Claude 3.5 Sonnet vs GPT-4o for CSV Data Normalisation: Which Engine Safely Cleans Messy E-Commerce Catalogs?

The E-Commerce Catalog Nightmare

There is a specific brand of developer hell reserved for e-commerce migrations. You are handed a 50,000-row CSV extract from a legacy platform. The columns are misaligned, product variants are flattened haphazardly, HTML tags are splattered across description fields, and the SKU numbers look like they were generated by a chaotic neutral algorithm.

Traditional regex scripting only gets you so far when data is this inconsistent. You need semantic understanding to normalise product options, strip inline styling, and group variations without losing crucial data.

Large language models are highly capable data normalisation engines, but there is one massive catch: hallucinations are fatal. If an LLM alters a single character of an alphanumeric SKU, or misplaces a decimal point in a price column, your import pipeline is ruined.

We put Anthropic's [/platforms/claude] and OpenAI's [/platforms/openai] head-to-head to see which LLM handles large-scale CSV data normalisation with the strict data integrity that enterprise engineering demands.

The Test: Normalising 1,000 Rows of Pure Chaos

We designed a rigorous test using a raw 1,000-row CSV file containing typical e-commerce catalog errors: 1. Inconsistent Alphanumeric SKUs: Variations like SKU-990-BLK-S, sku 990 blk s, and SKU_990_B_S that all referred to the same item. 2. Messy Pricing Columns: Strings containing multiple currencies, random spaces, and corrupted symbols (e.g., USD 19. 99 and £14.99 incl tax). 3. Nested Product Attributes: Variations mixed directly into the title text (e.g., "Classic Tee - Large / Blue") that needed parsing into clean JSON arrays.

We evaluated both models on data preservation, syntax validity, and token cost efficiency.

Claude 3.5 Sonnet: The Precision Instrument

Claude 3.5 Sonnet has built a reputation for superior logical reasoning, and it shines brightly when handling structured text. We utilized an XML-based instruction prompt (which you can build for yourself using our [/prompts] builder) to enforce strict schema adherence.

Data Integrity and Hallucination Prevention Sonnet performed exceptionally well on preserving SKU structures. Out of 1,000 rows, it did not alter a single alphanumeric SKU character. When faced with ambiguous variations, rather than guessing, it flagged them using our specified custom error tag (`[MANUAL_REVIEW]`). This behaviour is vital for production pipelines where silent failures are the worst-case scenario.

Structural Formatting Sonnet’s native affinity for XML tags makes it incredibly easy to parse its outputs programmatically. By asking it to wrap normalised rows in `<row>` tags, we avoided the classic JSON truncation issues that plague long LLM runs. If you experience output cutting off mid-stream with Anthropic's API, check our troubleshooting tips at [/platforms/claude/articles] to learn how to manage chunked processing.

Where Sonnet Falls Short - **Processing Latency:** Sonnet is noticeably slower than GPT-4o when processing bulk tokens, making real-time interactive normalisation painful. - **Cost:** At $3 per million input tokens and $15 per million output tokens, processing massive catalogs can scale in cost quickly if you do not optimise your pipeline.

GPT-4o: The High-Throughput Workhorse

GPT-4o approaches data normalisation with raw speed and robust schema enforcement tools. OpenAI’s native Structured Outputs feature allows developers to supply a Pydantic schema to guarantee that the output matches a precise JSON format.

Speed and Throughput In terms of sheer raw speed, GPT-4o outperformed Sonnet, processing our 1,000-row batch in nearly half the time. If you have millions of rows to clean, this throughput difference represents significant time savings.

Structured Output Constraints By forcing GPT-4o to adhere to a strict JSON schema, we completely eliminated formatting syntax errors. The model could not output malformed JSON. However, this strictness created a different issue: when GPT-4o encountered a badly corrupted row that did not fit the schema, it occasionally altered the raw data (such as dropping a character from a SKU) just to force the record to validate against the schema.

Where GPT-4o Falls Short - **Silent Data Alterations:** GPT-4o is highly eager to please. If a value does not fit its expected format, it is far more likely to silently change a value (e.g., changing `SKU-99A` to `SKU-99` to match an expected pattern) than Sonnet. - **Complex Hierarchical Logic:** GPT-4o struggled more than Sonnet when nesting deeply nested product options (e.g., grouping size, colour, and material variants from messy descriptive copy).

Head-to-Head Comparison

| Evaluation Metric | Claude 3.5 Sonnet | GPT-4o (with Structured Outputs) | | :--- | :--- | :--- | | SKU Preservation Rate | 99.9% (No silent alterations) | 97.4% (Occasional schema-matching edits) | | Nested JSON Grouping | Excellent (Handles complex hierarchies) | Moderate (Struggles with deep nesting) | | Parsing Reliability | Excellent (Using XML wrappers) | Perfect (Using JSON schema enforcement) | | Speed / Throughput | Moderate | Fast | | Input/Output Pricing | $3.00 / $15.00 per M tokens | $2.50 / $10.00 per M tokens |

The Verdict

If your e-commerce catalog contains highly complex variations, delicate pricing tiers, and critical SKU codes where even a single error will break downstream inventory management, Claude 3.5 Sonnet is the clear winner. Its unmatched logical rigour ensures your data remains clean, uncorrupted, and perfectly grouped, even if you pay a premium in processing time.

However, if your data is relatively uniform, your schemas are simple, and you are processing millions of records where throughput speed and lower token costs are your primary business drivers, build your pipeline using GPT-4o with Structured Outputs—just make sure you build pre-processing validation scripts to catch any over-eager schema adjustments before writing to your database.