OpenAI: GPT-4o-mini (2024-07-18)

Review this Python function for potential bugs. It's supposed to calculate the median of a list, but users report incorrect results on even-length lists.

The function has a critical indexing error in the even-length branch. When the list has an even number of elements, you're calculating `(sorted_list[mid] + sorted_list[mid]) / 2` — you're adding the same middle element to itself instead of averaging the two central values. Change the second index to `mid + 1`. Also consider adding a guard clause for empty lists, which currently would raise an IndexError. The sorting step works correctly, but if performance matters for large lists, you might want to use a selection algorithm instead of full sorting.

This example shows GPT-4o-mini's solid code review capability at a fraction of the cost of larger models. The 128K context window means it can review entire modules in one pass. The response is accurate and actionable, though it lacks the deeper architectural suggestions you'd get from GPT-4o — a reasonable trade-off given the 4× lower output pricing.

I have a photo of a handwritten recipe card from my grandmother. Can you transcribe it and convert the measurements to metric?

The model would process the uploaded image and return a structured transcription: "Chocolate Chip Cookies — 2 cups flour (250g), 1 cup butter (225g), 3/4 cup sugar (150g), 2 eggs, 1 tsp vanilla extract (5ml), 1/2 tsp salt (2.5g), 1 tsp baking soda (5g), 2 cups chocolate chips (350g). Cream butter and sugar, add eggs and vanilla, mix dry ingredients separately, combine, fold in chips. Bake 375°F (190°C) for 10-12 minutes." The transcription would accurately capture cursive handwriting and faded ink typical of old recipe cards.

The multimodal capability handles real-world image-plus-text tasks that previously required separate OCR and LLM steps. At $0.15/Mtok input, processing images is economical for batch workflows. However, complex handwriting or low-contrast photos may require higher-resolution preprocessing — this isn't a specialized OCR model.

Summarize the key arguments from these three research papers on urban heat islands. Focus on proposed mitigation strategies and their cost-effectiveness. [Attaches three 20-page PDFs]

The model would analyze all three papers within its 128K context window and produce a synthesis: "All three studies agree that increasing urban vegetation provides the highest cost-benefit ratio for heat mitigation. Chen et al. found that strategic tree placement reduces surface temperatures by 2-4°C at $50-80 per tree over 20 years. Rodriguez's meta-analysis shows cool roofing materials cost 15-30% more upfront but pay back through energy savings in 5-7 years in hot climates. The Singapore case study demonstrates that combined approaches (green roofs + permeable pavement + shade structures) achieve 6-8°C reductions but require $200-400/m² investment. Key disagreement: Chen argues for distributed small interventions; Rodriguez advocates for concentrated pilot zones."

This showcases the 128K context window handling multiple long documents simultaneously — a task that would require chunking strategies with smaller models. The synthesis quality is strong for the price point, though the model occasionally misses subtle methodological critiques that GPT-4o would catch. For research teams on a budget, this is a practical document analysis tool.

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

A 12-person SaaS company routing 600 inbound support emails per day needs fast categorization and draft responses without burning through their AI budget. GPT-4o-mini wins here because the $0.60/Mtok output rate means each 300-token draft costs roughly $0.0002—under $120/month even at this volume. The 128k context window lets you dump the last 20 customer interactions plus your full help docs into each prompt, so responses stay consistent with your brand voice and past solutions. Quality sits below GPT-4o for nuanced edge cases, but 80% of support tickets are repeat questions where mini's speed and cost make it the obvious call. If your ticket complexity pushes above 30% requiring human escalation, test GPT-4o on a sample before committing.

Why compliance teams use GPT-4o-mini for contract redlining at scale

A 4-person legal ops team reviews 40 vendor contracts monthly, flagging non-standard clauses against a 60-page playbook. GPT-4o-mini's 128k context window fits an entire contract plus the full playbook in one prompt, so you're not chunking documents or losing cross-references. At $0.15/Mtok input, analyzing a 15k-token contract with a 50k-token playbook costs about $0.01 per review—cheap enough to run every contract twice for validation. The model handles structured extraction well (pulling liability caps, termination clauses, indemnity language into a spreadsheet), but struggles with ambiguous legal interpretation compared to GPT-4o. If more than 20% of your contracts involve custom jurisdictions or novel deal structures, upgrade to GPT-4o for those and keep mini on the standard MSAs.

When GPT-4o-mini turns 90-minute calls into Notion updates in seconds

A 6-person agency runs 8 client calls per week and needs each transcribed conversation (roughly 12k tokens) summarized into action items, decisions, and next steps posted to Notion within 60 seconds of the call ending. GPT-4o-mini processes this in under 10 seconds at $0.08 per summary (12k input + 1k output), keeping the workflow fast enough that your PM can review and publish before the next meeting starts. Image input support means you can also feed in whiteboard photos or slide decks from screen shares, pulling key points into the same summary. The model occasionally misattributes action items when multiple speakers overlap, so you'll want a human spot-check before client-facing distribution. For internal standups where speed beats perfection, mini is the right trade-off.