OpenAI: GPT-4.1 (batch)

Review this Python function for edge cases and potential bugs. Focus on the input validation and error handling: ```python def calculate_discount(price, discount_percent): return price - (price * discount_percent / 100) ```

This function has several edge cases that could cause issues: 1. **No input validation**: Accepts negative prices or discount percentages without complaint. A -20% discount on $100 would return $120. 2. **Type safety**: No checks for non-numeric inputs. Passing strings or None will raise TypeError at runtime. 3. **Percentage bounds**: Allows discount_percent > 100, resulting in negative final prices (customer gets paid). 4. **Floating-point precision**: For financial calculations, this can accumulate rounding errors. Recommend adding: type hints, input validation (price >= 0, 0 <= discount_percent <= 100), and using Decimal for currency math.

Demonstrates systematic code review with security and correctness focus. The 1M+ token context window means this model handles entire codebases in one prompt, but batch processing adds 50% latency — unsuitable for interactive debugging sessions.

Analyze this customer support ticket and draft a response. Ticket: "I was charged twice for my March subscription. Order IDs #8847 and #8851. I only authorized one payment. This is the second billing error in three months. If this isn't resolved by Friday I'm canceling." Context: Customer has Premium plan ($49/mo), account since 2022, previous ticket resolved in 24hrs.

**Analysis**: Frustrated long-term customer, second billing issue, explicit cancellation threat with deadline. Requires immediate escalation. **Draft response**: Hi [Name], I've immediately flagged both charges for our billing team. You're absolutely right — one charge is correct, and I can see the duplicate posted 3 hours later. I'm processing a $49 refund right now (2-3 business days to your account). I've also added a $49 credit to your next invoice as an apology for the repeat issue. Your account is noted for manual billing review going forward. I'll email you Thursday with confirmation that everything's resolved. Thank you for your patience while we fix this.

Shows nuanced tone calibration — acknowledges frustration without over-apologizing, provides concrete timeline and compensation. The model's file-handling modality would let you attach ticket history or policy docs directly, though batch mode means you're processing support queues in bulk, not real-time.

I'm attaching a 40-page technical specification PDF for a new API. Generate a 2-page executive summary highlighting: business impact, implementation complexity, security considerations, and recommended timeline. Assume audience is non-technical leadership. [PDF would be attached in actual use]

Given a detailed API specification document, this model would produce a summary structured like: **Business Impact**: The proposed API enables third-party integrations, potentially expanding market reach by 30-40% based on partner interest surveys. Revenue model shifts from one-time licenses to recurring integration fees. **Implementation Complexity**: Moderate. Requires 8-10 engineering weeks, primarily for OAuth2 implementation and rate-limiting infrastructure. Existing microservices architecture reduces integration risk. **Security Considerations**: Introduces new attack surface. Requires: API key rotation policy, request signing, IP allowlisting for enterprise tier. Compliance review needed for GDPR data-sharing clauses. **Timeline**: Recommend 4-month rollout: 2 months development, 1 month beta testing with 3-5 partners, 1 month phased public launch.

Illustrates multi-modal document processing — the model ingests technical PDFs and translates jargon into business language. The 1M+ token context means even lengthy specs fit in one prompt, but $4/Mtok output pricing makes this expensive for high-volume document processing compared to smaller models.

Why batch mode wins for high-volume contract extraction at scale

A 12-person legal ops team needs to extract clauses from 800 vendor contracts every quarter. GPT-4.1 batch cuts input costs to $1/Mtok—half the real-time rate—and the 1M+ token context means each contract (typically 40-80k tokens with exhibits) fits in a single call. You submit the batch at 6pm, results land by 7am, and the team reviews extractions over coffee instead of waiting on API calls. The trade-off: batch jobs can take up to 24 hours, so this only works if you can afford the delay. If you need answers in under an hour, pay the real-time premium. But for quarterly reviews, month-end closes, or any workflow where overnight is fast enough, batch mode turns a $400 job into a $200 job with zero code changes.

When 1M tokens lets you refactor across 30 files in one prompt

A 5-engineer startup is migrating a Python monolith to microservices. They need to trace a data model through 30 files (totaling 180k tokens) to identify every place it's instantiated, serialized, or validated. GPT-4.1's context window swallows the entire module tree in one call, so the model sees cross-file dependencies that smaller contexts miss. At $1 input per million tokens, analyzing the full codebase costs $0.18—cheaper than running three separate calls on a 128k model and stitching the results. The catch: if your refactor needs to run in CI on every commit, batch latency kills the loop. Use this for one-time migrations or weekly architecture reviews, not per-PR checks.

How batch pricing makes 10k support tickets affordable to summarize

A 20-person SaaS company collects 10,000 support tickets per quarter and wants a thematic rollup for the product roadmap meeting. Each ticket averages 600 tokens; the full corpus is 6M tokens. At $1/Mtok input, the entire analysis costs $6 in token fees. You batch-submit on Friday, get the synthesis Monday morning, and the PM presents it Tuesday. Real-time pricing would triple the cost for a task that doesn't need sub-hour turnaround. The boundary: if you're running this monthly or more often, build a streaming pipeline with a cheaper model. But for quarterly deep-dives where thoroughness beats speed, GPT-4.1 batch turns a $20 job into a $6 job and the context window means you're not chunking tickets into lossy summaries.