Claude Opus 4.7 Context Window Long Document Analysis

Claude Opus 4.7's context window carries forward from 4.6 — Anthropic's model documentation confirms the per-request input limits weren't the headline change in the April 16, 2026 release. What did change for long-document work: multi-session memory persistence, task budgets, and the new "xhigh" effort level. Together, those three features change how legal teams should think about long-document analysis — not by giving you a bigger window per call, but by letting work span sessions and matters without losing context. Here's how to actually use Opus 4.7 on long documents in 2026, where its context advantages over GPT-5.5's 1M-token window do and don't matter, and how the procurement math lands.

Context window vs effective context for legal work

There are two different numbers that matter in long-document analysis:

Per-request context window. The maximum input tokens Claude can process in a single API call. Per Anthropic's documentation, Opus 4.7 supports a substantial context window suitable for full-document analysis. GPT-5.5 ships with a 1M-token context window per the OpenAI release notes, which is the larger number on paper.

Effective context across a matter. The total context the model can leverage across multiple sessions on the same matter. Pre-4.7 Claude effectively had no cross-session memory — every session started cold. With 4.7's multi-session memory persistence (scratchpad/notes file), Claude now maintains analytical context across days, weeks, and months on the same matter.

For legal work, the second number often matters more than the first. A 90-day M&A diligence isn't a single 1M-token request; it's hundreds of sessions, each leveraging accumulated context. The model with 200K per request and persistent memory can outperform the model with 1M per request and no memory across the matter lifetime. The Opus 4.7 anchor covers the broader change set; the multi-session memory M&A diligence guide covers the persistence side.

When per-request context size is the binding constraint

Three legal workflows where the per-request context window is the dominant limit:

Single-document analysis on very long documents. A 600-page bond indenture, a multi-thousand-page regulatory filing, a complete deposition transcript. The model needs to reason across the whole document in a single call to catch cross-section interactions. For these, GPT-5.5's 1M-token context can handle longer single documents in a single shot than Opus 4.7.

Document-set comparison. Comparing two versions of a 200-page contract to identify all material differences. Both versions plus the comparison framework need to fit in the context window simultaneously. GPT-5.5's larger window helps; Opus 4.7 may need chunking.

Bulk extraction across long documents. Pulling all defined terms and their cross-references from a 400-page agreement. The whole document needs to be in context simultaneously to map term usage.

For these workflows specifically, GPT-5.5's 1M-token window is a real advantage on Opus 4.7. The GPT-5.5 calibration and disclosure analysis covers the broader GPT-5.5 picture for legal work.

When persistent memory beats per-request context

Three workflows where Opus 4.7's multi-session memory wins despite GPT-5.5's larger per-request window:

M&A diligence over 5-15 days. The work isn't one massive analysis; it's hundreds of focused sessions over weeks. The scratchpad accumulates context faster than re-loading documents into a 1M-token window every session. Opus 4.7 wins on cumulative analyst time and consumption cost.

Multi-day deposition prep. Witness preparation, exam outlines, document references, prior testimony. The corpus is large but no single session needs all of it in one window. Persistent memory carries the relevant context forward as the prep evolves.

Long-running matter research. White-collar matters that hold context for months. Regulatory investigations with rolling productions. The work compounds across time; the model that remembers compounds with it.

For these, Opus 4.7's multi-session memory + reasonable per-request context outperforms GPT-5.5's 1M-token window with cold starts. The task budgets discovery spoke covers the cost-side feature that complements memory in these workflows.

Practical strategies for long-document work in Opus 4.7

Five working approaches for legal teams running long-document analysis on Opus 4.7:

1. Hierarchical summarization. For documents that exceed the comfortable single-call context, structure the analysis in a hierarchy: summarize sections, then summarize the summaries, then reason against the top-level summary with drill-down to specific sections as needed. The scratchpad holds the hierarchy across sessions.

2. Targeted retrieval over full-document loading. Instead of loading the whole 400-page agreement into one prompt, load the table of contents and let Claude request specific sections. This pattern uses fewer input tokens per request and keeps reasoning focused.

3. Cross-session continuation for matter-level work. Use multi-session memory deliberately. Each session writes a summary of what it covered to the scratchpad. The next session reads the scratchpad first and continues. Periodic compaction keeps the scratchpad lean.

4. xhigh on the analysis, high on the extraction. When working with a long document, use high or medium effort for bulk extraction (pulling defined terms, dates, dollar amounts) and xhigh for the actual analysis (interaction effects, cross-section conflicts, strategic implications). The effort levels xhigh when-to-use spoke covers the per-task math.

5. Task budgets for predictable long-doc spend. Set a token cap on the full agentic loop covering a long-document analysis. Claude will prioritize the highest-signal sections within the budget. Predictable matter-level spend instead of unbounded consumption.

Where Opus 4.7 vs GPT-5.5 actually lands for legal long-document work

The honest comparison by use case:

Single 600-page document analysis (one session): GPT-5.5 wins on context size. If the work is genuinely one-shot; read this whole doc and tell me X; GPT-5.5's 1M-token window is the better fit.

Multi-day matter work: Opus 4.7 wins on persistence. The context-loss tax of returning to GPT-5.5 every session re-loaded outweighs the per-call window advantage on long-running work.

Cost predictability: Opus 4.7 wins on task budgets. GPT-5.5's per-call pricing per the OpenAI pricing page is $5/M input and $30/M output (with batching and cached input discounts), comparable to Claude on input but higher on output. For agentic loops, Claude's task budgets give cleaner per-matter spend.

Calibration and writing quality: Opus 4.7 wins on calibration on subjective measures, especially for legal prose. Both models improved calibration in the April 2026 releases; Anthropic's documentation specifically calls out reduced overconfidence on uncertain plans.

For most legal teams, the right answer is a hybrid: Opus 4.7 as the default for matter-level work, GPT-5.5 reserved for the specific single-shot long-document analyses where its window pays off. The API pricing vs 4.6 spoke covers the consumption-cost math.

Cost implications of long-document work on Opus 4.7

Long documents consume input tokens proportional to length. At Opus 4.7's $5 per million input token rate, a 600-page document at typical density runs roughly 200K input tokens; about $1 in input cost per full read. For repeated analysis across the same document, the cost adds up.

Two cost-control patterns that work in practice:

Cache the document load. Anthropic's prompt caching (where supported) lets you load the document once and run multiple queries against the cached context at materially reduced input cost. For workflows that hit the same long document 10+ times, caching pays back quickly.

Use the scratchpad to avoid re-loading. When Claude has analyzed sections of a long document and written summaries to the scratchpad, future sessions can reference the summaries instead of re-loading the source. Input-token spend drops without sacrificing analytical continuity.

For consumption-conscious firms, the workflow is: load the long document once at the start of the matter, build a structured scratchpad with section summaries and cross-references, then operate against the scratchpad for most of the matter, re-loading specific sections only when the analysis needs source detail. The tokenizer cost calculator covers the math.

The Bottom Line: The verdict: Opus 4.7's context story isn't "bigger window"; it's "persistent context across sessions plus task-budgeted predictable spend." For matter-level legal work running over days or weeks, that's the right architectural choice. For genuine one-shot long-document analysis, GPT-5.5's 1M-token window is the better fit. Most legal teams should default to Opus 4.7 and reserve GPT-5.5 for the specific single-shot use cases where window size dominates.