Claude Haiku 4.5 vs Claude Sonnet 4.6 for Tool Calling

What Tool Calling demands: accurate function selection, precise argument construction, correct sequencing of multi-step calls, and safe refusal/guardrails when a tool request is unsafe. Primary capabilities that matter: structured output format adherence (to populate arguments), agentic planning and long_context for multi-step orchestration, faithfulness to source data, and safety_calibration to avoid unsafe tool execution. In our testing both Claude Haiku 4.5 and Claude Sonnet 4.6 score 5/5 on the tool_calling benchmark (tied). Use our supporting proxies to distinguish them: Sonnet has safety_calibration 5 (Haiku 2) and creative_problem_solving 5 (Haiku 4), while both have structured_output 4, agentic_planning 5, and faithfulness 5. Sonnet also reports SWE-bench Verified 75.2% (Epoch AI), a third-party signal relevant to reliable code/tool usage; Haiku has no SWE-bench entry in the payload. Operational differences that affect tool calling: Sonnet’s context_window is 1,000,000 tokens vs Haiku’s 200,000, and Sonnet’s supported parameters include an extra 'verbosity' control; Haiku emphasizes efficiency and lower input/output costs (input: 1 vs 3, output: 5 vs 15 per m-token).

1. Safe production agent invoking live services (banking, admin APIs): Sonnet 4.6 — higher safety_calibration (5 vs 2) in our tests reduces risky or permissive tool calls; prefer Sonnet when refusals, policy checks, or conservative gating matter. 2) Complex multi-step orchestration across a large codebase or long session: Sonnet 4.6 — larger context_window (1,000,000 vs 200,000) and equal agentic_planning (5) help sequence many tool calls and keep state. 3) Cost-sensitive bulk tool invocation (high-volume calls with simple arguments): Claude Haiku 4.5 — matches Sonnet on tool_calling accuracy in our testing (both 5/5) but at much lower input/output pricing (1/5 vs 3/15 per m-token), so Haiku reduces operating cost for straightforward pipelines. 4) Edge cases requiring creative argument synthesis: Sonnet 4.6 — creative_problem_solving 5 vs Haiku 4 suggests Sonnet will better infer non-obvious argument structuring or plan alternate sequences when inputs are ambiguous. 5) Third-party coding/tool reliability signal: Sonnet 4.6 reports 75.2% on SWE-bench Verified (Epoch AI), a supplementary data point indicating stronger performance on real-world code issue resolution relevant to tool-calling agents.

For Tool Calling, choose Claude Haiku 4.5 if you need the same tool_calling accuracy at much lower cost for straightforward, high-volume tool invocations. Choose Claude Sonnet 4.6 if you need stronger safety calibration, better creative problem solving, larger context for long multi-step orchestration, or the additional SWE-bench (Epoch AI) signal.