Claude Haiku 4.5 vs DeepSeek V3.2 for Persona Consistency

What Persona Consistency demands: The task ("Maintains character and resists injection") requires an LLM to (1) preserve a specified character or role across turns, (2) resist prompt injection or context shifts, (3) keep output format when required, and (4) behave predictably when integrated with tools or multimodal inputs. Capabilities that matter most: long_context (to keep persona state across lengthy histories), tool_calling (to avoid persona-breaking tool responses and to supply persona-aware tool arguments), structured_output (when persona must adhere to JSON or schema), faithfulness (stick to the persona brief without inventing contradictory facts), and safety_calibration (refuse harmful persona requests). In our data there is no external benchmark for this task, so the verdict is based on our internal scores. Both Claude Haiku 4.5 and DeepSeek V3.2 score 5/5 on persona_consistency in our testing and are tied for rank 1 of 52. We use supporting internal metrics to break the tie for practical recommendations: Haiku 4.5 leads on tool_calling (5 vs 3) and classification (4 vs 3) which help maintain persona in agentic, multi-tool flows; DeepSeek leads on structured_output (5 vs 4) and constrained_rewriting (4 vs 3), which help when persona must be precise in schema or strict-length UIs. Both models tie on long_context (5) and faithfulness (5), so both can retain persona across long conversations.

Scenario 1 — Multimodal customer support agent with tools: Claude Haiku 4.5 is stronger. Both models are 5/5 on persona_consistency, but Haiku’s tool_calling 5 vs DeepSeek’s 3 reduces risk of tool-invoked persona breaks and misrouted calls. Haiku also supports text+image->text and a 200k context window, useful when persona must reference past images or long histories. Cost: Haiku output $5/mTok vs DeepSeek $0.38/mTok. Scenario 2 — Strict chatbot that must return a compact JSON persona profile for many users: DeepSeek V3.2 shines because structured_output is 5 vs Haiku’s 4 and constrained_rewriting is 4 vs 3, so DeepSeek more reliably fits tight schema and character-limited channels at far lower cost. Scenario 3 — Long-form narrative roleplay across dozens of turns: Both models score 5 on long_context and persona_consistency, so pick Haiku for multimodal prompts or when tool integration is required; pick DeepSeek to minimize cost while getting comparable persona fidelity in text-only contexts. Scenario 4 — Security-sensitive deployments resisting injection: Both models scored 5/5 for persona_consistency and tie on safety_calibration (both 2), so neither is exempt from additional guardrails; prefer the model that fits your integration needs (Haiku for tool-heavy pipelines, DeepSeek for schema-heavy, low-cost pipelines).

For Persona Consistency, choose Claude Haiku 4.5 if you need persona fidelity across multimodal inputs, tool-driven flows, or long-context sessions and you accept a much higher price (Haiku input $1/mTok, output $5/mTok; 200,000 token context). Choose DeepSeek V3.2 if you need equivalent persona consistency at far lower cost (input $0.26/mTok, output $0.38/mTok), or if strict structured outputs and constrained-length persona formats matter more than tool-call robustness.