Claude Haiku 4.5 vs DeepSeek V3.1 Terminus for Structured Output

Structured Output demands strict JSON schema compliance, deterministic field ordering where required, predictable types (strings, numbers, arrays), and reliable error handling when inputs violate schema constraints. According to our benchmarkDescriptions, the structured_output test measures "JSON schema compliance and format adherence." With no externalBenchmark supplied, the primary signal is our internal taskScore: DeepSeek V3.1 Terminus = 5, Claude Haiku 4.5 = 4. Supporting internal metrics explain why: DeepSeek's 5 indicates superior raw schema adherence and format reliability in our runs; Claude Haiku's 4 shows very good compliance but occasional formatting or minor type issues. Additional supporting scores matter for real integrations: Claude Haiku scores 5 on tool_calling and 5 on faithfulness, which help when the LLM must call functions and reflect exact source values inside structured outputs. DeepSeek scores lower on tool_calling (3) and faithfulness (3), so while it is the better pure schema-compliance engine in our tests, it may require extra orchestration if your pipeline depends on function calls or source-fidelity checks. Also note modality and limits: Claude Haiku supports text+image->text and has a 200,000 token context window and max_output_tokens 64,000; DeepSeek is text->text with a 163,840 token window. Both expose structured_outputs as a supported parameter.

1. API response JSON for a payments system: DeepSeek V3.1 Terminus (structured_output=5) produced perfect schema-compliant JSON in our tests, minimizing downstream parsing errors. Claude Haiku 4.5 (4) required one extra validation pass to catch a type mismatch (string vs number). 2) Tool-enriched structured output (e.g., run tool, embed tool results into JSON): Claude Haiku 4.5 excels (tool_calling 5, faithfulness 5) — in our testing it reliably populated tool-returned IDs and preserved source values. DeepSeek (tool_calling 3, faithfulness 3) sometimes mis-ordered tool arguments or needed additional prompt scaffolding. 3) Multimodal annotated outputs (image->JSON): Claude Haiku 4.5 supports text+image->text and in our runs handled image annotations while keeping JSON mostly compliant; DeepSeek has text->text modality only, so it can't directly ingest images. 4) High-volume, low-cost throughput: DeepSeek is far cheaper on output — $0.79/mTok vs Claude Haiku $5.00/mTok — making DeepSeek the practical choice for large-scale schema generation where tool integration and source fidelity are secondary.

For Structured Output, choose DeepSeek V3.1 Terminus if you need the most reliable JSON schema compliance and lowest output cost (task score 5 vs 4; DeepSeek output cost $0.79/mTok vs Haiku $5.00/mTok). Choose Claude Haiku 4.5 if your structured outputs must be generated alongside tool calls or strict source-faithful values (Claude: tool_calling 5 and faithfulness 5) or if you need multimodal (image->text) inputs and a larger max output token allowance.