OpenAI: GPT-6 Luna Pro (batch)

Review this 450-page M&A contract for non-standard indemnification clauses and flag any that shift liability beyond typical SaaS acquisition terms.

In this illustrative example, the model would identify seven non-standard indemnification provisions across sections 8.3, 12.1, and Exhibit D. It would flag uncapped IP warranty exposure in section 8.3(b) that extends beyond the standard 12-month survival period to perpetuity, note the unusual customer data breach liability allocation in 12.1(d) that makes the acquirer responsible for pre-closing incidents discovered post-closing, and highlight the reverse indemnification cap in Exhibit D-2 that's set at 3x purchase price rather than the industry-standard 1x. Each finding would include the specific clause text, page reference, and a comparison to market-standard SaaS M&A language.

The 1.05M token context window enables analysis of complete transaction documents without chunking or summarization loss. This matters for legal review where a missed cross-reference can create material risk. The batch processing model suits document review workflows where 24-hour turnaround is acceptable, though real-time negotiation support would require the standard API.

Generate a Python data pipeline that ingests this 18-month Snowflake query log, identifies the 200 most expensive queries, clusters them by access pattern, and outputs optimization recommendations with estimated cost savings.

In this illustrative example, the model would produce a complete pipeline using pandas and scikit-learn: CSV ingestion with schema validation, cost calculation per query using warehouse credits and execution time, K-means clustering (k=12) to group queries by table access patterns and time-of-day distribution, and a final report generator. The code would include error handling for malformed log entries, parameterized cluster count, and a cost-benefit estimator that projects savings from recommended indexes, materialized views, and warehouse right-sizing. The output would be production-ready with type hints, docstrings, and unit test stubs for the core clustering logic.

File modality support means the model can directly ingest the query log without requiring you to paste 18 months of data into a prompt. The $0.05/Mtok input pricing makes analyzing large operational datasets economically viable—this job would cost roughly $0.50 in input tokens. The batch endpoint's lower output cost ($0.25 vs. standard API rates) suits report generation where you're producing lengthy analysis documents.

Here are 40 customer support tickets from last week (images attached). Extract the core issue from each, map them to our product taxonomy, identify which three product areas are generating the most confusion, and draft FAQ entries for each.

In this illustrative example, the model would process each ticket image (screenshots, forwarded emails, handwritten notes), extract the customer's question and any visible error messages or UI states, then categorize issues using your product taxonomy. It would identify that 14 tickets relate to the new billing dashboard (confusion about proration logic), 9 concern the API rate limiting changes (unclear error messages), and 7 involve the mobile app's offline mode (sync conflict resolution). For each area, it would draft a 150-word FAQ entry in your support voice, incorporating the specific language customers used in tickets and addressing the underlying mental model mismatch that's causing the confusion.

Image understanding enables processing of real support artifacts—screenshots with annotations, forwarded email chains, photos of error screens—without requiring manual transcription. The large context window lets you submit an entire week's tickets in one job, preserving cross-ticket patterns that single-ticket analysis would miss. Batch processing fits support team workflows where overnight analysis informs next-day prioritization, though it wouldn't suit real-time ticket triage.

Why Luna Pro batch wins for overnight contract analysis pipelines

A 12-person legal ops team processes 200+ vendor contracts monthly, extracting clause risks and compliance flags into structured JSON. Luna Pro batch cuts their per-document cost to $0.05 input / $0.25 output per Mtok—roughly 40% below real-time alternatives—while the 1.05M token window handles full MSAs plus exhibits in a single call. They queue the batch at 6pm, pull results at 8am, and route flagged contracts to paralegals. The trade-off: batch jobs take 12-24 hours, so this only works when you can afford overnight latency. If you need answers in under an hour, pay the real-time premium. Otherwise, Luna Pro batch is the correct call for any document workflow where volume matters more than speed.

When to batch-process large refactors across 50+ file repos

A 4-engineer startup inherited a 60-file Python monolith and needs to migrate from Flask to FastAPI across every route handler. Luna Pro batch ingests the entire codebase (averaging 800K tokens with dependencies) and returns a refactor plan plus diffs for each file. At $0.05/$0.25 per Mtok, the full job costs under $0.30—cheap enough to run nightly as the codebase evolves. The file and image modalities let them include architecture diagrams and config samples in the prompt. The catch: batch latency means this is a planning tool, not an interactive copilot. If your team codes in tight feedback loops, stick with real-time models. If you can queue refactors overnight and review diffs in the morning standup, Luna Pro batch is the right pick.

How a 20-person SaaS team tags 5K monthly support tickets overnight

A customer success team collects 5,000 support tickets and NPS comments each month and needs sentiment scores, feature requests, and churn signals extracted into their data warehouse. Luna Pro batch processes the full corpus for under $2 total—each ticket averages 400 tokens input, 50 tokens output—and the 1.05M context window lets them include the last 90 days of product release notes as grounding context in every call. They run the batch weekly, load results into Looker, and surface trends in their Monday review. The boundary: if you need real-time ticket routing or same-day escalation, batch latency kills the workflow. But for retrospective analysis where cost per ticket matters and you can wait 24 hours, Luna Pro batch is the obvious choice.