This page compares two current models on one job: finding the unusual lines in a pasted monthly expense table and showing the arithmetic behind each flag. It tests them through their APIs and ends with a fair way to try both on your own tables.
Oct 13, 2026 · 10 min read
A short exception list that someone has to verify suits Sonnet 5.5 best. Grok 4.7 is a credible first-pass scanner when false positives are acceptable and its output is capped.
At matched high reasoning effort, Artificial Analysis gives the two models almost the same overall score, 47 for Sonnet 5.5 and 46 for Grok 4.7, and almost the same AA-Briefcase score, 1,638 against 1,6341. AA-Briefcase tests business workflows with hidden requirements and conflicting evidence. Grok leads on GDPval-AA, a broad test of professional work. Sonnet writes fewer output and reasoning tokens and had the lower cost per benchmark task1.
No public benchmark asks either model to find the odd lines in a pasted expense table, so the verdict combines the closest tests with judgment about the task. Every extra candidate line becomes something a person has to verify, which is why a controlled reviewer is usually the safer default.
A two-stage workflow is defensible. Let Grok scan widely and accept some false positives, then let Sonnet write the final audit-style list with a reason and an equation for each line. If only one model will be deployed, we would pick Sonnet 5.5. That split is a judgment call and not a measured rule.
You review a pasted monthly table and need a short list of odd lines with a reason and a visible calculation for each. Sonnet 5.5 writes less, so there is less to check.
You do not want to miss a subtle line. Grok 4.7 is a defensible wide first pass, and a second pass with Sonnet can turn the candidates into a tidy audit-style list.
You want one model for the scan and the summary, and you will read the answer yourself. A five-line cap and an equation per line keep the review short.
Flags flow into a tracker. Both APIs support schema-constrained JSON, so a strict schema and an automatic recalculation matter more than the model name.
This page compares the two models through their API in one neutral setup, and leaves out the apps and add-ons that sit around a model.
A pasted monthly expense table has columns such as month, vendor, category, amount, budget and prior-month amount. The job has three parts. The model has to find unusually large, duplicated, mistyped or suddenly changed spend. It has to explain which comparison made each line look odd. And it has to show the arithmetic, such as the dollar change, the percentage change and the share of total spend. We start from the official documentation, then the Artificial Analysis comparison of the two exact models at high effort and the methods behind its benchmarks1, 11, 12.
We left tools out of the specs table on purpose. Spreadsheet add-ons, file upload and code sandboxes belong to the app around the model, so the same model behaves differently in a chat product, in the API or inside a workspace. Judging those here would compare wrappers and say little about expense review. Both vendors do document code execution in their APIs, and that is a setup choice to apply equally to both models.
The model facts that shape an expense review. Tool and file features are left out, since those depend on the app around the model.
Figures from Anthropic and xAI documentation, checked October 2026. A token is the small chunk of text a model reads and bills by, and 1M means one million of them. The two vendors tokenize differently, so treat any cross-model cost comparison as directional.
The answer changes by job. This is the main analysis: which model has the edge on each part of an expense review, and what backs it up.
Grok looks stronger as an expansive investigator and Sonnet looks stronger as a controlled reviewer. Better-choice calls map to dimensions the sources evaluated, and rows resting on indirect evidence or on vendor documentation say so.
A useful test is repetitive. Same tables, same prompt, same reasoning level, same output format, then judge each answer on what a reviewer would do with it.
Choose three to five real tables of different difficulty. Include a clean month with no true anomaly, a month with one obvious amount error, a month with duplicates and category changes, a seasonal month with legitimate spikes, and a table with missing comparison fields or unclear labels.
One prompt with the same output schema and the same cap on flagged lines for both models. Neither model gets a richer version, and any change you make mid-test applies to both.
Use the same table text, system prompt, reasoning effort and token cap, and run both in the place your team will work. Chat products can add hidden instructions and tools, so API and chat results can differ.
