Claude Sonnet 4.6 vs Grok 4 for Coding

What Coding demands: correctness of generated code, reliable tool calling (function selection and arguments), structured output (schema/format compliance), faithful use of source code, long-context reasoning for large codebases, iterative debugging and safety calibration to avoid harmful or destructive suggestions. Primary evidence: on SWE-bench Verified (Epoch AI) — the authoritative external measure included in this payload — Claude Sonnet 4.6 scores 75.2%, which we treat as the primary signal for Coding performance. Our internal proxy scores support and explain that result: Sonnet scores 5/5 on tool_calling, 4/5 on structured_output, 5/5 on faithfulness, 5/5 on long_context, and 5/5 on safety_calibration in our testing. Grok 4 has no SWE-bench score in the payload; internally it scores 4/5 on tool_calling and 4/5 on structured_output, plus 5/5 long_context and 5/5 faithfulness, but only 2/5 on safety_calibration and 3/5 on agentic_planning. Because SWE-bench Verified is the primary benchmark here, and Sonnet posts a measurable external score while Grok does not, Sonnet is the defensible winner for Coding in our comparison.

Where Claude Sonnet 4.6 shines (grounded in scores):

• Large codebase refactoring and multi-file generation: Sonnet's 1,000,000-token context window and 5/5 long_context let it keep more of the repository in context during complex changes. (Context window: 1,000,000 vs 256,000.)
• Tool-driven workflows and end-to-end debugging: Sonnet's 5/5 tool_calling in our testing means more accurate function selection, argument formation, and sequencing — valuable when orchestrating linters, test runners, or CI hooks.
• Safety-sensitive code review and production recommendations: Sonnet's 5/5 safety_calibration and 5/5 faithfulness reduce risky or hallucinated suggestions in our tests. Where Grok 4 is the better pick (grounded in scores & model data):
• Tight rewriting or compression tasks: Grok scores 4/5 on constrained_rewriting (vs Sonnet's 3/5), so it's preferable for strict character-limit transformations or compact code summarization.
• File-based inputs and multi-modal inspections: Grok's modality includes file inputs (text+image+file->text) and its description notes support for parallel tool calling and structured outputs — useful when you supply local project files or binary artifacts for analysis.
• Comparable structured outputs and long-context retrieval: Grok ties Sonnet on structured_output (4/5) and long_context (5/5), so it can match Sonnet on schema compliance and many large-context retrieval tasks despite lacking a SWE-bench score in the payload. Costs and practicalities: both models list identical input/output cost rates in the payload (input 3 / output 15 per mT), so pricing is not a differentiator here.

For Coding, choose Claude Sonnet 4.6 if you need the model that leads on the primary external coding benchmark (75.2% on SWE-bench Verified, Epoch AI), excels at tool calling (5 vs 4), has top safety (5 vs 2), and handles massive contexts (1,000,000 tokens). Choose Grok 4 if you rely on file-based inputs, need stronger constrained rewriting (4 vs 3), or prefer its modality for multi-file inspection and parallel tool workflows—but note Grok 4 has no SWE-bench score in the payload and ranks lower on our Coding task (taskRank 13/52).