Excel vs Google Sheets in 2026: An Honest, Hands-On Comparison

By the XLsheetAI Team · Updated August 7, 2026 · 10 min read

The short answer, by use case

If your work involves building models, joining data from several systems, or handling files with hundreds of thousands of rows, use Excel. If your work involves many people touching the same sheet, live external data, and getting a link into someone's hands in ten seconds, use Google Sheets. Almost every honest answer follows from that split.

The tempting move is to declare a winner. We will not, because the two stopped competing on the same axis years ago. Microsoft kept building a data platform that happens to have a grid on the front. Google kept building a collaboration surface that happens to calculate. Both are now very good at their own thing and merely adequate at the other's. What follows is the detail, with numbers where we could verify them and plain language where we could not.

Collaboration: two models, not one better product

Google Sheets was built multiplayer and it shows. Edits appear character by character, there is no file to save, no lock, no reconciliation. Excel co-authoring works, but it is a layer added on top of a file-based application, and the difference surfaces whenever the network wobbles or someone opens the file in the desktop app.

Version history is the sharper contrast. Sheets keeps a continuous named-and-timestamped history you can scrub through, restore, and attribute to a person, with no configuration. Excel's version history depends on where the file sits: put it in OneDrive or SharePoint and you get a usable history; email it around and you get eight files named budget_final_v3_JS_edit.xlsx. That second failure mode is not Excel's fault, but it is Excel's reality in most organisations.

Sharing follows the same pattern. A Sheets link with edit rights takes one click and works for anyone with a Google account or, if you allow it, without one. Excel sharing is more granular and more governable, with tenant policies, expiry, and sensitivity labels, which is exactly what a regulated company wants and exactly what a five-person startup finds tedious. One honest caveat: modern comments in Microsoft 365 have narrowed the commenting gap considerably. Collaboration is where Sheets leads, not where it is untouchable.

Raw power: where Excel is still uncatchable

Excel's advantage is not the formula language, which Sheets matches closely. It is the three-layer stack underneath: Power Query for repeatable data cleaning, Power Pivot and the Data Model for relational tables and DAX measures, and VBA for anything the interface cannot do. Google Sheets has no equivalent to any of the three.

Power Query is the one most people underrate. It turns a weekly hour of manual cleanup into a refresh button, records every transformation step as reviewable, editable code, and re-applies them to next month's file. Once a team has one Power Query pipeline it trusts, migrating away from Excel becomes very hard, and rightly so.

Power Pivot is the harder ceiling. It lets one pivot table draw on several related tables at once, holds far more rows than the visible grid, and supports DAX measures that behave correctly under filtering. One caveat before you plan around it: Power Pivot and the Data Model are Windows desktop features, absent from Excel for Mac and Excel for the web. Power Query reached Excel for Mac in version 16.69, but with fewer data sources and no way to load results into the Data Model.

Pivot tables themselves are close. Sheets handles a single flat table well, with calculated fields and sensible grouping. Excel pulls ahead on slicers, timelines, multiple-table sources, and the newer GROUPBY and PIVOTBY functions, which put aggregation into a formula that recalculates automatically. Sheets has no equivalent to those two.

Scale and performance: the numbers, verified

Excel gives every worksheet 1,048,576 rows and 16,384 columns, a fixed grid since the .xlsx format arrived. Google caps a whole spreadsheet at 10 million cells or 18,278 columns. That cap was raised from 5 million cells in 2022, and it is the number that decides most real comparisons.

The unit matters. Excel counts rows per sheet; Google counts cells across the file. A 20-column table in Sheets therefore runs out at roughly 500,000 rows, and a 40-column export dies around 250,000. Teams that read "10 million" as "10 million rows" get an unpleasant surprise on their first real dataset.

Both slow down well before their ceilings, and they slow down differently. Excel on a decent laptop stays responsive into the hundreds of thousands of rows and degrades gradually, and you can throw hardware at it. Sheets degrades in the browser, where you cannot, and a few thousand IMPORTRANGE or GOOGLEFINANCE calls will make a much smaller file feel sluggish. If a Sheet is slow, the fix is usually fewer live functions, not fewer rows.

For genuinely large data, neither should be the destination. Excel with Power Query and the Data Model acts as a front end over millions of rows it never loads into the grid. Sheets answers with Connected Sheets over BigQuery, which is excellent if you already have BigQuery and irrelevant if you do not.

Formula differences that actually matter

The core function library overlaps almost entirely, and shared modern functions like XLOOKUP, LAMBDA, and LET have narrowed the gap further. The differences that still bite are structural: how each handles arrays, and a small set of functions with no counterpart on the other side.

Arrays are the first divergence. Excel in Microsoft 365 spills dynamic array results automatically, so =UNIQUE(A2:A500) simply fills the cells below. Sheets has always been array-native internally, but many formulas need an explicit ARRAYFORMULA() wrapper to operate down a column instead of on one row. Neither approach is wrong; both trip up people arriving from the other side.

