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Harvey turns legal context into stronger drafts with GPT-6 Astra

OpenAI published a Harvey case study showing that GPT-6 Astra improves legal document structuring and context awareness, cutting manual drafting time by roughly 40%. The company said the model can produce structured, matter-aware legal documents in a single pass instead of relying on heavy retrieval scaffolding and chunk-stitching, though production setups must adapt to model-prompting changes and face higher per-call token costs and latency for larger contexts.

read1 min views1 publishedSep 24, 2026
Harvey turns legal context into stronger drafts with GPT-6 Astra
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OpenAI

Harvey turns legal context into stronger drafts with GPT-6 Astra

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

GPT-6 Astra improves legal document structuring and context awareness, reducing manual drafting time by ~40%. This allows legal teams to shift focus from drafting to higher-value strategic work, streamlining workflow integration for firms already using AI-augmented tools. Production setups must adapt to model-prompting changes, but gains justify the transition.

GPT-6 Astra now handles long-context legal drafting well enough to produce structured, matter-aware documents in one pass rather than requiring heavy retrieval scaffolding and chunk-stitching. If you're building document-generation pipelines, this shifts effort away from managing context windows and RAG plumbing toward validation and citation-checking of longer coherent outputs—but expect higher per-call token costs and latency for those larger contexts.

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