Claude Haiku 4.5 vs Devstral 2 2512 for Agentic Planning

Agentic Planning demands clear goal decomposition, robust tool selection and sequencing, structured outputs for orchestration, and failure-detection + recovery strategies. In our data (no external benchmark provided), the primary signal is the internal agentic_planning score: Claude Haiku 4.5 = 5/5, Devstral 2 2512 = 4/5. Supporting benchmarks: tool_calling (how well the model picks and sequences functions) is 5/5 for Claude Haiku 4.5 vs 4/5 for Devstral 2 2512; structured_output (JSON/schema reliability) is 4/5 for Haiku vs 5/5 for Devstral. Faithfulness (sticking to source constraints) is 5/5 for Haiku vs 4/5 for Devstral, and safety_calibration is 2/5 vs 1/5 respectively. In short: Haiku's higher agentic_planning, tool_calling, and faithfulness scores are the primary reason it outperforms Devstral for multi-step, tool-driven agents; Devstral's strengths in structured_output and constrained_rewriting make it a better fit when strict schema adherence and compression are the priority.

Scenario A — Autonomous ticket triage and remediation: Claude Haiku 4.5 (agentic_planning 5/5, tool_calling 5/5, faithfulness 5/5) will better decompose a support goal into monitoring, reproduce steps, tool calls, and rollback strategies. Expect clearer tool sequencing and safer refusal behavior than Devstral. Scenario B — Strict orchestration with compact payloads: Devstral 2 2512 (agentic_planning 4/5, structured_output 5/5, constrained_rewriting 5/5) shines when you need exact JSON outputs and aggressive compression into fixed-size messages for downstream agents. Scenario C — Cost-sensitive CI/CD automation: Devstral 2 2512 is cheaper per mtoken (input_cost_per_mtok 0.4, output_cost_per_mtok 2) versus Claude Haiku 4.5 (input_cost_per_mtok 1, output_cost_per_mtok 5), so for high-volume, schema-driven agent loops you may prefer Devstral despite the 1-point lower agentic_planning score. Scenario D — Safety-critical recovery flows: Claude Haiku 4.5's higher faithfulness (5/5) and tool_calling (5/5) reduce risky hallucinations in recovery steps compared with Devstral's faithfulness 4/5 and safety_calibration 1/5.

For Agentic Planning, choose Claude Haiku 4.5 if you need best-in-class goal decomposition, reliable tool selection/sequencing, and higher faithfulness (Claude Haiku 4.5: agentic_planning 5/5, tool_calling 5/5, faithfulness 5/5). Choose Devstral 2 2512 if you prioritize strict structured outputs and constrained rewriting (Devstral 2 2512: structured_output 5/5, constrained_rewriting 5/5) or you need lower per-mtok costs (input 0.4 vs 1, output 2 vs 5).