Claude Haiku 4.5 vs Devstral 2 2512 for Creative Problem Solving

What Creative Problem Solving demands: non-obvious, specific, feasible ideas plus a reliable way to turn ideas into actionable steps. Key capabilities: creative idea generation, strategic analysis, agentic planning (goal decomposition), faithfulness (sticking to constraints and facts), structured output (JSON/schemas), tool-calling when external actions are needed, and cost/throughput for large-scale ideation. There is no external benchmark supplied for this task in the payload, so the primary evidence is our internal task scores: both models score 4/5 on creative_problem_solving (tie). Supporting internal benchmarks explain the split: Claude Haiku 4.5 scores 5/5 on strategic_analysis, tool_calling, agentic_planning, and faithfulness, indicating it generates ideas with clearer tradeoffs and better sequencing for execution in our tests. Devstral 2 2512 scores 5/5 on structured_output and constrained_rewriting, showing it is more reliable when the solution must be delivered in exact formats or compressed constraints. Also consider system-level differences: Haiku has a 200k token context window and output cost_per_mtok = 5; Devstral has a 262144 token window and output_cost_per_mtok = 2, so Devstral is materially cheaper per output token in our pricing data.

Brainstorming a product pivot for executives — Haiku 4.5: in our testing its strategic_analysis = 5 and agentic_planning = 5, so it produces tradeoff-aware, prioritized ideas with concrete next steps and recovery plans. Creating a constrained deliverable (compact copy or exact JSON schema) — Devstral 2 2512: structured_output = 5 and constrained_rewriting = 5 in our tests, so it reliably meets strict format and length limits. Turning ideas into multi-step tool-driven workflows — Haiku 4.5 shines because tool_calling = 5 in our tests, improving function selection and argument sequencing. High-volume ideation where token cost matters — Devstral 2 2512 is cheaper: output_cost_per_mtok = 2 vs Claude Haiku 4.5 output_cost_per_mtok = 5 (a 2.5x output cost ratio in our data), so Devstral reduces per-token spend for bulk exploration. Long-context, research-heavy problems are comparable: both models score 5 on long_context in our tests, so either can handle extended inputs, but choose based on downstream needs (Haiku for strategy fidelity, Devstral for formatted outputs).

For Creative Problem Solving, choose Claude Haiku 4.5 if you need higher-fidelity strategy, better tool-calling, stronger faithfulness, and clearer stepwise plans (Haiku scores 5 on strategic_analysis, tool_calling, faithfulness in our tests). Choose Devstral 2 2512 if you care most about lower token cost (output_cost_per_mtok = 2 vs 5) and flawless structured outputs or tight-length rewrites (Devstral scores 5 on structured_output and constrained_rewriting in our tests).