OpenAI: GPT-4o

Review this Python function for potential bugs. It's supposed to merge two sorted lists but users report incorrect output when one list is empty.

The function has an off-by-one error in the final cleanup loops. When `i < len(list1)` is true, you're appending `list2[i]` instead of `list1[i]`, and vice versa for the second loop. This causes index errors or wrong elements when one list exhausts first. The fix: change `result.extend(list2[i:])` to `result.extend(list1[i:])` on line 12, and `result.extend(list1[j:])` to `result.extend(list2[j:])` on line 14. Also consider edge cases: if both lists are empty, the function returns an empty list correctly, but add a docstring clarifying expected input types.

GPT-4o excels at code review with multi-step reasoning—it identifies the bug, explains the symptom, provides the exact fix, and suggests defensive improvements. The 128k context window means you can paste entire modules for review. Trade-off: without unit tests in the prompt, it won't verify the fix works in all edge cases.

I have a scanned receipt image. Extract the merchant name, date, total amount, and list of items with prices into a structured JSON format.

GPT-4o would process the uploaded receipt image and return structured data like: `{"merchant": "Whole Foods Market", "date": "2024-01-15", "total": 47.23, "items": [{"name": "Organic Bananas", "price": 3.49}, {"name": "Almond Milk", "price": 4.99}, {"name": "Sourdough Bread", "price": 5.50}]}`. It handles varied receipt layouts, cursive fonts, and faded ink better than pure OCR, inferring structure from visual context—recognizing that bold text at the top is likely the merchant, and right-aligned numbers near the bottom indicate totals.

The vision capability handles real-world document variance—crumpled paper, shadows, mixed fonts—without preprocessing. At $2.50/Mtok input, processing a high-resolution receipt image costs fractions of a cent. Trade-off: extremely low-contrast or damaged receipts may require prompt refinement to specify which regions to focus on.

Translate this technical support email into French, keeping the formal tone and preserving all product SKU codes and version numbers exactly as written.

GPT-4o would produce: "Madame, Monsieur, Nous avons bien reçu votre demande concernant le produit SKU-8472-B (version 3.2.1). Nos ingénieurs ont identifié un problème de compatibilité avec les systèmes Windows 11 antérieurs à la mise à jour KB5034441. Nous vous recommandons de mettre à jour votre système ou de télécharger le correctif disponible sur notre portail d'assistance. Veuillez nous contacter si vous avez besoin d'aide supplémentaire. Cordialement, L'équipe d'assistance technique." It maintains the formal register ("Madame, Monsieur", "Cordialement") and leaves technical identifiers untranslated.

GPT-4o handles mixed-content translation—natural language plus technical tokens—without mangling codes or version strings. The model respects formatting constraints in the prompt. Trade-off: at $10/Mtok output, translating large document batches costs more than specialized translation APIs, though quality and context-awareness are higher.

When GPT-4o handles large-scale refactors without losing context

A 12-person product team needs to refactor a legacy Node.js API spread across 40+ files, each 200-400 lines. GPT-4o's 128k token window means you can feed it the entire module structure—routes, controllers, middleware, tests—in one prompt and ask it to propose a clean separation of concerns. At $2.50/Mtok input, analyzing 80k tokens of code costs $0.20, and the model returns coherent refactor plans that reference cross-file dependencies accurately. The trade-off: if your codebase exceeds 100k tokens or you're doing this daily at high volume, input costs add up fast; consider a cheaper model for iterative edits. For quarterly refactors where you need the full picture in one shot, GPT-4o is the call.

Why GPT-4o's vision mode beats OCR pipelines for document parsing

A 5-person accounting firm processes 200 vendor invoices per month—PDFs and photos with inconsistent layouts, handwritten notes, and multi-currency line items. GPT-4o's image input lets you upload the raw file and prompt for structured JSON (vendor, date, line items, totals) without a separate OCR step. At $10/Mtok output, extracting 500 tokens per invoice costs $0.005, or $1 for the full month. The model handles rotated scans, mixed fonts, and table extraction better than rule-based parsers, and you skip the integration tax of chaining Tesseract + GPT-3.5. If you're over 1,000 invoices/month, batch pricing or a fine-tuned smaller model makes more sense. Under that, GPT-4o is the simplest path to 95%+ accuracy.

When GPT-4o's reasoning justifies the $10/Mtok output cost for support

A 20-person SaaS company routes 400 inbound tickets daily across billing, technical, and sales queues. GPT-4o reads the ticket body, checks account metadata (plan tier, recent activity), and assigns priority + queue with a two-sentence explanation. The explanation is the unlock: support leads trust the routing because they see the reasoning, not just a label. At 300 tokens output per ticket and $10/Mtok, you're spending $1.20/day or $36/month. The context window matters when tickets include long email threads or attached logs—GPT-4o won't truncate mid-conversation like smaller models. If your ticket volume is under 200/day and accuracy beats speed, this is the right model. Above 1,000/day, fine-tune Llama 3 and save 80% on inference.