DeepSeek V4's 1M context window breaks at 300k tokens, optimal range revealed

A developer (u/TangeloOk9486) rigorously tested DeepSeek V4's claimed 1M token context window across three production codebases: a 45k-token microservice, a 180k-token monorepo backend, and a 520k-token full-stack application. Tasks included dependency tracing, cross-file refactoring, and bug isolation to measure recall precision. At 45k tokens, the model accurately traced function calls across 8 files with perfect path reconstruction. At 180k tokens, multi-file refactors spanning 14 files showed consistent architectural understanding without contradictions or context loss.

However, past 300k tokens, precision quality degraded significantly. When asked for exact line numbers of functions defined 400k tokens earlier, the model responded with approximate answers (e.g., “around line 230” instead of the actual 247). At the full 520k context, outputs shifted to architectural summaries that skipped implementation details—a critical problem for edge-case debugging. The time-to-first-token was ~1.19s on DeepInfra's FP4 endpoint, but in max reasoning mode, the first answer took ~120 seconds as the model completed internal chain-of-thought before producing visible output.

Benchmarks also revealed a 94% hallucination rate on unknown-answer tasks (AA-Omniscience), where V4 confidently generated references to nonexistent utility functions or phantom dependencies. The practical optimal range for coding work is 150–250k tokens, where full context retention, sub-2s response latency, and minimal precision loss are achievable. Beyond 300k, defensive prompting and source verification become necessary. The 1M window functions technically but requires careful handling—context size shifts which prompt engineering techniques matter rather than eliminating the need for them.