Gemini 2.5 Pro vs GPT-5.4 for Persona Consistency

What Persona Consistency demands: maintaining a specified character or role across turns, resisting prompt injection, and preserving injected attributes (tone, persona facts, constraints) even during tool calls or long conversations. Capabilities that matter: - Safety calibration: correct refusals or safe handling when a persona conflicts with harmful or disallowed requests. - Long context: preserve persona over 30K+ tokens. - Faithfulness & structured_output: avoid inventing persona facts and keep format when persona requires structured replies. - Tool calling & agentic planning: maintain persona while invoking functions and passing arguments. - Multilingual consistency if persona spans languages. In our testing, both models score 5/5 on persona_consistency and tie for the top rank (tied for 1st out of 53). Supporting proxies: both models also score 5 on long_context, faithfulness, structured_output, and multilingual — all favorable for sustained persona adherence. Key divergence: safety_calibration is 5 for GPT-5.4 but 1 for Gemini in our tests, which directly affects how each model handles persona requests that border on policy or safety issues. Tool calling is 5 for Gemini vs 4 for GPT-5.4, which favors Gemini when persona-preserving tool sequences are required. All benchmark claims above are from our testing.

When Gemini 2.5 Pro shines (based on our scores): - Multi-step tooling persona: An assistant persona that must call external tools while staying in-character (e.g., a snarky devops persona that invokes monitoring APIs). Gemini’s tool_calling 5 vs GPT-5.4’s 4 in our tests suggests tighter function selection and argument handling while preserving persona. - Cost-sensitive deployments: Gemini input $1.25 / output $10 per mTok vs GPT‑5.4 input $2.50 / output $15 per mTok — same persona consistency at lower token cost. When GPT-5.4 shines (based on our scores): - Safety-sensitive persona enforcement: If the persona could be asked to produce content near policy boundaries, GPT-5.4’s safety_calibration 5 vs Gemini’s 1 in our tests means GPT-5.4 is much more likely to refuse or safely reframe harmful requests while maintaining persona constraints. - Risk-averse products: Services needing strong refusal behavior plus persona (e.g., medical or legal personas that must not give unsafe guidance) should favor GPT-5.4. Common strengths both share (from our testing): - Long multi-turn roleplay: both score 5 on long_context and persona_consistency, so either model maintains persona across very long conversations. - Structured persona outputs (JSON/slots): both scored 5 on structured_output, so both adhere to required persona-linked schemas.

For Persona Consistency, choose Gemini 2.5 Pro if you need lower token costs and stronger tool-calling while keeping an equally high persona score. Choose GPT-5.4 if safety-controlled persona behavior is critical (GPT-5.4 scores 5 vs Gemini's 1 on safety_calibration in our tests) and you prefer stricter refusal behavior even at higher per-token cost.