Claude Haiku 4.5 vs Gemini 2.5 Flash Lite for Persona Consistency

Persona Consistency demands: 1) resistance to prompt injection and malicious context (safety calibration), 2) stable character maintenance across long conversations (long_context), 3) faithfulness to the defined persona without drifting (faithfulness), and 4) correct behavior when calling tools or producing structured outputs that must preserve persona (tool_calling, structured_output). In our testing the primary task metric (persona_consistency) is identical: both models score 5/5 and are tied for 1st. To break ties, we look at supporting benchmarks from our 12-test suite: both models score 5 on faithfulness, long_context, and tool_calling — evidence they reliably maintain persona across lengthy, tool-enabled flows. Claude Haiku 4.5 shows higher safety_calibration (2 vs 1) and stronger agentic_planning (5 vs 4), which favors stricter refusal behavior and consistent persona enforcement when adversarial inputs appear. Gemini 2.5 Flash Lite offers broader modality support and a much larger context window in the payload (1,048,576 tokens) plus far lower token costs, which supports large-scale, multimodal persona deployments even though its safety_calibration score is lower in our tests.

Scenario A — Banking chatbot that must refuse credential-extraction attempts: Claude Haiku 4.5 (persona_consistency 5; safety_calibration 2; agentic_planning 5) — in our testing it better balances persona fidelity with stricter refusal behavior and recovery planning. Scenario B — Massive, multimodal roleplay platform that streams user audio/video and needs low cost: Gemini 2.5 Flash Lite (persona_consistency 5; context_window 1,048,576; input/output cost per mTok 0.1/0.4) — tied on persona in our tests but far cheaper to run at scale and supports audio/video inputs. Scenario C — Tool-driven assistant that must keep persona when calling external functions: both models scored 5 on tool_calling and 5 on faithfulness in our testing, so either is suitable; prefer Claude when the toolchain demands stricter refusal logic, prefer Gemini when you need to minimize costs or ingest large multimodal histories. Scenario D — Long-form serialized fiction that must preserve a character across 100k+ tokens: both scored 5 on long_context in our testing; pick Gemini if you need the absolute largest context window and multimodal context, pick Claude if you want the marginal safety/planning edge.

For Persona Consistency, choose Claude Haiku 4.5 if you need stricter safety/refusal behavior and stronger planning support while maintaining persona (safety_calibration 2 vs 1; agentic_planning 5 vs 4). Choose Gemini 2.5 Flash Lite if you need multimodal inputs, the largest context window, or far lower token costs (input/output cost per mTok: 0.1/0.4 vs Claude’s 1/5) while matching Claude on persona consistency in our testing (both 5/5).