This page compares a budget model and an open-weight flagship on one job: finding and fixing the bug in a pasted SQL query that returns the wrong rows. It weighs price against quality and ends with a fair way to test both on your own queries.
Oct 20, 2026 · 9 min read
Start with GPT-6 Luna for a pasted query that returns the wrong rows. Reach for Kimi K3 when the query cannot leave your environment, or when it is one part of a broader hunt for bugs.
Luna lists $0.10 per million input tokens and $0.50 per million output tokens. Kimi K3 lists $3.00 and $15.003, 4. A token is a small piece of text, roughly a short word. A request with 2,000 input tokens and 500 output tokens costs about $0.00045 on Luna and $0.0135 on K33, 4. The pair is a budget model against an open-weight flagship, so the price gap is part of the question.
The quality gap is smaller than the price gap. On a planted-bug benchmark K3 fixed 21 of 105 bugs in one run and Luna averaged 18.3 across three runs, though the setups differed6. On a SQL generation benchmark Luna scored higher, 53.63 to 19.51 on exactness5. Both are indirect evidence, because neither one hands the models a broken query and scores the repair. A sensible pattern is Luna first and escalation second, with K3 or a stronger model for the hard cases.
You get a query and a symptom such as duplicated customers or missing rows. A low-cost model that names one likely cause and the smallest fix suits that, and Luna lists the lower price by a wide margin.
The wrong rows come from a query buried in app code. When the SQL is one part of a larger bug, K3's single-run result on planted bugs is worth testing beside Luna.
Queries expose table layouts and business rules. K3's open weights allow a self-hosted setup, which some teams need before any query text leaves their systems.
You send many small queries and want a quick first answer. Start at medium effort and send the unclear cases up a level or to a person.
This page compares the two models through their API in one neutral setup, with the same query, table notes and prompt going to both.
The job is the one data teams get all week. Someone sends a query and a symptom such as duplicate customers, missing rows for accounts with no activity, or orders outside the requested period. The model has to find the cause and propose the smallest fix. We looked at official model documentation, two public benchmarks, a Vals AI comparison and two small tests1, 2, 5, 6, 8.
We left tools out of the specs table on purpose. A database connector, a code sandbox and file upload belong to the app around the model, so the same model behaves differently in a chat product, in the API or inside a workspace. Counting them would compare the wrappers. The fair test sends identical text to both models.
The model facts that shape a SQL debugging job. Tool and file features are left out, since those depend on the app around the model.
Figures from OpenAI and Moonshot AI documentation, checked October 20, 2026. Luna's long-context rate applies to the whole request once it passes 272K input tokens.
One row per working dimension, with the model that has the edge and the evidence behind it. Where a test is indirect or the setups differ, the row says so.
Better-choice calls map to dimensions the sources actually evaluated. No public benchmark gives both models the same faulty queries and scores the fixes.
Same query, same table notes, same prompt, no editing before scoring. Then judge what a data team pays for: did it find the real defect, keep the rest of the logic, and need little correction.
Cover the usual causes: a one-to-many join that duplicates rows, a filter on the nullable side of a LEFT JOIN, a date or time-zone boundary, a grouping or window mistake, and a correlated subquery or anti-join error.
State the dialect, the query, the table definitions, five to twenty sample rows and the expected result. Neither model gets a richer version. If you change the prompt mid-test, change it for both.
Match the effort setting where you can and run both in the place the team will use them. API and chat-product results can differ because system instructions, context handling and tools differ, so test where the work will happen12.
Check each answer for the real defect, unchanged unrelated logic, valid SQL for your dialect, correct predictions of which rows enter or leave, and no invented columns. For high-stakes work hide the model names and use two reviewers.
No public test gives both models the same wrong-rows query, so the evidence is a mix. Here is what each source helps judge.
Single-tester and community reports are a secondary signal, not a replacement for benchmarks or official docs.
Both models do better when the prompt names the dialect, caps the scope and asks for the smallest fix. They differ in how much structure to add around that.
GPT-6 Luna suits a compact contract. Ask for one main diagnosis before any alternatives, and start at medium effort, moving to high or max for nested logic. OpenAI positions Luna for focused, scoped problem-solving, and a tight request limits the risk of a broad rewrite you did not ask for10.
Kimi K3 suits explicit inspection stages and firm scope limits. Moonshot's guidance recommends clear instructions, delimiters, explicit steps and examples15. K3 always reasons, and you choose low, high or max, so a straightforward query rarely needs the top setting11.
A GPT-6 Luna prompt: a compact contract
You are debugging PostgreSQL.
The query returns extra rows.
Identify the single most likely semantic bug and
explain it in two sentences.
Give the smallest corrected query.
Preserve all unrelated logic.
Then give a three-row regression fixture showing
the before-and-after difference.
Do not invent schema facts.A Kimi K3 prompt: inspection stages and scope
Review this Snowflake query in this order:
join cardinality, nullable-side filters,
date boundaries, grouping, then window partitions.
Rank at most two hypotheses.
Patch only the highest-confidence bug and state
what evidence would disprove it.
Return the original fragment, the replacement
fragment and the expected row-level change.Neither model is perfect on SQL. The useful question is where each one stops short and what to change in the prompt or the workflow.
One question first. Is this a narrow, repeated job of fixing pasted queries? Then follow the branch that matches most of your work.
A starting point and not a rule. Test on your own queries before you commit.
If your team fixes many short queries with a known symptom, start with GPT-6 Luna at medium effort and escalate the failed or low-confidence cases. The price makes it easy to try a second pass, and OpenAI documents strict structured output for it3, 9.
If the query is part of a wider investigation across many files, test Kimi K3 beside Luna. K3 has a small edge on one planted-bug benchmark, though not under controlled conditions6. If the query or schema cannot leave your environment, choose K3 for its open weights2.
If the answer affects money, permissions, compliance or customer deletion, accept neither answer alone. Run the corrected query on a fixture, compare the result sets and have a person approve it. If you cannot supply expected rows or the business intent, use either model only to produce ranked hypotheses.
GPT-6 Luna is the safer default for debugging a pasted SQL query. Kimi K3 is the better specialist choice when open weights, private deployment or a broader search justify the higher API price.
The evidence has limits. No public benchmark gives both models the same faulty queries and scores the repairs. The SQL benchmark measures writing queries, the planted-bug benchmark used different tools and settings, and broad test suites disagree by task5, 6, 8. Prices and model versions also change quickly, so run a blind test on your own dialects, schemas and expected results.
The safest final step is to test the shape of your own data, not a generic prompt from the internet. A fair test needs the same setup for both models: the same query, the same table notes 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 GPT-6 Luna vs Kimi K3, and not just which one happened to be easier to reach that day.
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