer's language choice.

How Do Ecosystem Activity and Developer Interest Affect Growth?

Repository contributions and monthly participating accounts can reveal increasing activity within an ecosystem. Developer surveys provide a different signal by recording which languages respondents use or want to use.

For example the Stack Overflow Developer Survey 2026 records responses from 14,099 participants on programming, scripting and markup languages. It reports that 43.8% used TypeScript and 18.0% used Rust. 

These figures describe reported use among survey respondents not year over year growth rates.

Does Rising Employer Demand Prove a Language Is Growing Fastest?

No Job advertisements and recruiter surveys measure hiring demand whereas repository activity and developer surveys measure different forms of participation or reported use.

A language may attract more vacancies because employers need people to maintain existing systems even if its developer community is growing slowly. 

Conversely a language may expand rapidly in open source projects without immediately producing a similar increase in job advertisements.

Hiring evidence can support an assessment of industry relevance but it should not replace the primary growth metric. A defensible ranking must state which kind of growth it measures.

Why Do Programming Language Growth Rankings Differ?

Rankings differ because their publishers use different populations, measurement methods and observation periods. A language may lead a repository based growth comparison while another tops a survey of desired technologies.

Neither result necessarily contradicts the other. The rankings answer different questions so readers should examine the underlying evidence before deciding which finding is most relevant.

What Is the Difference Between GitHub Activity, Surveys and Popularity Indexes?

GitHub measures activity within its own platform including contributions to repositories. Developer surveys record respondents' reported behaviour or preferences while popularity indexes use their own selected signals and weighting methods.

For instance GitHub's 2026 methodology counts distinct accounts participating through pushes, pull requests, issues, reviews or comments and attributes them to a repository's primary language. An account can count in several languages.

These methods do not measure identical populations. Comparing their results requires explaining what each source counts rather than combining their rankings into one supposedly universal order.

Can a Smaller Language Grow Faster Than an Established Language?

Yes Percentage growth depends on the starting value so a smaller community can record a higher growth rate while adding fewer participants overall.

Consider this hypothetical example:

• A language growing from 100 to 150 participants records 50% growth.

• A language growing from 10,000 to 11,000 participants records 10% growth.

The first language has the higher percentage increase but the second adds 900 more participants. Both measurements are useful neither alone provides a complete picture of adoption.

How Reliable Are the Available Language Growth Statistics?

A growth statistic is only as reliable as its source, methodology and reporting context. Prioritise original reports with clear definitions, dated observation periods and transparent calculations.

GitHub's 2026 report provides a defined annual window and explains how it counts participating accounts. Its findings are useful for understanding GitHub activity but they should not be presented as a complete measurement of global programming language adoption.

What Should Readers Check Before Trusting a Growth Percentage?

Before accepting a reported figure check:

• Publication date: When did the source release the results?

• Observation period: Which dates does the comparison cover?

• Baseline: What starting value determines the percentage?

• Population: Which developers, repositories or survey respondents are included?

• Calculation method: Does the source measure relative growth, absolute growth or another indicator?

• Limitations: Are sampling gaps, platform coverage or methodological changes disclosed?

Original reports should take priority over secondary summaries. Where sample sizes are available include them when they help readers understand the strength and coverage of the evidence.

How Should Conflicting or Incomplete Data Be Reported?

Report each source's findings using its own terminology. If GitHub reports contributor growth while a survey reports the percentage of respondents using a language present them as separate indicators.

Do not invent missing percentages or infer growth from a single popularity ranking. When evidence is incomplete identify the limitation and avoid making a stronger claim than the source supports.

How Should You Interpret the Fastest Growing Languages List?

Treat this list as a comparison of documented growth signals rather than a universal verdict on which languages are best. The strongest conclusion depends on the metric, reporting period, source coverage and quality of evidence.

GitHub's latest figures support Rust as the leader by percentage growth among the top languages it tracks with TypeScript and Python showing substantial growth in both participation and absolute contributor numbers. 

The conclusion applies to GitHub's measured ecosystem, not every developer or employer worldwide.

Which Languages Qualify for a Defensible Growth Ranking?

A language should qualify only when its reported growth is attributable to a credible source, tied to a defined period and supported by a clear metric. The ranking should also distinguish percentage growth from absolute increases. 

When two sources measure incompatible signals, group their findings separately instead of forcing them into a single order.

Under these criteria, Rust, TypeScript and Python have particularly strong recent evidence in GitHub's 2026 report. Other candidates should be added only when comparable, attributable data supports their inclusion.

What Can Growth Statistics Tell Us About Emerging Programming Languages?

Rising activity can signal increasing developer interest or expanding participation but it does not guarantee sustained growth. A short term increase may reflect a specific platform, changing project practices or a temporary surge in activity.

Emerging programming languages should therefore be assessed through repeated measurements, transparent source methods and evidence across time. Forecasts can provide context but they remain estimates rather than confirmed outcomes.

Conclusion

GitHub's 2026 data identifies Rust as the fastest growing language by percentage among its leading languages while TypeScript and Python recorded larger absolute contributor gains. These results show why relative and absolute growth should be assessed separately.

The most defensible answer comes from dated, transparent evidence and a clearly defined measurement method not a single popularity ranking or unsupported prediction.

FAQs

What Are the Fastest Growing Coding Languages Right Now?

GitHub's 2026 data highlights Rust, TypeScript and Python with Rust leading by percentage growth.

How Is the Growth of a Programming Language Measured?

Growth is measured through percentage change, contributor increases, repository activity or survey results over a defined period.

Is the Fastest-Growing Language Also the Most Popular?

No Growth measures change over time while popularity measures existing use or interest.

Why Do Different Programming Language Rankings Disagree?

Rankings use different data sources, metrics, populations and reporting periods.

Does GitHub Activity Prove That a Coding Language Is Growing?

It shows activity within GitHub but does not prove global adoption.

Can a Language With Fewer Users Have a Higher Growth Rate?

Yes A smaller starting baseline can produce a higher percentage growth rate.