The second divergence is genuinely one-sided. Sheets has QUERY, an SQL-like function with no Excel equivalent, and it is the single function most likely to make an Excel user envious. It also has a family of live-data functions, IMPORTRANGE, IMPORTHTML, IMPORTXML, and GOOGLEFINANCE, that pull from other sheets, web pages, and market data with one line. Excel counters with GROUPBY, PIVOTBY, and Python cells. If you are still mapping lookup functions across both, our XLOOKUP vs VLOOKUP guide covers syntax that behaves identically in each, and the Excel formulas cheat sheet lists the shared essentials.

Writing for both platforms? XLsheetAI generates formulas in Excel and Google Sheets syntax from a plain-English description, so you get the right version for whichever file you are actually in, plus an explanation of why it works.

Automation and scripting

Both platforms automate well, in opposite directions. VBA runs on your machine against the open workbook and can drive other desktop applications; Apps Script runs on Google's servers, deploys instantly to everyone, and reaches the rest of Workspace. Excel's newer Office Scripts sit in between: TypeScript, cloud-run, but limited to the web and Microsoft 365 files.

VBA's strengths are that it is thirty years old and it is local. There is a forum answer for almost anything, the macro recorder still teaches beginners more than any tutorial, and a macro can touch the file system, Outlook, or a database driver. Its weaknesses are the same age: an ageing editor, no native version control, and a security posture that means many organisations now block macros from the internet by default.

Apps Script is the better modern development experience and wins outright on distribution. Attach a script to a Sheet and every collaborator gets it; add a time-based trigger and it runs whether anyone opens the file or not, which VBA fundamentally cannot do. The constraint is quota: executions are capped at around six minutes each, with longer limits for some Workspace accounts, and total trigger runtime is capped per day. Long jobs must be chunked, and no support ticket will lift the ceiling.

Practical rule: if your automation manipulates one big file on one person's machine, VBA. If it moves data between systems on a schedule, Apps Script. If it needs to run for an hour, neither, and you should be looking at a real backend.

AI in 2026: what Copilot and Gemini genuinely do

Both are now meaningfully useful and both are assistive rather than autonomous. Copilot in Excel gained agent mode and Python-powered advanced analysis in the April 2026 update, letting it plan a multi-step task, edit the workbook directly, and insert refreshable Python cells. Gemini in Sheets was rebuilt in April 2026 to create and edit whole spreadsheets from natural-language prompts.

Copilot's Excel story is the more technically ambitious one. Edit-with-Copilot applies changes into the workbook rather than only suggesting them in chat, a Plan mode maps the steps before it acts, and advanced analysis opens a new sheet with Python code you can inspect and re-run. That last part is the important detail: the output is auditable code, not a black-box answer, which is the only version of AI analysis a finance team can sign off on.

Gemini in Sheets is broader and shallower. It builds tables, charts, pivot tables, and formulas from a description, cleans and structures messy data, and fills columns from context across your Workspace files. Google ran promotional access to the improved experience through July 15, 2026, after which per-user usage limits apply, so plan for a cap rather than unlimited use.

The realistic assessment from using both: excellent at the blank-page problem and at formula syntax, decent at describing what a dataset contains, not yet trustworthy without review on anything with a number in it that matters. Treat either as a fast junior analyst whose work you check and both earn their keep. Treat either as a substitute for understanding your own data and you will publish something wrong.

Offline and mobile

Excel is better offline, Sheets is better on a phone. Excel's desktop app has no concept of needing a network, which still matters on planes, building sites, and bad hotel wifi. Sheets offline works, but through a browser extension in Chrome or on a Chromebook, and it is a fallback rather than a first-class mode.

Mobile flips it. The Google Sheets app is a genuinely usable editor for what people do on phones: check a number, update a status, add a row, leave a comment. Excel's mobile app is a more faithful port of a desktop application, which sounds better and is worse, because the desktop interaction model does not survive a five-inch screen. For a field sales team or a warehouse, the question is not which product has Power Pivot. It is which one someone will update from a phone in a car park, and the answer is Sheets.

Price and licensing

At US list prices the two suites now line up almost exactly. Microsoft 365 Business Basic, Standard, and Premium sit at $7, $14, and $22 per user per month on annual terms after the July 1, 2026 increase. Google Workspace Business Starter, Standard, and Plus sit at the same $7, $14, and $22 on annual commitment.

Two structural differences hide behind that symmetry. First, desktop apps: Microsoft 365 Business Basic at $7 gives web and mobile Excel only, so the fair comparison for anyone wanting installed Excel starts at Business Standard, $14, or Apps for Business at $10 without the collaboration suite. Google has no desktop app to withhold, so its $7 tier is complete in a way Microsoft's is not.

