Claude Haiku 4.5 vs Devstral Small 1.1 for Structured Output

Structured Output in our suite means JSON schema compliance and format adherence. Key capabilities: exact response_format/structured_outputs support, adherence to field types and required keys, deterministic formatting, long-context handling when schemas span many tokens, and reliable tool calling when outputs depend on external data. Both models explicitly support structured_outputs and scored 4/5 on our structured_output test, placing them tied at rank 26 of 52. Supporting proxy metrics: Claude Haiku 4.5 surpasses Devstral Small 1.1 on tool_calling (5 vs 4), long_context (5 vs 4), and faithfulness (5 vs 4) — attributes that reduce schema drift for large, multi-step generation. Devstral Small 1.1 matches Haiku on the core structured_output score (4/5) while being text-only and much cheaper (input_cost_per_mtok 0.1 vs 1; output_cost_per_mtok 0.3 vs 5). With no third-party external benchmark present in the payload, our internal structured_output score is the primary signal for this task.

1. Large multimodal invoice extraction and JSON generation: Claude Haiku 4.5 (context_window 200000, long_context 5/5, modality text+image->text) is better when the schema must integrate OCR results and keep 50k+ token context while preserving exact keys and types. 2) High-throughput API that returns compact JSON responses for millions of requests: Devstral Small 1.1 is preferable because it delivers the same structured_output score (4/5) with far lower cost (output_cost_per_mtok 0.3 vs 5), reducing per-response spend by ~16.7×. 3) Tool-driven pipelines where the model selects functions and formats their outputs into a strict schema: Claude Haiku 4.5 (tool_calling 5/5) is likelier to choose correct functions and format arguments reliably. 4) Simple text-only schema generation (forms, webhooks, short responses): Devstral Small 1.1 matches structured compliance while saving compute and cost.

For Structured Output, choose Claude Haiku 4.5 if you need multimodal input, very large context (200k tokens), stronger tool-calling (5 vs 4) or higher faithfulness for complex schemas and you can accept higher cost. Choose Devstral Small 1.1 if you need the same core structured-output quality (both score 4/5 in our tests) for text-only workloads and want far lower output cost (0.3 vs 5 per mTok).