Claude Haiku 4.5 vs Codestral 2508 for Agentic Planning

Agentic Planning demands robust goal decomposition, reliable failure detection and recovery, accurate sequencing of actions, and safe decision constraints. On this task we rely on our agentic_planning benchmark first — Claude Haiku 4.5 scores 5/5 vs Codestral 2508's 4/5 in our testing. Supporting internal signals explain why: Haiku posts a 5 on strategic_analysis (nuanced tradeoff reasoning) while Codestral scores 2, indicating Haiku is better at multi-step tradeoff planning. Both models score 5 on tool_calling (function selection and sequencing), so either can orchestrate tools precisely. Codestral leads on structured_output (5 vs Haiku's 4), making it easier when strict JSON schema adherence is required. Long-context handling is equal (both 5), but Haiku also has higher safety_calibration (2 vs 1), reducing risky agentic behaviors in our tests. Note modality and cost differences: Claude Haiku 4.5 supports text+image->text and has a 200k token context window and explicit max output tokens of 64k; Codestral 2508 is text->text with a 256k context window. Input/output cost per mTok: Haiku 4.5 = 1 / 5, Codestral 2508 = 0.3 / 0.9. These are trade-offs to weigh alongside the raw agentic_planning score.

Where Claude Haiku 4.5 shines (based on scores):

• Complex multi-step agent: Decompose an ambiguous business goal into conditional subtasks with fallback plans — Haiku's 5 agentic_planning and 5 strategic_analysis help generate nuanced tradeoffs and recovery plans.
• Safety-sensitive agents: Automated escalation rules that must refuse or safely route risky requests — Haiku's safety_calibration 2 vs 1 for Codestral reduces unsafe recommendations in our tests.
• Multimodal planning: Agents that must interpret images plus instructions (e.g., floor plans) benefit from Haiku's text+image->text modality when visual context informs plan steps. Where Codestral 2508 shines (based on scores):
• Strict schema-driven orchestration: If your agent must output exact JSON with strict fields for downstream executors, Codestral's structured_output 5 vs Haiku's 4 reduces post-processing.
• Cost-sensitive high-frequency agents: Codestral's input/output cost per mTok (0.3 / 0.9) is materially lower than Haiku's (1 / 5), so for many small-plan calls Codestral can be far cheaper.
• Very large context sequences: Codestral's 256k context window slightly exceeds Haiku's 200k, useful when the agent must reference extremely long histories or large data blobs.

For Agentic Planning, choose Claude Haiku 4.5 if you need stronger goal decomposition, better strategic tradeoff reasoning, safer failure recovery, or multimodal (image+text) context — it scores 5 vs Codestral's 4 in our agentic_planning benchmark. Choose Codestral 2508 if you prioritize strict structured outputs, lower per-token costs (0.3/0.9 vs 1/5), or the largest context window for very long histories.