OpenAI: GPT-4 Turbo

Review this Python function for edge cases and suggest improvements. The function calculates shipping costs based on weight and distance, but users report unexpected charges for international orders.

The function has three issues. First, it doesn't handle the case where `distance` exceeds 5000km — the rate multiplier caps at domestic rates, so international shipments get undercharged. Second, `weight` isn't validated for negatives or zero, which could return negative costs. Third, the currency conversion happens before applying the distance multiplier, so exchange rate fluctuations affect domestic and international orders differently. I'd add input validation, extend the distance brackets to 10000km with a 2.4x multiplier for international, and move currency conversion to the final step after all calculations.

This example shows GPT-4 Turbo's strong logical reasoning across business context and code structure. The 128k context window means it can review entire codebases in one pass. However, at $30/Mtok output, verbose explanations get expensive — teams often need to prompt for conciseness.

I'm preparing a board presentation on Q3 revenue trends. Here's our sales data CSV (42 columns, 8,000 rows). Identify the three most significant patterns that explain our 12% revenue decline, and suggest one tactical change for Q4.

Three patterns stand out. First, enterprise deals (>$50k) dropped 31% while SMB deals grew 18% — your average contract value fell from $34k to $23k. Second, churn spiked in month two of Q3 (July) across all segments, coinciding with your pricing change rollout. Third, new logo acquisition stayed flat, but expansion revenue from existing customers dropped 40%. The data suggests customers are downgrading rather than leaving entirely. For Q4: pause upsell motions and focus on usage activation in the first 30 days. Your at-risk cohort shows 60% are under 40% feature adoption.

GPT-4 Turbo handles large structured datasets well within its 128k window, connecting quantitative patterns to business implications. The model balances analysis with actionable recommendations. Trade-off: multimodal input means you can include charts, but text-only analysis is often clearer and cheaper than vision processing.

Translate this product launch email into French, German, and Japanese. Preserve the enthusiastic tone but adapt idioms for each market. The English version uses 'game-changer' and 'hit the ground running' — find natural equivalents that don't sound like direct translations.

For French, I'd replace 'game-changer' with 'une véritable révolution' (a real revolution) and 'hit the ground running' with 'prendre un départ sur les chapeaux de roues' (start with wheels spinning). In German, 'game-changer' becomes 'ein Meilenstein' (a milestone) and the running idiom translates to 'direkt durchstarten' (launch directly). Japanese requires more restructuring: 'game-changer' works as '業界を変える革新' (innovation that changes the industry), while the urgency of 'hit the ground running' maps to 'スタートダッシュを決める' (nail the starting dash). Each version maintains the energy while respecting how each culture expresses momentum and innovation.

This demonstrates GPT-4 Turbo's cultural localization beyond literal translation — it understands pragmatic equivalence across languages. The model's training includes enough multilingual data to catch tone mismatches. Limitation: output quality varies by language; less-common languages get more literal translations than the major ones shown here.

When 128K context beats RAG for contract cross-reference work

A 4-person legal ops team needs to cross-reference clauses across 15-20 vendor contracts during M&A diligence. GPT-4 Turbo fits the entire contract set in one context window—no chunking, no vector search, no retrieval errors. At $10/Mtok input, loading 100K tokens of contracts costs $1 per query. The model handles multi-hop reasoning across documents without losing thread: "find all force majeure clauses that reference pandemic language and compare termination notice periods." If you're running fewer than 200 discovery queries per deal, the context window advantage outweighs the per-token cost. Beyond that volume, consider caching strategies or a cheaper 128K alternative, but for ad-hoc diligence work where accuracy trumps speed, this is the call.

Why vision + JSON mode works for invoice processing at scale

A 10-person accounting firm processes 300 supplier invoices per month—photos, PDFs, scanned forms with inconsistent layouts. GPT-4 Turbo's vision input handles the image directly; no separate OCR pipeline. The model extracts line items, totals, and vendor details into JSON with 95%+ accuracy on first pass. At $30/Mtok output, a 500-token JSON response costs $0.015—negligible compared to manual keying time. The 128K context means you can batch-process 20 invoices in one call if needed, though single-invoice calls keep error isolation clean. If your invoice volume exceeds 2,000/month, test GPT-4o for the same vision capability at half the output cost. Below that threshold, Turbo's reliability justifies the premium.

When output cost kills the business case for chat moderation

A 15-person SaaS support team wants to auto-classify inbound tickets—bug, billing, feature request—and draft initial responses. GPT-4 Turbo handles the reasoning well: it parses messy user language and suggests next steps. But at 400 tickets/day and 300 tokens per response, output costs hit $3,600/month ($30/Mtok × 0.3M tokens × 30 days). The model works; the unit economics don't. If ticket volume stays under 100/day, the $900/month cost is defensible for the time saved. Above that, switch to GPT-4o ($15/Mtok output) or use Turbo only for the 20% of tickets that need the extra reasoning horsepower. Don't default to the most capable model when a cheaper one closes 80% of cases.