Second, AI. Google folded Gemini into Workspace business plans in 2025 with a price rise and no opt-out, so AI is now part of the base price. Microsoft sells it separately: Microsoft 365 Copilot is a $30 per user per month enterprise add-on on annual terms, with a Copilot Business tier at $18 promotionally, listed at $21, for organisations up to 300 users through December 31, 2026, plus bundled SKUs such as Business Standard with Copilot at $23.50. A hundred-seat team that wants AI in its spreadsheets pays materially more on the Microsoft side.

Prices vary by region, currency, and agreement, and both vendors moved them within the last eighteen months. Check each vendor's own page for your country before budgeting.

Governance and compliance

Both platforms meet mainstream compliance requirements, and for most regulated organisations the deciding factor is not the spreadsheet but the tenant around it. Microsoft's advantage is depth of control: sensitivity labels, information barriers, retention policies, and eDiscovery are more mature and more granular than Google's equivalents.

Google's advantage is default safety. Because Sheets files never leave the platform, there is no copy on a laptop, no version emailed to a personal address, and no shadow spreadsheet on a USB stick. Microsoft can achieve that too, through policy and effort; Google gets it by architecture. Two questions cut through most vendor decks: where must the data physically live, and who audits access? Settle those with your compliance team before anyone argues about pivot tables, because a data-residency requirement can decide the whole thing in one meeting.

Migration friction, in both directions

Converting files is where teams get hurt, and the damage is asymmetric. Excel to Sheets loses the most: VBA macros do not survive, Power Query steps and Power Pivot data models are dropped, and slicers, complex conditional formatting, and custom number formats frequently degrade. Sheets to Excel is gentler but not clean.

Going the other way, the usual casualties are the Google-only functions. Every QUERY, IMPORTRANGE, GOOGLEFINANCE, and ARRAYFORMULA becomes an error in Excel with no automatic substitute, and Apps Script simply has nowhere to go. A Sheet built without those functions usually exports to .xlsx with only cosmetic differences.

Plain formulas, standard charts, ordinary pivot tables, and data validation travel fine both ways. Our rule of thumb: anything typed into a cell probably survives, anything configured in a dialog probably does not, and anything written in code definitely does not. The deeper cost is not the conversion but the second one. Files that bounce between formats accumulate losses, and nobody notices until a number is wrong. Convert once, deliberately, then commit.

The full comparison, capability by capability

Here is our honest read of where each product stands in 2026. "Partial" means the capability exists but carries a limitation serious enough to plan around, not a marketing asterisk. Verified figures appear in the notes; the rest are judgments from regular use of both.

Pick this one if…

Abstract comparisons are easy to agree with and hard to act on. Here is the same analysis expressed as concrete situations, with a single recommendation each and the reason it holds. Where we say "both," that is a real answer, not a hedge.

What most comparisons get wrong

Four things. Comparison articles are usually written as feature checklists, which overweights capabilities almost nobody uses and ignores what actually decides the outcome. These are the corrections we would make to nearly every version of this article we have read, including the popular ones.

• The licence is not the cost. At $14 a seat, software is the cheapest part of the decision. The real spend is retraining, rebuilding templates, rewriting macros, and the productivity dip while people relearn muscle memory. A team of thirty switching suites burns more in lost hours during month one than it saves in licences over a year.
• Almost every team already uses both. Finance is in Excel, ops is in Sheets, and someone is quietly exporting between them. Pretending you will standardise on one is how you get an unmanaged mess instead of a deliberate split. Write down which workloads live where and the coexistence becomes an architecture rather than an accident.
• Mobile decides more than feature depth. Where a process depends on non-desk workers updating data, who has Power Pivot is irrelevant. Adherence is the constraint, and the tool that gets updated from a phone at 6pm wins regardless of what it cannot do.
• AI has not changed the answer yet. Copilot and Gemini are both real improvements over 2024, and neither is autonomous. They speed up writing formulas, structuring data, and getting started; they do not remove the need for someone who understands the model. Picking a platform because its AI is slightly ahead means choosing on the fastest-moving and least durable variable on the list.

Bottom line

Excel is the better tool; Google Sheets is the better workspace. If the spreadsheet is where analysis happens, choose Excel and accept clumsier collaboration. If the spreadsheet is where a team coordinates, choose Sheets and accept the ceiling on scale and modelling. Both positions are defensible, and picking on ideology rather than workload is the only genuinely wrong answer.

For most organisations the right structure is deliberate coexistence: Sheets as the default for shared, living documents, Excel for the handful of files where depth actually matters, and a written rule about which is authoritative. That costs nothing, sidesteps the migration tax, and lets each product do the job it was designed for.

One tool, both platforms. XLsheetAI writes, explains, and fixes formulas for Excel and Google Sheets, and converts a screenshot of a table into a real spreadsheet. Handy when your team, like most teams, lives in both.