GPT-5.1-Codex-Max vs Grok 4 Fast

The 14-point score gap (75 vs 61) likely reflects real-world performance differences in code completion accuracy and debugging speed, not feature availability. Grok 4 Fast's 5x larger context window (2M vs 400K tokens) enables handling entire codebases in memory, which is critical for understanding complex dependencies in large projects.

For most coding workflows, 30K output tokens covers even extensive code generation sessions, making the $10/M output pricing hard to justify. The sweet spot where GPT-5.1-Codex-Max shines is generating complete documentation sites or multi-file boilerplate projects in a single request, where that 128K ceiling becomes essential and the 20x cost multiplier is offset by eliminating multiple API calls.

OpenAI's ecosystem lock-in through existing toolchains, fine-tuned prompts, and API integrations creates significant migration friction despite the 6.25x input pricing disadvantage. Additionally, teams generating massive outputs (documentation, test suites) may find GPT-5.1-Codex-Max's 128K output limit prevents costly request splitting that would occur with Grok's 30K ceiling.

With 2M tokens, Grok 4 Fast can ingest an entire medium-sized codebase (roughly 50K lines) in one context, while GPT-5.1-Codex-Max's 400K limit forces chunking and loses cross-file understanding. This 5x difference translates directly to accuracy: Grok maintains full dependency graphs and type information across files, explaining part of its 14-point scoring advantage.