OpenAI: GPT-4o-mini

Review this Python function for potential bugs and suggest improvements. Focus on edge cases and performance: ```python def find_duplicates(items): seen = [] dupes = [] for item in items: if item in seen: dupes.append(item) seen.append(item) return dupes ```

The function has a performance issue: checking `if item in seen` on a list is O(n), making the overall complexity O(n²). Edge cases to consider: (1) empty input returns empty list correctly, (2) all duplicates vs. no duplicates both work, but (3) the function adds duplicates multiple times if an item appears 3+ times. Improvements: use a set for `seen` to get O(n) performance, and use a set for `dupes` initially to avoid recording the same duplicate multiple times, then convert to list if order matters.

This example shows GPT-4o-mini's code review capability within its 128k context window, suitable for analyzing functions with surrounding context. The model identifies both algorithmic complexity and logical bugs. At $0.15/$0.60 per million tokens, it's 3-6× cheaper than GPT-4o for this task, though responses may lack the deeper architectural insight of larger models.

I have a photo of a restaurant receipt. Extract all items, prices, tax, and tip into a structured JSON format. The receipt is handwritten and slightly blurry.

The model would process the uploaded receipt image and return structured data like: `{"items": [{"name": "Margherita Pizza", "price": 18.50}, {"name": "Caesar Salad", "price": 12.00}], "subtotal": 30.50, "tax": 2.74, "tip": 6.00, "total": 39.24}`. For handwritten text, the model attempts character recognition but may misread ambiguous numbers (like 5 vs. 6) or cursive letters. It typically flags low-confidence extractions in a notes field.

GPT-4o-mini handles multimodal input (text + image) in a single request, making it practical for document parsing workflows. The 128k context window accommodates multiple receipt images in one call. Vision accuracy is good for printed text but degrades with handwriting or poor lighting—users should validate extracted financial data before automated processing.

Translate this customer support email into French, German, and Spanish. Maintain a professional but warm tone: "Thanks for reaching out! I've checked your account and see the payment went through yesterday. Your premium features should activate within 2 hours. Let me know if you're still having trouble after that."

**French:** Merci de nous avoir contactés ! J'ai vérifié votre compte et constate que le paiement a été effectué hier. Vos fonctionnalités premium devraient s'activer dans les 2 heures. N'hésitez pas à me recontacter si vous rencontrez toujours des difficultés après ce délai. **German:** Vielen Dank für Ihre Nachricht! Ich habe Ihr Konto überprüft und sehe, dass die Zahlung gestern eingegangen ist. Ihre Premium-Funktionen sollten innerhalb von 2 Stunden aktiviert werden. Lassen Sie mich wissen, falls danach noch Probleme bestehen. **Spanish:** ¡Gracias por contactarnos! He revisado tu cuenta y veo que el pago se procesó ayer. Tus funciones premium deberían activarse en 2 horas. Avísame si sigues teniendo problemas después de ese tiempo.

This demonstrates GPT-4o-mini's multilingual capability across three target languages in one request, preserving tone and context. The model handles informal-to-formal register shifts (Spanish uses 'tu' here, matching the original's warmth). At 1/6th the output cost of GPT-4o, it's economical for high-volume translation tasks, though specialized translation models may better preserve regional idioms or technical terminology.

When GPT-4o-mini handles 500+ daily tickets under budget

A 12-person SaaS support team routing 600 inbound tickets daily needs fast classification without burning through API costs. GPT-4o-mini wins here because the $0.15/$0.60 per Mtok pricing means you can process a 1,200-token ticket (question + metadata) and generate a 300-token routing decision for roughly $0.00036 per ticket—about $216/month at that volume. The 128k context window lets you include the last 20 customer interactions in each call, so routing accuracy stays high even for repeat issues. If your tickets average under 2k tokens and you're prioritizing cost over the absolute ceiling performance of full GPT-4o, this is the model. Above 1,000 tickets/day, the savings gap widens further and justifies the switch from pricier alternatives.

Why GPT-4o-mini works for weekly 50-doc research digests

A 4-person investment research team uploads 50 PDFs every Monday—earnings reports, analyst notes, regulatory filings—and needs 200-word summaries by Tuesday morning. GPT-4o-mini handles this because the 128k context window fits most financial documents in a single call (a typical 10-page PDF is 8k-12k tokens), and the $0.15 input rate means processing 50 docs at 10k tokens each costs about $7.50 in input alone. Output is another $6 for 50 summaries at 200 words. Total batch cost under $15, which beats hiring a junior analyst for 4 hours. The trade-off: if you need citation extraction or complex multi-document reasoning, you'll hit accuracy limits and should test full GPT-4o. For straightforward summarization at this frequency, GPT-4o-mini clears the bar.

When this model powers internal knowledge bots for under $50/month

A 20-person startup runs a Slack bot that answers HR, IT, and onboarding questions by searching a 30k-token knowledge base (employee handbook, FAQ docs, benefits PDFs). The bot fields 200 questions/week, each requiring a 35k-token context (knowledge base + conversation history) and a 150-token answer. GPT-4o-mini costs about $0.0074 per question at those token counts—$6/month at 200 questions/week, well under the $50 budget threshold where you'd consider self-hosting or a cheaper model. The image and file modalities mean employees can upload screenshots of error messages or PDF snippets directly in Slack, and the bot parses them inline. If your question volume exceeds 1,000/week or you need sub-500ms latency, test response times under load first. Otherwise, this is the obvious pick for internal tooling.