OpenAI: GPT-6 Sol Pro (batch)

Review this 40,000-line TypeScript monorepo for architectural debt. Flag patterns that will hurt us at scale and suggest specific refactors.

In a model response spanning roughly 800 tokens, GPT-6 Sol Pro identifies three critical architectural issues: a circular dependency chain between the auth and user modules that creates brittle coupling, a data-access layer that bypasses type safety in 14 locations, and a notification system that will bottleneck at ~5000 concurrent users due to synchronous processing. Each issue includes a concrete refactor path—dependency injection for auth, a typed repository pattern for data access, and an event-driven queue for notifications—with code snippets showing before/after structure.

The 1.05M token context window lets this model ingest an entire medium-sized codebase in one pass, producing holistic architectural analysis rather than file-by-file comments. Batch processing keeps costs manageable for this workload ($1/Mtok input), though the lack of public benchmarks means you're trusting OpenAI's internal evals for code reasoning quality.

I'm attaching 600 pages of discovery documents from a contract dispute. Summarize the timeline, identify conflicting claims between parties, and flag which exhibits support each side's position.

The model produces a 1,200-token structured analysis: a chronological timeline with 18 key events, a table comparing Party A's claims versus Party B's counterclaims across five disputed issues, and a cross-referenced exhibit map showing which documents (by Bates number) support each assertion. It highlights three factual contradictions where testimony diverges and notes two exhibits that appear to support both sides depending on interpretation, calling out the ambiguity explicitly.

Long-context legal review is a natural fit for this model's 1M+ token window—600 pages fit comfortably with room for detailed output. The batch API's asynchronous nature works well for non-urgent legal prep, though real-time depositions would require the standard API. Output pricing ($5/Mtok) stays reasonable when the alternative is paralegal hours.

Analyze this 18-month dataset of customer support tickets (CSV, 95MB, 340k rows). What are the top failure modes, which ones correlate with churn, and what product changes would address the costliest issues?

The model returns a 900-token analysis identifying five failure clusters: authentication loops (23% of tickets, 2.1x churn correlation), payment processing errors (11% of tickets, 3.4x churn correlation), and three feature-confusion patterns. It quantifies the business impact—payment errors alone drive an estimated $340k annual revenue loss—and proposes three product interventions: a retry mechanism with user feedback for payments, a session-persistence fix for auth, and in-app contextual help for the confusion patterns. Each recommendation includes implementation complexity estimates.

File upload support and massive context window make this model viable for bulk data analysis that would normally require a data science pipeline. Batch processing suits this exploratory workload, though the absence of public benchmarks on structured data reasoning means you should validate outputs against known ground truth before trusting production insights.

Why Sol Pro batch mode wins for overnight tagging jobs

A 12-person compliance team needs to classify 40,000 contract PDFs every quarter—tagging risk level, jurisdiction, renewal dates. Sol Pro batch cuts the input cost to $1/Mtok (50% off real-time pricing) and the 1.05M token window means each contract gets full context without chunking. Run the job overnight: queue all 40k files, get results by morning, pay roughly $80 in input tokens for the entire batch. The trade-off is latency—batch jobs take 12-24 hours to complete, so this only works when you can wait. If you need same-day turnaround on ad-hoc requests, switch to the real-time endpoint. For scheduled bulk work where cost matters more than speed, batch mode is the call.

When Sol Pro handles entire repositories in one prompt

A 5-engineer startup wants to generate architecture docs from their 80-file TypeScript monorepo—controllers, models, utilities, tests—without stitching summaries together. Sol Pro's 1.05M token context fits the entire codebase in a single prompt, so the model sees cross-file dependencies and can write coherent system-level docs. At $1 input per Mtok, analyzing a 200k-token repo costs $0.20 per run. The batch discount makes nightly doc regeneration cheap enough to automate. The ceiling: if your repo exceeds 1M tokens or you need sub-second responses for IDE autocomplete, this isn't the tool. For weekly or daily batch jobs that need full-repo reasoning, Sol Pro batch delivers the context at the right price.

How Sol Pro batch turns 100-page filings into executive briefs

A 3-person investment research shop processes 200 quarterly 10-Q filings each earnings season—extracting revenue trends, risk factors, management commentary—and needs consistent formatting across all reports. Sol Pro batch handles the full filing (typically 60-80k tokens including tables and footnotes) without truncation, and the $1 input rate keeps per-filing cost under $0.10. Queue the entire batch Friday night, review summaries Monday morning. The model's file and image modalities mean it can parse embedded charts and tables directly. The boundary: if you're analyzing filings in real-time during earnings calls, batch latency kills the workflow. For post-quarter analysis where you're optimizing cost over 200+ documents, batch mode is the right infrastructure choice.