This page compares two budget models, through their API, on one job: spotting inconsistent, duplicate and out-of-range answers in a pasted survey table and proposing fixes. It ends with a fair way to test both.
Oct 6, 2026 · 11 min read
GPT-6 Luna is the cheaper default for a routine table audit. DeepSeek V4.1 Flash is the stronger second pass when answers contradict each other across columns or rows.
The report behind this page found no public test that runs these exact versions on survey cleaning and counts the good rows each one flags by mistake. A good row flagged as bad is called a false positive, and that is the risk this page watches most. So the verdict on false positives stays open7.
What the sources do support is a cost gap and a small quality edge. GPT-6 Luna lists $0.10 per million input tokens and $0.50 per million output tokens1. That is below DeepSeek V4.1 Flash's off-peak rates of $0.15 and $0.602. At maximum reasoning effort, Artificial Analysis scores DeepSeek at 39 on its broad Intelligence Index against 38 for Luna, and 68.9% against 53.2% on AutomationBench-AA4.
A sensible two-stage setup uses Luna to screen every table, then sends the uncertain rows to a higher reasoning setting or to DeepSeek. That staging is a judgment call from the sources. No test has measured it. In both stages, ask for a report of candidate problems with row IDs and proposed fixes, and never ask either model to delete a row.
You check range limits, exact duplicates and written consistency rules on every wave. Luna at low or medium effort handles those mechanical checks at the lower price per table.
You clean many exports and the cost per table adds up. Luna's lower standard rates make repeated audits and verification passes inexpensive.
Your tables hide impossible combinations and branching answers that contradict each other. DeepSeek at high or max effort scores slightly higher on broad reasoning and answers faster.
You own the rulebook and the final say. Screen with Luna, send uncertain rows to DeepSeek or a higher Luna setting, and keep fixed checks and a human review in front of every change.
This page compares the two models through their API in one neutral setup. App features such as spreadsheet add-ons, code sandboxes and file upload are not part of the verdict.
The parts that matter for cleaning a survey table are exact and probable duplicates, answers outside an allowed range, contradictions between related questions, impossible combinations, suspicious missing values and conservative fixes that keep legitimate records. Official docs come first, then independent comparisons and public research on table errors6, 7, 8.
We left tools out of the spec table on purpose. A spreadsheet add-on, a code interpreter or a file-upload flow depends on the app around the model, so the same model can behave very differently in a chat product, in the API or inside a workspace. Judging those here would mean comparing the wrappers around the models.
The model facts that affect a table audit. Prices are per million tokens, the small chunks of text a model reads and writes, and tool features are left out.
Prices from OpenAI and DeepSeek documentation, checked October 2026. Peak and off-peak are DeepSeek's own price schedule.
Three sample table sizes, priced from the published rates. The last row shows the one case where DeepSeek's off-peak rate comes out lower.
These are arithmetic examples from published prices, not measured costs. They leave out retries and extra reasoning tokens.
The answer changes by job. Here is each part of cleaning a table, which model has the edge and the source behind the call.
Better-choice calls map to dimensions the sources evaluated. Where the evidence is a broad proxy or a judgment call, the row says so.
Same prompt, same table format, same rulebook, same reasoning setting, same output schema. Then count which flagged rows were truly bad and which good rows got flagged.
Use a mostly clean table to expose false positives, one with exact duplicates and range violations, one with contradictory branching answers, one with near-duplicates or inconsistent free text, and one near your normal maximum size.
One prompt, one table format and one rulebook for both models. Put an unchanging row ID on every line and write out the allowed ranges and branching logic instead of asking either model to guess your survey policy.
Use the same reasoning class and the same output schema, and run both in the place your team will use in production. API and chat results can differ because system prompts and surrounding tools differ.
Do not edit outputs before scoring. Record precision (the share of flagged rows that are truly bad), recall (the share of known bad rows found), clean-row retention, fix validity, latency, billed tokens and cost per correctly handled table. For commercial work, hide the model names and have two people review independently.
No public benchmark covers survey cleaning for these exact models, so the evidence is a mix. Here is what each source helps judge.
Both models do better when every row has an ID and the ranges and branching rules are written out. After that, the prompt shape differs.
GPT-6 Luna does best with concise, explicit rules and a strict output schema. Start at low effort for mechanical checks and test medium or high for contradictions. OpenAI recommends lower effort for extraction and classification and more effort for diagnosis10.
DeepSeek V4.1 Flash does best with a staged prompt. Its reasoning level is configurable3, and anomaly research supports separating the search for candidate rows from the check that confirms them7.
A GPT-6 Luna prompt: short rules and a strict schema
Audit the survey table below. Report candidates only.
Do not delete or rewrite any row.
Check:
- the allowed ranges listed under RANGES
- exact duplicate rows
- the cross-question rules listed under RULES
A row is valid unless a stated rule is violated.
Return JSON with one item per candidate:
row_id, issue_type, evidence, confidence, proposed_fixA DeepSeek V4.1 Flash prompt: candidates first and then checks
Step 1: Infer the table schema and list the rules you will apply.
Step 2: Identify candidate bad rows.
Step 3: Verify every candidate against the original row and the
relevant comparison rows.
Step 4: Remove any flag you cannot support.
Do not delete or rewrite rows. Return only the verified issue list
as JSON with row_id, issue_type, evidence, confidence, proposed_fix.Neither model is safe to run unchecked on a survey table. The useful question is where each one adds cost or risk, and what to change.
One question first. Is your main limit audit cost or catching hard inconsistencies? Then follow the branch that matches most of your tables.
A starting point for your own test. Check it on your tables before you commit
If you run many ordinary tables and cost is the main limit, start with GPT-6 Luna at low or medium effort for range checks, exact duplicates and clearly written consistency rules1. If the hard part is ambiguous contradictions across rows, start with DeepSeek V4.1 Flash at high or max effort4.
If a wrong flag does more damage than a missed issue, choose Luna and run it in report-only mode with a second verification pass. This is an operating choice. No test shows Luna wrongly flagging fewer good rows. If you need fast answers at high effort, the independent speed measurements favour DeepSeek4.
If your tables regularly pass 272,000 input tokens, price both models before you decide. At 300,000 input and 10,000 output tokens DeepSeek costs about $0.051 off-peak and about $0.102 at peak, against about $0.0675 for Luna1, 2. If strict JSON is the goal, either model works, since both support schema output1, 3.
For high-stakes or regulated cleaning, neither model should act as an autonomous editor. Run fixed checks first and use a model only to rank and explain the candidates. Playgram is not the right buy for everyone either. If one analyst runs one scheduled audit through one model's API from their own script, a team workspace adds little.
GPT-6 Luna is the better default for routine audits because each table costs less. DeepSeek V4.1 Flash is the quality-first challenger for hard inconsistencies.
The limits are real. The exact-model comparisons are broad benchmarks and none is a survey-quality test. Vendor and independent results use different prompts, reasoning budgets and harnesses. Artificial Analysis gives DeepSeek a narrow lead at maximum effort while Vals finds the pair essentially tied4, 5. Prices and routed API versions can change quickly. Neither model has public evidence strong enough to justify deleting or correcting rows without a check.
The safest final step is to test the shape of your own survey tables rather than a generic prompt from the internet. A fair test needs the same setup for both models: the same table, the same prompt and the same place to run them, so the result reflects the models themselves. 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 V4.1 Flash vs GPT-6 Luna, and the less it depends on which one happened to be easier to reach that day.
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