
GPT-5.3-Codex for PDF to CSV Conversion: Can OpenAI's New Model Extract Bank Statements?
We test OpenAI's GPT-5.3-Codex on converting PDF bank statements to CSV and Excel. See the prompt, benchmark results, and how it compares to dedicated bank statement converter tools.
OpenAI launched GPT-5.3-Codex on February 5, 2026 — minutes after Anthropic released Claude Opus 4.6. GPT-5.3-Codex is OpenAI's most capable coding and agentic model, 25% faster than its predecessor, and the first model that was instrumental in creating itself. But can it handle a practical task like converting PDF bank statements to CSV?
We put it to the test alongside Claude Opus 4.6 to see how both frontier models perform on bank statement extraction.
What Makes GPT-5.3-Codex Different
GPT-5.3-Codex is not just a coding model. OpenAI says it combines the frontier coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of GPT-5.2 — all in one model. Key specs:
- 77.3% on Terminal-Bench 2.0 (highest agentic coding score)
- 64.7% on OSWorld-Verified (computer use tasks)
- 56.8% on SWE-Bench Pro Public (software engineering)
- 25% faster than GPT-5.2-Codex
- Fewer tokens to accomplish the same tasks
- Interactive steering — you can redirect the model mid-task without losing context
For PDF to CSV conversion, the reasoning capabilities and ability to follow structured instructions are what matter most.
The Prompt: Converting a PDF Bank Statement
We used the same prompt across both Claude Opus 4.6 and GPT-5.3-Codex for a fair comparison:
Convert the bank statement in this PDF into a structured CSV format.
Columns required: Date, Description, Withdrawal Amount, Deposit Amount, Balance.
Rules:
- Merge multi-line descriptions into a single cell.
- Ensure all dates are formatted as DD/MM/YYYY.
- Remove all currency symbols (e.g., $) and commas from numbers.
- Ignore page headers, footers, and summary tables. Only extract the transaction list.
- Output the result as a code block containing the CSV data.
The test document was a real bank statement PDF with multi-line transaction descriptions, mixed deposits and withdrawals, and several pages of data.
How GPT-5.3-Codex Performed
We uploaded the same one-page bank statement PDF and gave GPT-5.3-Codex the prompt. The model thought for 4 minutes and 8 seconds before producing the output — a structured Excel-style table titled "Extracted Transactions."

The model did extract the data correctly:
- Identified the transaction table and separated it from headers and summaries
- Merged multi-line descriptions into single cells
- Converted date formats to DD/MM/YYYY as instructed
- Stripped currency symbols from amounts
- Maintained the correct column structure with Date, Description, Withdrawal Amount, Deposit Amount, and Balance
However, the 4-minute processing time for a single page is the elephant in the room. For comparison, Claude Opus 4.6 completed the same extraction in under 30 seconds, and our dedicated Bank Statement Converter finishes in about 10 seconds with higher accuracy.
Claude Opus 4.6 vs. GPT-5.3-Codex: Side-by-Side
Both models handle the task competently. Here is how they compare on bank statement to Excel conversion:
| Feature | Claude Opus 4.6 | GPT-5.3-Codex | Dedicated Converter |
|---|---|---|---|
| PDF parsing | Excellent | Excellent | Excellent |
| Multi-line descriptions | Merged correctly | Merged correctly | Merged correctly |
| Date reformatting | Followed DD/MM/YYYY | Followed DD/MM/YYYY | Automatic |
| Currency stripping | Complete | Complete | Automatic |
| Summary table exclusion | Correctly skipped | Correctly skipped | Automatic |
| Speed (1-page statement) | ~30 seconds | ~4 minutes | ~10 seconds |
| Context window | 1M tokens | Standard | Unlimited (batch) |
| Output format | CSV text block | Excel table | Downloadable .xlsx |
| Accuracy guarantee | No | No | 99.9% |
The speed difference is significant. Claude Opus 4.6 completed the same extraction roughly 8x faster than GPT-5.3-Codex on this task. Both models produced correct results, but for a task where you might process dozens of statements, a 4-minute wait per page adds up quickly.
GPT-5.3-Codex's strength lies in its agentic capabilities and interactive steering — you can correct extraction errors mid-conversation without starting over. Claude Opus 4.6's 1M token context window handles larger documents without truncation. But for pure speed and reliability, a dedicated converter wins.
Why Frontier AI Models Still Fall Short for Production Use
Despite impressive results from both Claude Opus 4.6 and GPT-5.3-Codex, using general-purpose AI for regular bank statement conversion has significant drawbacks:
1. Speed Can Be Painfully Slow
GPT-5.3-Codex took over 4 minutes to process a single one-page bank statement. A 12-page statement could take 30+ minutes. Claude Opus 4.6 was faster at ~30 seconds, but a dedicated converter finishes the same job in about 10 seconds.
2. No Batch Processing
Both models require one-at-a-time document processing. If you're an accountant with 30 client statements to convert for month-end, that's 30 separate conversations. A dedicated bank statement converter processes multiple files in one upload.
3. Accuracy Is Not Guaranteed
LLMs can misread numbers, skip rows, or hallucinate transaction details. Financial data requires 100% accuracy. Our Bank Statement Converter achieves 99.9% accuracy with built-in validation that catches extraction errors before they reach your spreadsheet.
4. No Structured Output
Both models output CSV as plain text in a code block. You still need to manually save it as a .csv file and import it into Excel. A purpose-built converter gives you a downloadable .xlsx file with proper column types, date formatting, and number precision.
5. No Bank-Specific Optimization
General AI models treat every PDF the same. Dedicated converters have pre-built parsers optimized for 1000+ bank formats — they know where Chase puts the balance column, how HSBC formats multi-line descriptions, and where Bank of America hides the transaction date.
6. Cost Adds Up
Using frontier models like Opus 4.6 or GPT-5.3-Codex through their respective APIs costs significantly more per document than a dedicated conversion tool. Factor in the time spent crafting prompts and verifying results, and the total cost increases further.
When AI Models Make Sense for Bank Statement Conversion
There are scenarios where using Claude Opus 4.6 or GPT-5.3-Codex for PDF to CSV conversion is practical:
- One-off conversions: You have a single statement and need quick results
- Unusual formats: A statement from a small bank or foreign institution that dedicated tools haven't seen before
- Custom extraction: You need specific columns or transformations that standard tools don't support
- Prototyping: You're building your own conversion pipeline and want to test extraction logic
For everything else — regular bookkeeping, batch processing, accounting software imports, client document handling — a dedicated tool is the right choice.
The Best Approach: Purpose-Built Tools
Whether you prefer Claude or ChatGPT, the conclusion is the same: frontier AI models can convert PDF bank statements to CSV, but they are not the most efficient or reliable method for regular use.
A dedicated bank statement to excel tool offers:
- Batch processing — upload multiple PDFs at once
- 99.9% accuracy — validated extraction with error detection
- Instant Excel/CSV downloads — no copy-paste required
- 1000+ bank formats — pre-built parsers for major banks worldwide
- 10 free pages daily — test before you commit
Get Started
Skip the prompt engineering. Upload your bank statement PDF and get a clean Excel file in about 30 seconds.
Questions? Email [email protected]
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