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A CSV Benchmark With Two Zero Scores—and a Useful Diagnostic

A synthetic CSV inventory benchmark gave Gemini 3.7 Flash and Claude Haiku 4.5 strict scores of 0/60, but a narrowly defined one-fence diagnostic revealed different content results.
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In Simon Levy’s synthetic inventory-reconciliation benchmark, Gemini 3.7 Flash and Claude Haiku 4.5 both scored 0/60 under the strict requirement that each raw response be valid JSON and satisfy the CSV contract. A separate, post hoc test that removed one complete outer JSON code fence produced sharply different content scores: 59/60 for Gemini and 14/60 for Claude. Those measurements answer different questions; neither is a general ranking of the models.

What the CSV benchmark tests

Each model receives an export CSV, a count CSV, an explicit column mapping, and a reconciliation contract. It must return one JSON object containing a candidate CSV, row-level issues, and any fatal file error. The task is to match each count to an exported product row, replace its new quantity, preserve other data, and block unsafe updates.

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The contract makes details that are easy to overlook consequential:

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  • SKU 0005 is a string identifier, not a number to normalize.
  • North and north are distinct locations.
  • A blank new-quantity cell differs from 0.
  • Quoted fields can contain commas or newlines, and unknown columns must be preserved.
  • Duplicate keys, conflicting values, or incomplete product information can make an update unsafe.

The scorer permits reordered headers, differences in CSV quote style, LF or CRLF line endings, and equivalent integer spellings in the editable quantity field. It still requires exact string preservation in protected cells, and candidate row order matters.

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How the run was set up

The benchmark collection contains 72 fixtures: 48 original cases and 24 compositions. Twelve original cases were reserved for development. Each reported run therefore covered 36 original evaluation cases plus all 24 compositions. The compositions follow six recipes, each with four related transformations; they are useful structured tests, but not 24 independent examples of real inventory work.

The comparison used Gemini 3.7 Flash (google/gemini-3.7-flash) and Claude Haiku 4.5 (anthropic/claude-haiku-4-5@20251001) through Kaggle Model Proxy. Both completed the same 60 selected cases, with a fresh chat for each case. Requests specified temperature 0, seed 0, no tools, and a maximum of 8,000 output tokens per response. These were text-generation calls, not calls with an enforced JSON output schema or generated-code execution. Levy says inspection of the installed SDK showed that its string-response path added no formatting instruction and returned model text unchanged.

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Strict scores: neither model met the full contract

The primary score was 0/60 for both models. Each returned 60 nonempty responses, but the score required the raw response to pass the JSON interface as well as match the allowed CSV semantics and issue report. A Markdown-fenced JSON object is not valid as a raw JSON response, even if a person can understand its contents. Because both models’ primary responses failed JSON parsing, the strict score does not establish how often their CSV edits would have been semantically correct.

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One-fence diagnostic: a different measurement

Levy also applied a narrowly defined post hoc diagnostic: remove exactly one complete outer json code fence, then assess the resulting content. The procedure did not accept surrounding prose, repair incomplete JSON, or make any other correction. Responses to which the procedure did not apply remained in the denominator.

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Model Strict raw-response score One-fence diagnostic Original evaluation cases Compositions
Gemini 3.7 Flash 0/60 59/60 35/36 24/24
Claude Haiku 4.5 0/60 14/60 14/36 0/24

Eight Haiku responses had fenced JSON with trailing explanation or multiple blocks, so the one-fence procedure did not apply to them; they stayed in the 60-case denominator. The diagnostic therefore shows a substantial difference under that particular adjustment, while the strict score shows that neither raw-response stream met the direct-consumption interface. Reporting only the zeros hides the diagnostic difference; reporting only adjusted scores hides the interface failure.

What the remaining failure does—and does not—show

Gemini’s sole diagnostic miss was case preservation-05. Its response contained 448 visible characters, while reported usage was 7,996 output tokens against the requested 8,000-token cap. Token usage is not the same as visible-text length. The report does not establish why the JSON was incomplete; a larger output budget is a possible follow-up experiment, not a demonstrated fix.

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This was conservative offline validation: no Shopify import or real inventory change took place. The report’s emitted-row mutation diagnostic counts unexpected rows relative to the expected candidate; omitted rows can fail overall correctness without counting as emitted-row mutations. Since both primary response sets failed JSON parsing, mutation was unassessed. That means unknown, not “zero damage,” and the benchmark provides no evidence of actual inventory harm.

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The suite also does not cover every written-contract error branch. The author notes that checking expected outputs against the scorer can establish consistency between them, but does not independently prove that every expected label is correct.

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How to interpret the comparison

Simon Levy describes the comparison as “descriptive.” It is one completed run per model on a small, inspectable synthetic suite with correlated cases, not a formal statistical generalization or a broad ranking of model families. The figures are the benchmark author’s results, not independent estimates of either model’s general performance.

The useful distinction is between interface compliance and content performance after a specific, limited formatting adjustment. The strict result asks whether the raw answer can be consumed as required and is correct. The diagnostic asks what happened after removing one kind of wrapper. Treating those as interchangeable would obscure both the format failure and the content difference observed in this run.

What a stronger follow-up should measure

Levy proposes separating interface conditions in advance rather than retroactively replacing the primary metric:

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  • the existing raw-text prompt;
  • a one-fence adapter; and
  • supported structured-output modes.

Further useful work includes varying output budgets on long preservation cases, adding independently authored cases, filling gaps in contract-error coverage, and repeating runs. These steps would help distinguish repeatable behavior from results specific to this single run and fixture set.

The article states that the CSV Inventory Contract Integrity benchmark and its collection are publicly published on Kaggle, with retained materials including the contract, fixtures, expected outputs, scorer, request hashes, raw responses, usage, and separate diagnostics. The results presented here are those reported by Levy; they should not be read as an independent audit of the collection.

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