Claude Haiku 4.5 vs Claude Opus 4.6 for Coding

What Coding demands: reliable tool calling (function selection, correct args, sequencing), strict structured output (JSON/patches), deep long-context handling (multi-file repos, 30K+ tokens), faithful adherence to source code, strong safety calibration for risky actions, and agentic planning for multi-step refactors or test-driven repairs. Primary signal: SWE-bench Verified (Epoch AI) is the authoritative external measure for software-engineering tasks in our payload — Opus scores 78.7% on that benchmark, which is the primary basis for the winner call. Supporting evidence from our internal proxies: both models score 5 on tool_calling and 5 on long_context, and both have structured_output = 4, meaning both can follow JSON schemas and format code patches. Opus pulls ahead on creative_problem_solving (5 vs 4) and safety_calibration (5 vs 2), which matter for proposing non-trivial fixes and refusing unsafe code actions. Haiku lacks an external SWE-bench score in the data, which leaves its comparative real-world coding reliability unverified by that primary benchmark despite strong internal efficiency claims.

Where Claude Opus 4.6 shines: - Large-scale repo refactor: Opus has long_context 5, agentic_planning 5 and scored 78.7% on SWE-bench Verified — good for multi-file transformations and coordinated test/regression updates. - Complex debugging with tests: Opus's creative_problem_solving 5 and safety_calibration 5 reduce risky suggestions and produce more reliable fixes. - Agentic workflows (CI integrations, multi-step tool chains): tool_calling 5 combined with Opus's high external benchmark makes it the safer choice. Where Claude Haiku 4.5 shines: - Cost-sensitive single-file generation or prototyping: Haiku has tool_calling 5 and long_context 5 but much lower per-mtoken cost (input 1 / output 5 vs Opus input 5 / output 25), so it’s attractive for high-throughput, lower-cost iterations. - Fast iterations where aggressive refusal behavior is less critical: Haiku’s lower safety_calibration (2) can mean looser handling of edge cases—acceptable for sandbox prototyping but risky in production. Direct score references: Opus — SWE-bench Verified 78.7% (Epoch AI), creative_problem_solving 5 vs Haiku 4, safety_calibration 5 vs Haiku 2, and both models tie at tool_calling 5 and structured_output 4.

For Coding, choose Claude Haiku 4.5 if you need lower-cost, high-throughput single-file code generation or rapid prototyping where SWE-bench verification is not required. Choose Claude Opus 4.6 if you want the best coding reliability per our external benchmark (SWE-bench Verified 78.7%, Epoch AI), need safer refusal/permission decisions, multi-file refactors, or agentic, long-running developer workflows — accepting materially higher cost (Opus input 5 / output 25 per mtoken vs Haiku input 1 / output 5).