OpenAI GPT-5.6 Luna vs DeepSeek V4 Flash: Audit Budget
Verdict first: OpenAI GPT-5.6 Luna wins if your audit workflow is output-heavy and you want the lowest listed input cost. DeepSeek V4 Flash punches back hard on balanced pricing, especially when you need lots of generated notes. This is a cost fight, not a benchmark coronation.
Price: Luna lands the first clean shot
OpenAI GPT-5.6 Luna is listed at $0.10/1M input tokens and $0.60/1M output tokens. DeepSeek V4 Flash is listed at $0.14/1M input tokens and $0.28/1M output tokens.
That means Luna is cheaper on input by $0.04 per 1M tokens. If you’re feeding mountains of audit logs, policy text, detector reports, classroom submissions, or citizen-science records into a model for classification, that gap matters. Luna takes the input-volume round.
But DeepSeek V4 Flash hits back on output. Its $0.28/1M output price is less than half Luna’s $0.60/1M output price. If your system writes long rationales, appeal summaries, evidence packets, or reviewer notes, DeepSeek can flip the math fast.
Trust workflows: don’t let either model be the judge and jury
The Verge’s AI-detector piece is the right backdrop here: automated suspicion is becoming its own problem. For that kind of work, neither OpenAI GPT-5.6 Luna nor DeepSeek V4 Flash should be treated as a final authority on whether something is AI-written.
Use them as triage engines: flag inconsistencies, summarize evidence, compare claims against logs, and prepare material for a human reviewer. The Show HN audit-log project points to the same lesson from another corner: records need to survive challenge, not just sound convincing.
Deployment math: input-heavy or output-heavy?
If your workload is mostly reading, Luna’s $0.10/1M input price is the cleaner jab. Think ingestion, tagging, retrieval prep, and short labels.