R1 0528 vs GPT-5.4 for Persona Consistency

Persona Consistency requires maintaining a stable character across turns and resisting injection or prompt manipulation. Key capabilities: long_context (to retain persona history), faithfulness (to stick to the defined character), safety_calibration (to refuse malicious persona changes), structured_output (when personas are stored or serialized), and robustness to tool- or prompt-based attacks. On these dimensions both models score 5/5 for persona_consistency in our 12-test suite and tie in long_context (5/5) and faithfulness (5/5). Differences that determine a winner are operational: GPT-5.4 scores 5/5 on safety_calibration versus R1 0528's 4/5, and GPT-5.4 scores 5/5 on structured_output while R1 0528 scores 4/5 and is documented to return empty responses on structured_output. R1 0528 scores higher on tool_calling (5/5 vs GPT-5.4's 4/5), which helps in workflows that rely on external function calls for persona state, but its quirks (reasoning tokens consuming output budget; empty structured_output responses) can harm short or schema-driven persona tasks. Use these internal scores as the basis for the verdict since no external benchmark is provided for this task.

1. Long-running roleplay with adversarial user attempts: GPT-5.4 is preferable — both models have persona_consistency 5/5, but GPT-5.4's safety_calibration 5/5 (vs R1 0528's 4/5) and 1,050,000-token context window (vs 163,840) reduce injection risk and retain lengthy persona state. 2) Structured persona storage and JSON schema enforcement: GPT-5.4 wins (structured_output 5/5 vs R1 0528 4/5); R1 0528 can return empty responses on structured_output, breaking schema-driven pipelines. 3) Cost-sensitive multi-agent chatbot that uses tool calls to fetch persona facts: R1 0528 is attractive — tool_calling 5/5 (vs GPT-5.4 4/5) and much lower costs (input cost 0.5 vs 2.5 per mTok; output cost 2.15 vs 15 per mTok). 4) Moderation-sensitive deployments where refusing harmful persona switches is critical: GPT-5.4's safety_calibration 5/5 gives a measurable operational advantage over R1 0528's 4/5.

For Persona Consistency, choose R1 0528 if you need a lower-cost model with excellent tool calling and can accommodate its quirks (input cost 0.5 vs 2.5 per mTok; output cost 2.15 vs 15 per mTok; tool_calling 5/5). Choose GPT-5.4 if you need the strongest operational resistance to persona injection, reliable structured-output handling, and the largest context window (safety_calibration 5 vs 4; structured_output 5 vs 4; context 1,050,000 vs 163,840), and you can accept higher cost.