This page compares two models on one job: reading a broken formula pasted into chat, explaining what it does, and rewriting it so it works. It covers wrong-reference detection, speed, cost and prompting, and ends with a fair way to test both on your own formulas.
Aug 18, 2026 · 11 min read
Grok 4.5 is the safer bet when the formula is syntactically fine but wrong about what it is actually calculating, since it leads on the closest independent reasoning benchmark. Qwen 3.7 Max answers faster and has its own published spreadsheet-specific result, but its aggregate reasoning score trails Grok's by a wide margin.
That split rests on an independent exact-version reasoning benchmark1, a vendor-reported spreadsheet benchmark that does not include Grok2, and measured speed and latency figures1, not on a dedicated formula-repair test, since none exists publicly for these exact models.
The practical rule is to match the model to the kind of mistake. A wrong reference that still calculates something plausible is a semantic problem, where Grok's broader reasoning evidence helps most. A routine spreadsheet operation where speed matters more than subtlety leans toward Qwen.
A wrong reference in a finance model can be expensive and easy to miss. Grok's stronger reasoning evidence suits catching a subtle, plausible-looking error.
You fix the same handful of formula mistakes across many reports. Qwen's faster response time suits a high-volume, lower-stakes workflow.
You field formula questions from non-technical colleagues all day. Public evidence does not separate the two models on plain explanation quality, so a quick blind test decides it.
The broken formula sits inside a workbook with many named ranges and adjacent formulas. Qwen's larger context window fits more of that surrounding structure.
This page compares the two models through their API in one neutral setup, not one model working inside Excel or Google Sheets against the other inside a different add-on.
The parts that matter for this task are reading the current formula correctly, catching a wrong reference, range or return column, distinguishing a confirmed fix from an assumption, and returning a formula that calculates the intended result. Official docs and the closest independent evidence come first.
We left tools out of the spec table on purpose. Working directly inside a spreadsheet app, an office-agent integration or a browser plug-in depends on the app around the model, so the same model can behave differently in a chat product, the API or a workspace. Judging those here would compare software, not formula repair.
The model facts that actually affect explaining and fixing a formula. Tool features are left out, since they change with the app around the model.
Figures from xAI and Alibaba Cloud documentation, checked August 18, 2026. Alibaba's pricing page was showing a limited-time promotion at the time of checking, so production budgets should use the list prices above. xAI's documentation also notes that Grok charges a higher rate once a single request exceeds 200,000 tokens, well inside its 500,000-token window. That only matters if a team pastes a very large amount of surrounding workbook context, not for a single formula.
The answer changes by part of the job, not by brand. This is the main analysis: which model has the edge on each part of explaining and fixing a formula, and what backs it up.
Better-choice calls map to dimensions the sources actually evaluated. Where the evidence is a vendor-reported score without a matching Grok result, the row says so.
A useful test feels boring. Same headers, same formula location, no editing before scoring. Then judge what your team actually pays for: did it identify the exact wrong reference, and did it separate a confirmed fix from an assumption.
Include a syntactically invalid formula, a valid formula pointing at the wrong return column, an off-by-one aggregation range, and an ambiguous formula that cannot be fixed without more context.
The same headers, formula location, neighboring formulas and reasoning setting for both. Neither model gets a richer version of the surrounding sheet.
Enable thinking or reasoning consistently for both, and test the corrected formula in Excel or Google Sheets against changed input values, not just against the original numbers.
Do not edit either response before scoring. Check whether it named the exact defective token, distinguished certainty from assumption, and produced a formula that gives the right answer across test values.
No public benchmark answers this head-to-head directly. Here is what each source available today actually helps judge.
The pattern across sources is consistent: give the model headers and neighboring formulas, and require it to flag uncertainty rather than guess.
Both models repair a formula better when asked to audit every cell reference against the headers before rewriting, rather than just asked to fix the formula.
Grok 4.5 does best with medium reasoning for ordinary formulas and high reasoning for nested lookups or date logic, plus an explicit instruction to separate diagnosis from assumptions3.
Qwen 3.7 Max does best with thinking enabled and a rigid, numbered output contract that keeps an assumptions section, so a confident-sounding answer does not skip past an unresolved reference4.
A Grok 4.5 prompt: audit before rewriting
Formula in E2: =SUMIFS($H:$H,$B:$B,B2,$A:$A,">="&
EOMONTH(C2,-1)+1,$A:$A,"<="&EOMONTH(C2,0))
Headers: A=Date, B=Sales Rep, C=Customer,
D=Reporting Month, H=Revenue.
Explain the formula term by term. Check every
cell reference against the headers. Identify the
exact wrong reference. If the correction is not
certain, ask for missing context instead of
guessing. Then give one corrected formula and
two test cases.A Qwen 3.7 Max prompt: a numbered contract with assumptions
Analyse the formula below using only the
supplied context.
Context: Formula cell E2. A=Date, B=Sales Rep,
C=Customer, D=Reporting Month, H=Revenue.
Formula: [paste formula]
Return exactly:
1. Current behavior
2. Reference audit
3. Confirmed error
4. Corrected formula
5. Assumptions requiring verification
Do not invent missing sheet names, columns or
business rules.A pasted formula alone often lacks enough evidence to distinguish a wrong reference from an unusual but intentional business rule. The useful question is where each model adds risk, and what to change in the prompt.
One question first. Does the repair depend on catching a subtle semantic mismatch, or is it a straightforward spreadsheet operation? Then follow the branch that matches your situation.
A starting point, not a rule. Test on your own formulas before you commit.
If the repair depends on catching a subtle mismatch between the formula and the intended business logic, pick Grok 4.5, and use medium or high reasoning for nested lookups or date logic1.
If it is a straightforward spreadsheet operation and latency matters, pick Qwen 3.7 Max. Its larger context also helps when a large pasted workbook description accompanies the formula, and its published instruction-following evidence suits a strict multi-part answer4, 2.
If the prompt contains only a formula with no headers or stated intent, do not pick either model blindly. The correct first response is a request for context, so run both through an ambiguity test and use the one that declines to invent a reference.
One case neither model nor Playgram solves on its own: automatically pushing a corrected formula back into a live, shared workbook without a person confirming the change. That needs a spreadsheet application's own review or approval workflow, not a chat workspace, so a team building that kind of automated pipeline should evaluate the models directly through xAI's or Alibaba Cloud's API rather than through Playgram.
Grok 4.5 is the safer first choice for catching a wrong spreadsheet reference in chat. Qwen 3.7 Max is the faster alternative, with stronger published spreadsheet-specific evidence but weaker independent aggregate reasoning evidence.
The important caveat is substantial. There is no execution-checked, same-prompt public benchmark comparing these exact models on pasted broken formulas. Vendor benchmarks use different harnesses, public evidence is uneven, and behavior can change with reasoning settings and snapshots.
The safest final step is to test the shape of the formulas your team actually breaks, not a generic example from the internet. A fair test needs the same setup for both models: the same headers, the same formula and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first answer comes back. The cleaner the setup, the more the difference you see is really Grok 4.5 vs Qwen 3.7 Max, and not just which one happened to be easier to reach that day.
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