GPT-5.4 vs Grok 4 for Long Context

What Long Context demands: accurate retrieval and grounding across 30K+ tokens requires (1) large addressable context, (2) reliable chunking/indexing and cross-referencing, (3) faithful extraction and structured outputs, (4) support for long-form outputs, and (5) predictable safety calibration when dealing with sensitive content. In our testing both models hit the top Long Context score (5/5) and tie for task rank, showing they are competent at retrieval at scale. Key differentiators in the data: GPT-5.4 exposes a ~1,050,000-token window and explicit max output of 128,000 tokens, enabling single-shot access to far larger inputs; Grok 4 provides a 256,000-token window and explicitly notes parallel tool calling in its description and a uses_reasoning_tokens quirk. Support scores matter as supporting evidence: GPT-5.4 scores higher on structured output (5 vs 4) and safety calibration (5 vs 2) in our tests — both important for reliable extraction and for permitting/refusing content correctly across long documents. Tool-calling and faithfulness are tied (tool calling 4, faithfulness 5), so multi-step tool workflows are feasible on both, but GPT-5.4’s capacity and auxiliary strengths make it the safer, higher-capacity choice for the largest retrieval tasks.

Where GPT-5.4 shines (based on data):

• Ingesting and querying an entire enterprise codebase or legal corpus in one session: 1,050,000-token window plus 128,000 max output avoids chunking; structured output 5 supports precise schema extraction.
• High-stakes regulatory or safety-sensitive summarization: safety calibration 5 in our testing reduces risky allowances compared with Grok 4’s 2.
• Large-model single-pass question answering over multi-book corpora, or producing very long, cohesive reports (128k outputs). Where Grok 4 shines (based on data):
• Developer workflows that need 256k context with parallel tool calling and structured outputs: Grok 4 supports parallel tool workflows and has structured output 4.
• Long-context classification and routing inside long documents: Grok 4 scored classification 4 vs GPT-5.4’s 3, so tasks that emphasize accurate labeling across long inputs may favor Grok 4.
• Simpler cost-sensitive long-context jobs where 256k is sufficient: Grok 4’s feature set (reasoning-token quirk, parallel tools) can make it efficient for multi-tool pipelines. Concrete score-and-cost anchors from our data: both models are 5/5 on long context in our tests; GPT-5.4 input_cost_per_mtok is 2.5 vs Grok 4’s 3, output_cost_per_mtok is 15 for both; GPT-5.4 records 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (Epoch AI), while Grok 4 has no SWE-bench/AIME scores in the payload.

For Long Context, choose GPT-5.4 if you need the largest single-session capacity (1,050,000 tokens), long single-pass outputs (up to 128k), stronger structured-output and safety calibration, or the SWE-bench/AIME external results. Choose Grok 4 if your tasks fit inside a 256k window, require parallel tool-calling workflows, or prioritize classification across long documents.