Check seven things. Precision: how many flagged lines were worth reviewing. Recall: how many known anomalies were found. Arithmetic: whether every change, percentage and total recomputes exactly. Evidence: whether each claim cites real rows and not an invented baseline. Format: whether every flag came back in the requested structure. Review effort: how much text a person must cut. Calibration: whether the model separates unusual from incorrect. For commercial work, hide the model names and have two people review independently.
No public benchmark gives both exact models the same expense tables, so the evidence is a mix of related tests and vendor guidance. Here is what each source helps judge.
No public benchmark asks both models to flag anomalies in identical expense tables and show their arithmetic. The verdict rests on related tests and stays short of conclusive.
Both prompts ask for a short ranked list with an equation per line. They differ in how they handle thinking, effort and the cap on answer length.
For Sonnet 5.5, use high effort and ask it to think the problem through before it answers. Anthropic recommends this for tasks that combine calculation with structured output, and advises reserving xhigh and max effort for work where a quality gain has been measured, since replies get much longer there7.
Grok 4.7 works best with a narrow search space and a hard output cap, since it used more tokens than Sonnet at matched effort1. Medium effort is a sensible start for routine tables. xAI describes medium as suited to complex data analysis and keeps high for harder multi-step problems6.
A Claude Sonnet 5.5 prompt: think first and show one equation per flag
Review the pasted monthly expense table. Think the problem
through before answering.
Flag at most five lines that are unusual relative to comparable
rows, prior months, category norms, or the stated budget. For each,
give the row identifier, the reason, the comparison baseline, and
one explicit equation.
Do not call a line erroneous unless the table proves it. Finish
with a reconciliation check and a short list of missing information.A Grok 4.7 prompt: a narrow scope and a word cap per flag
Audit this expense table for unusual spend. Return exactly two
sections: (1) the five strongest flags, ranked, and (2) checks
performed.
Each flag must contain row, amount, baseline, equation, reason, and
confidence. Use no more than 60 words per flag. Separate
mathematical evidence from possible business explanations.
Do not list weak possibilities merely to be comprehensive.Neither model is safe to trust blindly on a finance table. The useful question is where each one adds cleanup work, and what to change in the prompt or the workflow.
One question first: is the bigger risk a missed anomaly or too many flags to check. Then follow the branch that matches your job.
Use this as a starting point and test on your own tables before you commit
If the bigger risk is too many false positives to review, choose Claude Sonnet 5.5 with high effort, a five-flag cap and an explicit equation for each line. If one model has to do both the scan and the final memo, Sonnet is also the safer single pick1.
If the bigger risk is missing a subtle anomaly, use Grok 4.7 for the first pass and constrain the final answer, or send its candidates through a second review stage. If you combine huge ledgers and supporting documents in one request, Sonnet's 1,000,000-token window is twice Grok's 500,0002, 3.
If the output feeds an automatic exception queue, either model works with a strict schema8, 14. Prefer Sonnet when review burden matters and Grok when its lower $6 output rate matters more than the tokens it used in testing1, 4. If the arithmetic must hold up in an audit, trust neither model's unaided calculation. Give both the same calculator or code tool, validate the totals, then choose Sonnet for the concise report or Grok for broader exploration.
If one person reviews one small table a month with a single model, Playgram is not the right buy. Its value is in comparing models and sharing context across a team.
Claude Sonnet 5.5 is the recommended default for a short monthly exception review. Grok 4.7 is a credible alternative for teams that want exhaustive exploration and will set strict length and evidence rules.
The limits are real. Public evidence is uneven, the closest benchmarks are far broader than a pasted expense table, vendors configure their benchmark runs differently, and prices and behavior change quickly. Sonnet's edge on explanations is a judgment call, and no public test measures either model on this job.
The safest final step is to test the shape of your own tables with planted anomalies and independently recomputed arithmetic. A fair test needs the same setup for both models: the same table text, the same prompt 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 Claude Sonnet 5.5 vs Grok 4.7, and which one happened to be easier to reach that day matters less.
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