Claude Sonnet 4.6 vs GPT-5.4 for Long Context

Long Context (retrieval accuracy at 30K+ tokens) requires: stable, very large context windows; consistent token handling across extremely long prompts; strong faithfulness so extracted facts match source material; high structured_output compliance when results must fit schemas; and reliable tool calling when retrieval is combined with external functions. In our testing both models score 5/5 on long_context and 5/5 on faithfulness, indicating comparable core retrieval accuracy. Secondary signals explain practical differences: GPT-5.4 lists a 1,050,000-token window vs Claude Sonnet 4.6's 1,000,000 (favoring GPT-5.4 for absolute buffer), GPT-5.4 has structured_output 5 (better for strict schema extraction) while Claude Sonnet 4.6 has tool_calling 5 (better when retrieval is tightly coupled to function/agent flows). The payload also shows higher SWE-bench and AIME numbers for GPT-5.4 (76.9% and 95.3%), which, while not our primary Long Context metric, supplement the case that GPT-5.4 handles complex, large-input tasks with slightly better external benchmark performance.

1. Large-document extraction for compliance reports (500K+ tokens): GPT-5.4 is the better pick — 1,050,000-token window, structured_output 5, and lower input cost (2.5 vs 3 per mTok) reduce cost and improve schema fidelity. 2) Multi-file R&D synthesis where you must ingest varied files: GPT-5.4 supports text+image+file->text modality in the payload, which helps unified file ingestion across long contexts. 3) Long-running agentic codebase navigation (chained tool calls, iterative edits across huge repo state): Claude Sonnet 4.6 shines because it has tool_calling 5 and broader supported parameters (temperature, top_k, top_p, verbosity, tool_choice), making complex agent workflows inside long contexts easier to orchestrate. 4) High-assurance extraction where faithfulness matters: both score faithfulness 5, so either model will match source content, but choose GPT-5.4 when strict JSON/schema output is required (structured_output 5 vs 4). Concrete numbers from the payload: long_context 5/5 each; tool_calling A=5 vs B=4; structured_output A=4 vs B=5; window sizes 1,000,000 vs 1,050,000; input cost 3 vs 2.5 per mTok; SWE-bench 75.2% vs 76.9%; AIME 85.8% vs 95.3%.

For Long Context, choose GPT-5.4 if you need the largest possible single-context buffer, strict schema/JSON extraction, lower input cost (2.5 vs 3 per mTok), or the slightly higher SWE-bench/AIME figures in the payload. Choose Claude Sonnet 4.6 if your long-context workload relies on heavy agentic tool calling, nuanced function orchestration inside a session, or the additional tuning parameters Sonnet exposes.