This page compares two current models on one job: explaining what actually drove a change in a pasted sales or usage table. It looks at driver attribution, cost, prompting and a fair way to test both on your own numbers.
Sep 1, 2026 · 10 min read
GPT-5.6 Sol is the safer default for discovering the real driver in a pasted sales or usage table. DeepSeek V4 Pro is the stronger economic choice once the workflow independently verifies every conclusion.
That split rests on Sol's lead on a broad independent reasoning index1, 2 and on finance-specific evidence5, set against DeepSeek's published rates, which run a fraction of Sol's on both input and output3, 4. Neither figure is a dedicated test of contribution analysis, mix shifts or denominator traps, so the honest reading is that Sol carries the stronger general-purpose signal and DeepSeek carries the stronger economics.
In practice that argues for a workflow, not a single pick: use Sol at high or xhigh effort when a wrong driver would be expensive, and lean on DeepSeek's low cost to fund a second decomposition or an adversarial critique pass when the table is one of many run every week.
A wrong driver in a board update or a pricing decision is expensive. Sol's broad reasoning lead and finance-specific evidence support it as the safer first read.
You explain the same kind of change across dozens of tables a week. DeepSeek's published rates are a fraction of Sol's, which can fund a verification pass on every table instead of trusting one read.
A churn or usage explanation feeds a real intervention. Require a reconciled contribution table and a competing explanation from either model before a team acts on it.
Downstream systems need a fixed shape. GPT-5.6 Sol enforces an exact JSON schema. DeepSeek V4 Pro only guarantees valid JSON, so add a validation step before trusting its output structure.
This page compares the two models through their API in one neutral setup, not one model inside a spreadsheet add-on against the other inside a BI dashboard.
The parts that matter for explaining a sales change are finding the underlying driver rather than the first visible pattern, reasoning about price, volume and mix effects, holding a fixed reporting schema, and cost per table. Official docs come first, then independent reasoning and finance leaderboards with a clear method.
We left spreadsheet and BI features out of the spec table on purpose. A pivot-table add-on or a connected dashboard belongs to the app around the model, not to the model itself. Judging those here would compare wrappers, not which model finds the real driver.
The model facts that actually affect explaining a pasted table. Spreadsheet and BI tools are left out, since they belong to the app around the model.
Figures from DeepSeek and OpenAI documentation, checked September 1, 2026. DeepSeek's peak hours are specified in its own pricing documentation. Sol's rate is OpenAI's current promotional price, not its list price.
The answer changes by dimension, not by brand. This is the main analysis: which model has the edge on each part of explaining a sales or usage change, and what backs it up.
Better-choice calls map to dimensions the sources actually evaluated. No cited source directly tests contribution analysis, mix-shift detection or denominator traps on these two exact models.
A useful test feels boring. Same tables, same prompt, same reasoning tier. Then judge what your team actually pays for: did the numbers reconcile, and was the named driver the real one.
Include a small segment with the largest percentage growth while a large segment drives the absolute change, a price-versus-volume swap, a mix shift, an offset hidden by the total, and a denominator change.
State the business metric, demand arithmetic reconciliation, and explicitly prohibit causal claims the table does not support.
Match the reasoning tier where possible, and run both through the API or production surface the team will actually use, since chat and API results can differ.
Check whether the contributions reconcile to the total, whether it ranked absolute drivers over dramatic percentages, and whether it tested a competing explanation. Hide the model names for a commercial decision.
No public benchmark tests sales-change decomposition on these exact models. Here is what each source helps judge, and how much weight it can carry.
Community benchmark threads exist for both models but use one run per test and model-based judging. Treat them as a secondary signal, not a replacement for the leaderboards above.
The best prompt is not the same for both. Sol benefits from an explicit ban on unsupported causal claims. DeepSeek benefits from a forced multi-pass audit sequence.
For GPT-5.6 Sol, use high or xhigh effort first, state the business metric explicitly, and demand arithmetic reconciliation before naming a driver. Prohibit causal claims the table does not support.
For DeepSeek V4 Pro, use max effort for difficult decompositions and give it an explicit four-pass sequence. Ask for a compact JSON object so additional reasoning does not turn into an overlong narrative.
A GPT-5.6 Sol prompt: reconciliation before naming a driver
Explain the change from Period A to Period B. Calculate each
row's contribution to the absolute total change, check that
contributions reconcile, then test for price, volume and mix
effects.
Name the top driver only after completing all checks. Separate
"shown by the table" from "possible business causes."A DeepSeek V4 Pro prompt: a forced four-pass audit
At max reasoning effort, audit this table in four passes:
totals, row contributions, mix or denominator effects, and
competing explanations.
Return JSON with reconciliation, ranked_drivers, offsets,
unsupported_inferences, and confidence. Do not select a driver
until the reconciliation passes.Neither model is a safe unsupervised analyst. The useful question is where each one adds risk, and what to change in the prompt or the workflow.
One question first. What is the cost of a plausible but wrong explanation? Then follow the branch that matches your volume and review process.
A starting point, not a rule. Test on the tables your team actually pastes in.
If the explanation feeds a board update, a pricing decision or a churn diagnosis, start with GPT-5.6 Sol at high or xhigh effort. It carries the stronger broad-reasoning and finance-specific evidence1, 2, 5, and the cost of a wrong driver there is higher than the API bill.
If the table is one of thousands run every week and a human or a deterministic check reviews the output, DeepSeek V4 Pro is the stronger economic choice. Its published rates are a fraction of Sol's3, 4, which can fund a second, independent decomposition pass on every table rather than trusting one read.
For a strict downstream format, GPT-5.6 Sol can enforce an exact JSON schema natively. DeepSeek V4 Pro only guarantees syntactically valid JSON, so pair it with your own schema validation before trusting the shape of its output. For a very long pasted history, DeepSeek's flat time-based pricing is the more predictable choice unless the output is going in front of someone who cannot wait for a second opinion.
GPT-5.6 Sol is the safer first trial for discovering the real driver in a pasted sales or usage table. DeepSeek V4 Pro is the stronger economic choice when the workflow independently verifies every number and conclusion.
That verdict is deliberately narrower than 'Sol is smarter.' The exact-model independent evidence favors Sol, and its finance results are relevant, but no public test directly measures whether either model resists percentage salience, a mix shift or unsupported causal storytelling. Vendor benchmarks use different harnesses, and prices and endpoints change quickly. Playgram is not the right buy for everyone either: a solo analyst who only ever needs one model is better served by a single vendor subscription.
The safest final step is to test the shape of your own tables, not a generic prompt from the internet. A fair test needs the same setup for both models: the same pasted data, 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 DeepSeek V4 Pro vs GPT-5.6 Sol, and not just which one happened to be easier to reach that day.
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