Claude Haiku 4.5 vs R1 0528 for Persona Consistency

What Persona Consistency demands: maintaining a character across turns, obeying role constraints, and resisting prompt injections that attempt to change role or leak system instructions. Capabilities that matter: safety_calibration (ability to refuse or correctly handle malicious or out-of-character prompts), long_context (tracking persona state across many tokens), faithfulness (sticking to the persona's facts and constraints), and structured_output/tool_calling when system roles require strict formats. In our testing the primary signal for this task is the persona_consistency score (both models: 5/5, tied for 1st). Supporting evidence: R1 0528 shows higher safety_calibration (4) versus Claude Haiku 4.5 (2) in our tests — a meaningful proxy for resisting injection. Both models scored 5 on long_context and faithfulness and 5 on tool_calling, indicating equivalent ability to hold state and follow function-like roles. Additional differences relevant to implementation: Claude Haiku 4.5 supports text+image->text and offers a larger context window (200,000 tokens) versus R1 0528's 163,840, which favors multimodal and extremely long-session use cases. R1 0528 includes a documented quirk: empty responses on some structured_output tasks unless high max completion tokens are provided; plan around that if you require strict JSON outputs.

1. Roleplay chatbot that must refuse malicious pivoting: R1 0528 shines — both models scored 5/5 on persona, but R1’s safety_calibration is 4 versus Claude Haiku 4.5’s 2, so R1 is more likely to resist injection in our tests. 2) Multimodal mascot assistant using images to establish character: Claude Haiku 4.5 is the practical choice because it supports text+image->text and has a larger 200k token window; both models tie on persona_consistency but Haiku’s modality advantage matters here. 3) Long-running story with 100k+ tokens of context: both scored 5 on long_context, but Claude Haiku 4.5’s 200,000-token window offers more headroom versus R1’s 163,840. 4) Cost-sensitive conversational API with strict persona rules: R1 0528 is preferable — lower output cost ($2.15 per mTok vs $5) and stronger safety_calibration in our testing. 5) Strict structured-output persona (JSON schemas): both models scored 4 on structured_output, but R1’s quirk of returning empty responses on structured_output may force you to increase max completion tokens or choose Claude Haiku 4.5 to avoid that integration risk.

For Persona Consistency, choose Claude Haiku 4.5 if you need multimodal persona (image inputs), the largest context window (200,000 tokens), or fewer integration quirks. Choose R1 0528 if you prioritize injection resistance and safety (safety_calibration 4 vs 2 in our testing) and lower output cost ($2.15 vs $5) while keeping top-tier persona behavior (both score 5/5).