OpenAI: GPT-3.5 Turbo (batch)

Summarize this 3-page customer support transcript into bullet points highlighting the customer's main issue, steps taken, and resolution status.

This example would produce a structured summary extracting key information from a lengthy support conversation. The model identifies the core complaint (billing discrepancy for a subscription upgrade), lists troubleshooting steps chronologically (account verification, transaction log review, manual credit application), and clearly states the outcome (issue resolved, $47 credit applied, customer satisfied). The formatting is clean with proper bullet hierarchy, and the tone remains neutral and factual throughout.

GPT-3.5 Turbo excels at distilling long-form text into structured formats, making it ideal for batch processing support logs or meeting notes. The 16K context window handles most single-conversation transcripts comfortably. However, nuanced sentiment analysis may miss subtle customer frustration that newer models would catch.

Generate 50 product description variations for 'wireless noise-canceling headphones' targeting different customer segments: audiophiles, commuters, remote workers, students, and fitness users.

This batch request would return 50 distinct descriptions, each 40-60 words, tailored to specific buyer personas. The audiophile version emphasizes frequency response and driver quality; the commuter version highlights portability and battery life; the remote worker version focuses on call clarity and comfort for all-day wear. Each variation maintains consistent product facts while shifting emotional appeal and feature prioritization to match segment needs.

The batch API's $0.25/$0.75 pricing makes high-volume content generation economically viable—50 descriptions cost roughly $0.04 total. GPT-3.5's output is serviceable for e-commerce copy but lacks the creative flair of GPT-4 for premium brand voice. Best for volume over artistry.

Extract structured data from these 200 unformatted customer feedback emails: sentiment (positive/negative/neutral), product mentioned, issue category, and urgency level. Return as CSV.

This example would process a batch of raw email text and return a CSV with four columns per email. Each row contains the extracted sentiment classification, the specific product name mentioned (even if misspelled or abbreviated), a standardized issue category from a predefined taxonomy (shipping, quality, billing, feature request), and an urgency score (1-5). The model handles varied email formats, informal language, and implicit sentiment cues with reasonable accuracy.

Batch processing shines for data extraction tasks where 50% cost savings and 24-hour latency are acceptable trade-offs. GPT-3.5 handles straightforward classification well but may misclassify edge cases—sarcasm, mixed sentiment, or ambiguous product references—that require human review or a more sophisticated model.

Batch processing 10,000+ support emails while your team sleeps

A 12-person SaaS support team gets 15,000 emails overnight across four time zones. They need every message tagged (bug/feature/billing/spam) and routed before the morning shift starts at 6am ET. GPT-3.5 Turbo batch mode cuts their classification cost to $0.25/Mtok input—half the real-time price—and the 24-hour processing window fits perfectly since no one needs answers until morning anyway. At 200 tokens per email average, that's 3M tokens in, roughly $0.75 for the entire night's work. The 16K context window handles even the longest threads with full history. If you need answers in under 10 minutes, this won't work—but for any workflow where 24-hour latency is acceptable and volume is high, batch mode turns GPT-3.5 into the cheapest production-grade classifier available.

When 5,000 SKUs need SEO-friendly copy on a $50 budget

An e-commerce team inherits a catalog of 5,000 products with manufacturer specs but no customer-facing descriptions. They need 150-word SEO-optimized blurbs for each item, and they need them written this week. GPT-3.5 Turbo batch mode at $0.75/Mtok output makes this a $112.50 job (5,000 products × 150 words × 1.3 tokens/word × $0.75/M). Real-time GPT-4 would cost $750 for marginally better prose that most shoppers won't notice. The 16K context lets you feed product specs, brand voice guidelines, and two example descriptions in every prompt. Batch the requests Monday night, get results Tuesday afternoon, spot-check 50 samples, and publish. If you're writing 10 descriptions, pay for real-time GPT-4—but at catalog scale, batch 3.5 is the only model that keeps unit economics sane.

Analyzing 30 days of customer feedback for $2 instead of $40

A 4-person product team collects NPS comments, app store reviews, and chat transcripts all month—roughly 8,000 pieces of feedback totaling 1.2M tokens. On the first of each month, they need sentiment scores, theme extraction, and a ranked list of pain points for the roadmap meeting. They submit the entire batch to GPT-3.5 on the 30th, get results on the 31st, and walk into the meeting with a $2 analysis instead of the $40 real-time GPT-4 would cost. The 16K window means each review gets analyzed with the previous month's theme list for consistency. The trade-off: if a critical bug surfaces in the feedback on the 15th, you won't see it flagged until month-end. If you need daily monitoring, pay for real-time—but for monthly retrospectives where yesterday's data is fine, batch mode makes LLM analysis a rounding error in the budget.