Claude Haiku 4.5 vs Claude Sonnet 4.6

Summary of our 12-test suite (internal scores are 1–5; ranks show position among ~53–55 models). Wins: Sonnet 4.6 wins creative_problem_solving (5 vs Haiku 4) and safety_calibration (5 vs Haiku 2) in our testing. Those are material: Sonnet’s safety_calibration is tied for 1st ("tied for 1st with 4 other models out of 55 tested") while Haiku’s safety score ranks 12 of 55 (rank 12 of 55). On creative_problem_solving Sonnet is tied for 1st ("tied for 1st with 7 other models out of 54 tested"); Haiku ranks 9 of 54. Ties: the remaining ten tests are ties — both models score identically on strategic_analysis (5), tool_calling (5), faithfulness (5), agentic_planning (5), persona_consistency (5), multilingual (5), long_context (5), classification (4), structured_output (4), and constrained_rewriting (3). Context for real tasks: - Safety_calibration (Sonnet 5 vs Haiku 2): Sonnet’s 5/5 means it more reliably refuses or permits appropriately in our tests — important for user-facing moderation, compliance, or higher-risk assistants. - Creative_problem_solving (5 vs 4): Sonnet generates more non-obvious, actionable ideas in our prompts, helpful for R&D brainstorming and product innovation. - Tool_calling and agentic_planning tied at 5: both models are strong at function selection, argument accuracy, sequencing, and goal decomposition in our tests, so developer-facing agent workflows should work well on either. External benchmarks (supplementary): Sonnet 4.6 scores 75.2% on SWE-bench Verified (Epoch AI), ranking 4 of 12 — a third-party indicator of coding strength — and 85.8% on AIME 2025 (Epoch AI), rank 10 of 23. The payload contains no external SWE-bench or AIME entries for Haiku, so Sonnet has an explicit external advantage on those public math/coding measures.

Per the payload, Claude Haiku 4.5 costs $1 per 1K input-token-equivalent (listed as $1 per M-token unit) and $5 per M output; Claude Sonnet 4.6 costs $3 input / $15 output per M. That is a 3x multiplier on both input and output (priceRatio = 0.333...). Example monthly costs: per 1M input tokens — Haiku $1, Sonnet $3; per 1M output tokens — Haiku $5, Sonnet $15. If you use 1M input + 1M output: Haiku $6/month vs Sonnet $18/month. Scale to 10M in/out: Haiku $60 vs Sonnet $180. Scale to 100M in/out: Haiku $600 vs Sonnet $1,800. The absolute gap matters most to high-volume producers and API-driven apps (teams at 10M–100M tokens/month); small-scale testers or hobbyists will likely prefer Haiku for cost savings while teams requiring Sonnet’s safer refusals and creative edge may justify the extra spend.

Choose Claude Haiku 4.5 if: - You need near-Sonnet parity across most tasks at much lower cost (Haiku costs $1 input / $5 output per M vs Sonnet $3 / $15). - You operate at high token volumes (10M–100M tokens/month) where the $/M difference multiplies. - Your workloads emphasize long context, tool calling, classification, agentic planning, multilingual output, or faithfulness — all tests where Haiku ties Sonnet in our suite. Choose Claude Sonnet 4.6 if: - Safety-sensitive, regulated, or public-facing applications demand higher safety calibration (Sonnet 5/5 vs Haiku 2/5 in our tests). - You want the highest creative problem-solving in our suite (Sonnet 5 vs Haiku 4) or explicit external coding/math signals (Sonnet: 75.2% SWE-bench Verified and 85.8% AIME 2025 per Epoch AI). - Your team values the extra capability and will absorb a 3x per-token price premium.