Claude Haiku 4.5 vs Claude Sonnet 4.6 for Coding

What Coding demands: correctness (faithfulness), reliable tool calling (function selection, argument accuracy, sequencing), strict structured output (JSON/format compliance), long-context comprehension for large repos, creative problem solving for algorithm design, robust safety calibration for risky code, and agentic planning for multi-step debugging. Primary evidence: Sonnet’s 75.2% on SWE-bench Verified (Epoch AI) is the leading external signal for real-world GitHub issue resolution and is the basis of our winner call. Supporting signals from our internal suite: both models score 5/5 on tool_calling and 4/5 on structured_output (so both handle function selection and schema adherence well). Sonnet’s internal strengths include safety_calibration 5 vs Haiku 2 and creative_problem_solving 5 vs 4, which explain why Sonnet performs better on SWE-bench tasks that require safe, inventive fixes. Context and scale matter: Sonnet’s 1,000,000-token window and 128,000 max output tokens favor large codebase navigation; Haiku’s 200,000-token window is smaller but still strong for medium-sized projects. Cost and latency are trade-offs: Haiku is substantially cheaper (input_cost_per_mtok 1, output_cost_per_mtok 5) vs Sonnet (3 and 15), so cost per inference is materially lower on Haiku.

When to pick Sonnet 4.6 (practical):

• Large monorepo debugging: Sonnet’s 1,000,000-token context and SWE-bench 75.2% make it better for tracing cross-file bugs and producing end-to-end fixes.
• Security- or safety-sensitive code changes: Sonnet’s safety_calibration 5 vs Haiku’s 2 reduces risky suggestions and incorrect-safe/unsafe refusals.
• Complex algorithm design or inventing non-obvious solutions: Sonnet’s creative_problem_solving 5 vs Haiku 4 yields more feasible, novel approaches.
  When to pick Haiku 4.5 (practical):
• Cost-constrained CI/codegen loops: Haiku’s input 1 / output 5 per mTok is ~1/3 the per-mTok price of Sonnet, making it economical for frequent short-generation tasks.
• Fast, iterative unit-level fixes and code snippets: Haiku ties Sonnet on tool_calling (5) and structured_output (4), so for small-to-medium tasks you get similar function-calling and schema adherence at much lower cost.
• Latency-sensitive dev tooling where throughput and cost matter: Haiku’s description notes higher efficiency; combine that with its solid internal ranks (taskRank 13 of 52) for reliable everyday coding.

For Coding, choose Claude Haiku 4.5 if you need a low-cost, efficient model for frequent small-to-medium code generation, CI automation, or fast snippet-level debugging (input_cost_per_mtok 1, output_cost_per_mtok 5; ties Sonnet on tool_calling and structured_output). Choose Claude Sonnet 4.6 if you need the strongest coding performance by external measures (SWE-bench Verified 75.2% according to Epoch AI), plus better safety and creative problem solving, large-context codebase navigation (1,000,000-token window), and higher-ranked results (taskRank 4 of 52).