Claude Opus 4.6 vs Claude Opus 4.7

Across our 12-test suite the matchup is tightly clustered: Claude Opus 4.6 wins 2 tests, Claude Opus 4.7 wins 1, and 9 tests tie. Detailed walkthrough:

• Safety calibration: Opus 4.6 scores 5 vs Opus 4.7's 3; in our rankings Opus 4.6 is "tied for 1st with 4 other models out of 56 tested" while Opus 4.7 is "rank 10 of 56 (3 models share this score)" — meaning 4.6 is substantially more likely to refuse harmful prompts and accept legitimate ones in our safety scenarios.

• Multilingual: Opus 4.6 scores 5 vs Opus 4.7's 4; Opus 4.6 ranks "tied for 1st with 34 other models out of 56 tested" while Opus 4.7 ranks "36 of 56" — pick 4.6 for higher-quality non-English outputs.

• Constrained rewriting: Opus 4.7 wins (4 vs 3). Opus 4.7 ranks "rank 6 of 55 (26 models share this score)" versus Opus 4.6's "rank 32 of 55" — that reflects better compression and precision inside hard character limits for Opus 4.7.

• Ties (no clear winner): tool calling (both 5; "tied for 1st with 17 other models out of 55"), agentic planning (both 5; "tied for 1st with 15 other models out of 55"), long context (both 5; "tied for 1st with 37 other models out of 56"), strategic analysis (both 5; "tied for 1st with 26 other models"), creative problem solving (both 5), faithfulness (both 5), structured output (both 4), classification (both 3), and persona consistency (both 5). In practice those ties mean both models will behave similarly on agent workflows, long-context retrieval, tool selection, and creative tasks.

• External benchmarks: Beyond our internal scores, Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI), placing it "rank 1 of 12 (sole holder)" on that external coding benchmark in our data, and 94.4% on AIME 2025 (Epoch AI), where it ranks "4 of 23". Opus 4.7 has no external benchmark entries in the provided data. Overall, 4.6 is the safer, more multilingual and better-evidenced option; 4.7 is narrowly better for constrained rewriting.

Input costs $5 per million tokens and output costs $25 per million tokens for both Claude Opus 4.6 and Claude Opus 4.7. That means: per million input tokens = $5; per million output tokens = $25. Example mixes: if you generate 1M output tokens and consume 1M input tokens, total = $30; 10M input + 10M output = $300; 100M + 100M = $3,000. If your pipeline is balanced (50/50 input vs output by count), combined cost per total million tokens is $15: so 1M combined (0.5M input + 0.5M output) ≈ $15, 10M combined ≈ $150, 100M combined ≈ $1,500. The cost gap does not exist here (price parity), so only latency, throughput, and benchmark performance should drive selection. Teams that produce large volumes of generated output (long-form generation, transcripts, code synthesis) should care because output tokens are billed at $25/M and dominate total spend.

Choose Claude Opus 4.6 if: you need stronger safety calibration and multilingual quality (4.6 scores 5 vs 3 on safety, 5 vs 4 on multilingual), you prioritize SWE-bench Verified and AIME 2025 performance (78.7% and 94.4% on Epoch AI benchmarks), or you run safety-sensitive, multi-language, or long-running agent workflows. Choose Claude Opus 4.7 if: your primary requirement is constrained rewriting/compression inside hard limits (4.7 scores 4 vs 3) and you otherwise accept parity on tool calling, agentic planning, long context, and creative problem solving. Note: both models have identical pricing ($5/M input, $25/M output), so choose on capability not cost.