OpenAI: GPT-4.1 Mini

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 a boundary condition bug. When either input list is empty, the while loop never executes, but the remaining elements from the non-empty list aren't appended to the result. Add these lines after the main loop: ```python result.extend(list1[i:]) result.extend(list2[j:]) ``` This ensures any remaining elements get included. The logic inside your loop is correct—the issue only surfaces at the edges. Also consider adding explicit empty-list checks at the start for clarity, though the fix above handles it.

Demonstrates precise debugging with minimal context. The 1M+ token window means you could paste entire codebases, but this example shows the model handles focused tasks efficiently. At $0.40 input, large context dumps get expensive fast—scope your prompts.

I have a screenshot of a dashboard with three charts. Explain what story the data tells and suggest one design improvement for clarity.

The line chart shows user growth plateauing after March while the bar chart reveals a spike in support tickets during the same period—suggesting onboarding friction coincided with growth stalling. The pie chart breaks down ticket categories, with 'account setup' dominating at 41%. Design improvement: replace the pie chart with a horizontal bar chart. Pie charts make magnitude comparison difficult, especially with five similar-sized slices. Bars would let viewers instantly rank issue severity and spot the account-setup outlier.

Showcases multimodal input—the model processes visual data and provides both analytical and design feedback. The image understanding is functional but not as nuanced as vision-specialist models. Good for quick dashboard reviews, less suited for detailed UI annotation.

Write a cold email to a procurement director at a mid-sized manufacturer. We sell inventory forecasting software. Keep it under 100 words, focus on one specific pain point.

Subject: Cutting your safety stock costs without risking stockouts Hi [Name], Most manufacturers I talk to keep 30-40% excess inventory as a buffer against demand swings. That's working capital sitting idle. Our forecasting model uses your historical order data to predict demand spikes two quarters out—clients typically reduce safety stock by 18-22% in the first year without increasing backorders. Worth a 15-minute call to see if your SKU mix fits the pattern? [Your name]

Shows the model can match tone and structure to business context—direct, benefit-focused, no fluff. The output stays within constraints and targets a specific persona. Trade-off: at $1.60/Mtok output, using this for high-volume email generation adds up quickly compared to cheaper alternatives.

When 1M+ token context beats chunking for contract review

A 4-person legal tech startup processes client onboarding packets—20-40 PDFs per case, totaling 300K-800K tokens. GPT-4.1 Mini handles the entire packet in one call at $0.40/Mtok input, letting you ask cross-document questions without retrieval pipelines or chunking logic. The 1,047,576-token window means you load everything, prompt once, and get coherent summaries that reference clause conflicts across multiple agreements. Output cost is $1.60/Mtok, so a 10K-token summary runs $0.016—cheap enough to process 50+ cases daily without budget panic. If your packets consistently exceed 900K tokens or you need sub-$0.30 input pricing, switch to a longer-context model like Claude 3.5 Sonnet. Otherwise, this is the simplest path from PDF dump to structured intake report.

Why image+text modality wins for visual troubleshooting at scale

A 12-person SaaS support team fields 200+ tickets daily, half with screenshots of error states or UI confusion. GPT-4.1 Mini's image modality lets agents paste the screenshot and ticket text into one Switchy prompt, getting triage tags and suggested replies in 3-4 seconds. At $0.40 input per million tokens, a 2K-token ticket with one image costs under $0.001 to classify—you'll spend $30/month processing 30K tickets. The model reads UI text in screenshots well enough to route "billing page 404" versus "dashboard layout broken" without custom OCR. If you need faster than 4-second p95 latency or you're over 100K tickets/month, consider a fine-tuned smaller model. For mid-scale teams mixing text and visual context, this hits the cost-speed-accuracy sweet spot.

When file modality cuts 40% off your data pipeline build time

A 3-person analytics consultancy ingests client CSVs, PDFs, and spreadsheets to generate weekly insight reports. GPT-4.1 Mini's file modality means you upload the raw files via API, prompt for trend summaries or anomaly flags, and skip the parse-clean-embed pipeline you'd otherwise write. A 50MB CSV with 200K rows fits comfortably in the context window; the model returns structured JSON or prose summaries in one call. At $0.40/Mtok input and $1.60/Mtok output, a 100K-token file analysis with 5K-token output costs $0.048—cheap enough to run 500 reports/month for under $25. If your files exceed 800K tokens regularly or you need guaranteed sub-2-second response, build the pipeline. For ad-hoc or weekly batch jobs, this eliminates two weeks of ETL work.