Claude Haiku 4.5 vs Devstral Medium for Agentic Planning

What Agentic Planning demands: goal decomposition, sequencing of sub-tasks, robust tool selection/argumenting, failure detection and recovery, and retaining plan state across long contexts. Because no externalBenchmark is provided for this task in the payload, our taskScore and ranks are the primary evidence: Claude Haiku 4.5 achieves taskScore 5 and taskRank 1 of 52, while Devstral Medium scores 4 and ranks 16 of 52. Supporting proxy metrics from our 12-test suite explain why: tool_calling (tool selection, argument accuracy, sequencing) is 5 for Claude Haiku 4.5 vs 3 for Devstral Medium; long_context (retrieval accuracy at 30K+ tokens) is 5 vs 4; structured_output is tied at 4; faithfulness (avoiding hallucinations) favors Haiku 5 vs 4. Strategic_analysis (nuanced tradeoff reasoning) is 5 for Haiku vs 2 for Devstral Medium — important when agents must weigh options or recover from partial failures. Safety_calibration is better for Haiku (2 vs 1), reducing unsafe agent actions in our tests. These internal scores are the basis for our verdict because no external benchmark overrides them.

1. Complex multi-API automation: Claude Haiku 4.5 (tool_calling 5 vs 3) — better at selecting the right functions, filling accurate arguments, and sequencing calls with recovery steps if an API fails. 2) Long-running research assistant: Haiku 4.5 (long_context 5 vs 4) — retains plan state across 100k+ token contexts and recomposes plans when new constraints appear. 3) Safety-sensitive orchestration: Haiku 4.5 (safety_calibration 2 vs 1; faithfulness 5 vs 4) — in our tests it more reliably refuses risky actions and sticks to source constraints during planning. 4) Cost-sensitive, simpler agents: Devstral Medium (agentic_planning 4) — at lower I/O cost (input 0.4 vs 1 per mTok; output 2 vs 5 per mTok) Devstral is a pragmatic choice for smaller end-to-end agents that need solid decomposition and structured outputs (structured_output tied 4) but don’t require the highest tool orchestration fidelity or largest context window. 5) Classification or routing subcomponents: both models tie on classification (4), so either can serve a routing step; Haiku still holds an edge when routed tasks require deep multi-step planning.

For Agentic Planning, choose Claude Haiku 4.5 if you need top-tier multi-step decomposition, robust tool calling, long-context plan state, and stronger failure recovery (scores: agentic_planning 5, tool_calling 5, long_context 5). Choose Devstral Medium if you need a lower-cost option that still performs well on plan decomposition and structured output (agentic_planning 4, structured_output 4) and can accept weaker tool orchestration and shorter context (tool_calling 3, long_context 4).