Claude Haiku 4.5 vs Devstral Small 1.1 for Agentic Planning
Claude Haiku 4.5 is the clear winner for Agentic Planning in our testing. It scores 5/5 on our agentic_planning test versus Devstral Small 1.1's 2/5 (a 3-point gap). Haiku leads on strategic_analysis (5 vs 2), tool_calling (5 vs 4), long_context (5 vs 4) and faithfulness (5 vs 4), and is ranked 1st for this task (taskRankA: 1 of 52) while Devstral ranks 51st of 52. The tradeoff is cost: Haiku's input/output costs are 1/mtok and 5/mtok versus Devstral's 0.1/mtok and 0.3/mtok, so Devstral is substantially cheaper (output cost ratio ~16.67x). Choose Haiku for robust, resilient planning; choose Devstral only when strict budget or very simple scripted planning is the priority.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
mistral
Devstral Small 1.1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.300/MTok
modelpicker.net
Task Analysis
What Agentic Planning demands: goal decomposition, sequencing of subgoals, failure recovery and fallback strategies, accurate tool selection and argument sequencing, consistent structured outputs for orchestration, and persistence across long contexts. In our testing the primary signal is the agentic_planning score (5 vs 2). Claude Haiku 4.5 earned a 5/5 on agentic_planning, supported by top-tier strategic_analysis (5), tool_calling (5), long_context (5) and faithfulness (5) — all capabilities that enable reliable decomposition and recovery. Devstral Small 1.1 scored 2/5 on agentic_planning and shows lower strategic_analysis (2) and creative_problem_solving (2), though it matches Haiku on structured_output (4) and has acceptable tool_calling (4). Safety calibration is identical in our tests (both 2/5), so neither model strongly outperforms the other on refusal quality. Because no external benchmark is provided for this task in the payload, our internal taskScore and component scores are the basis for the verdict.
Practical Examples
Where Claude Haiku 4.5 shines (use Haiku when): - Orchestrating a complex software release: Haiku's 5/5 agentic_planning, 5/5 tool_calling and 5/5 long_context let it decompose milestones, sequence API calls, and recover from failed build steps. - Multi-step research assistants that must reason about tradeoffs and fallbacks: strategic_analysis 5/5 ensures sensible contingency planning. - Agents that must stick to source constraints and avoid hallucination: faithfulness 5/5 supports accurate recovery plans. Where Devstral Small 1.1 is appropriate (use Devstral when): - Cost-sensitive, high-volume automated agents with simple workflows: Devstral's input/output costs (0.1/mtok and 0.3/mtok) make it ~16.7x cheaper on output tokens than Haiku. - Rigid, schema-driven pipelines where structured_output is the key requirement: Devstral ties Haiku on structured_output (4/5) and matches classification (4/5), so it can reliably emit JSON or arguments at much lower cost. Caveats tied to scores: Devstral's agentic_planning 2/5 and strategic_analysis 2/5 mean it will struggle with non-trivial decomposition, failure recovery, and creative fallback strategies compared with Haiku.
Bottom Line
For Agentic Planning, choose Claude Haiku 4.5 if you need robust goal decomposition, reliable failure recovery, strong tool-calling, and long-context reasoning — it scores 5 vs 2 in our tests. Choose Devstral Small 1.1 if unit-cost and throughput matter more than planning robustness (Devstral is far cheaper: input 0.1 vs 1 and output 0.3 vs 5 per mTok), and your agents run simple, schema-driven workflows.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.