Claude Haiku 4.5 vs Claude Sonnet 4.6 for Agentic Planning

What Agentic Planning demands: clear goal decomposition, robust failure recovery, correct tool selection and sequencing, structured outputs for orchestration, long-context state tracking, and safety calibration to avoid harmful actions. In our testing both models earn 5/5 on agentic_planning and tie for rank 1, so the raw planning capability is equivalent on the task itself. Use secondary metrics to break the tie: Sonnet 4.6 has safety_calibration 5 vs Claude Haiku 4.5's 2 (important for agents that may need to refuse or escalate risky steps), and Sonnet scores 5 on creative_problem_solving vs Haiku's 4 (helps with non-obvious decomposition and fallback strategies). Tool calling is 5/5 for both; structured_output is 4/5 for both. Operational trade-offs: Haiku is positioned as lower-cost and lower-latency, while Sonnet provides a larger context window (1,000,000 vs 200,000) and higher max output tokens (128,000 vs 64,000), which matter for multi-step agents that persist long task histories. Sonnet also has external benchmark evidence on SWE-bench Verified (75.2%) and AIME 2025 (85.8%) according to Epoch AI, which supports its strength on code- and reasoning-adjacent agentic workflows.

When to pick Claude Sonnet 4.6 (practical cases):

• Long-running autonomous project manager that must keep a months-long conversation, state, and plan across many files: Sonnet's 1,000,000-token context and 128k max output reduce truncation risk.
• High-risk action planning (safety-sensitive tooling, escalation rules): Sonnet's safety_calibration 5 vs Haiku's 2 reduces harmful or unsafe action suggestions.
• Agents that must improvise non-obvious fallbacks and debug complex codebases: Sonnet's creative_problem_solving 5 and SWE-bench 75.2% (Epoch AI) provide supporting evidence. When to pick Claude Haiku 4.5 (practical cases):
• Cost-sensitive, call-heavy agentic deployments where latency and price matter: Haiku input/output costs are lower (input 1 vs 3 per m-tok; output 5 vs 15 per m-tok).
• Short-to-medium planning tasks that still need top-tier planning quality but can accept weaker safety calibration and smaller context (200k). Concrete score differences to ground the examples: both models score 5/5 on agentic_planning and tie top rank, but Sonnet leads on safety_calibration (5 vs 2), creative_problem_solving (5 vs 4), context window (1,000,000 vs 200,000), and has external SWE-bench 75.2% and AIME 85.8% (Epoch AI). Haiku's advantage is cost-efficiency and lower latency per the model description and input/output pricing.

For Agentic Planning, choose Claude Haiku 4.5 if you must minimize cost and latency for high-volume agents and can accept weaker safety calibration and a smaller context window. Choose Claude Sonnet 4.6 if safety, complex long-context plans, or stronger creative/problem-solving and external coding/reasoning evidence (SWE-bench 75.2%, AIME 85.8% per Epoch AI) are primary — Sonnet is the safer, more capable production pick.