Claude Haiku 4.5 vs Claude Opus 4.6

Across our 12-test suite the two models tie on the majority of measured tasks; Opus wins 2 tests (creative_problem_solving 5 vs 4 and safety_calibration 5 vs 2) while Haiku wins classification (4 vs 3). Ties include strategic_analysis (5/5), tool_calling (5/5), faithfulness (5/5), long_context (5/5), persona_consistency (5/5), agentic_planning (5/5), multilingual (5/5), structured_output (4/4), and constrained_rewriting (3/3). Key pivots: - Safety: Opus scores 5/5 and is tied for 1st on safety_calibration in our testing (ranked tied for 1st of 55), while Haiku scores 2/5 (rank 12 of 55). That matters when you must reliably refuse harmful inputs or tightly gate outputs. - Creative problem solving: Opus 5/5 (tied for 1st) vs Haiku 4/5 (rank 9 of 54); expect Opus to produce more non-obvious feasible ideas in ideation tasks. - Classification/routing: Haiku 4/5 and wins here; Haiku is tied for 1st in classification in our testing, while Opus scores 3/5 (rank 31 of 53), so Haiku is stronger for precise labeling and routing. - Tool calling & long context: both models score 5/5 and are tied for top ranks for tool_calling and long_context in our tests; practically both are strong for function selection and 30K+ token retrieval, but Opus provides a larger context window (1,000,000 vs 200,000) and higher max output tokens (128,000 vs 64,000) which supports longer-running workflows. - External benchmarks (supporting evidence): Claude Opus 4.6 scores 78.7% on SWE-bench Verified and 94.4% on AIME 2025 (Epoch AI), reinforcing its advantage for coding/math tasks in third-party measures. In short: Haiku is the price-efficient choice with top classification and parity on many core capabilities; Opus trades higher cost for stronger creative, safety, and external coding/math signals.

Raw pricing (per mTok): Claude Haiku 4.5 = $1 input / $5 output; Claude Opus 4.6 = $5 input / $25 output. Assuming a 50/50 split of input and output tokens, combined per-mTok cost is $6 for Haiku vs $30 for Opus (Opus is 5x more expensive). At 1M tokens/month (1,000 mTok): Haiku ≈ $6,000; Opus ≈ $30,000. At 10M tokens/month: Haiku ≈ $60,000; Opus ≈ $300,000. At 100M tokens/month: Haiku ≈ $600,000; Opus ≈ $3,000,000. Teams running production APIs, high-volume chat, or cost-sensitive batch generation should favor Haiku; teams where each call must maximize safety, creative/problem-solving, or coding/math accuracy may accept Opus’ higher spend.

Choose Claude Haiku 4.5 if: you need a much lower-cost option for high-throughput chat, classification, and general assistant workloads (Haiku: $1/$5 per mTok; wins classification 4/5 and ties on many core tasks). Choose Claude Opus 4.6 if: you need best-in-class safety calibration (5/5, tied 1st), stronger creative problem solving (5/5), and external coding/math performance (SWE-bench 78.7% and AIME 94.4% per Epoch AI), and you can absorb ~5x per-token cost and prefer a 1,000,000-token context window for long-running agents.