Claude Haiku 4.5 vs GPT-4.1

Summary of our 12-test comparison (scores on a 1–5 scale in our testing):

• Wins for Claude Haiku 4.5: creative_problem_solving 4 vs 3 (Haiku), agentic_planning 5 vs 4 (Haiku), safety_calibration 2 vs 1 (Haiku). These wins mean Haiku generated more feasible, novel ideas in our creative tasks, decomposed goals and recovery steps better in agentic planning, and scored higher at refusing harmful/allowing legitimate requests in our safety calibration tests. Claude ties or leads in high-level planning: it is tied for 1st on strategic_analysis and ranks tied for 1st on agentic_planning (tied with 14 others) per our rankings.
• Win for GPT-4.1: constrained_rewriting 5 vs 3 (GPT-4.1). GPT-4.1 performed substantially better on hard compression / strict-character-limit rewriting in our tests and ranks tied for 1st in constrained_rewriting (tied with 4 others). For tasks that require aggressive compression or exact short-form transformations, GPT-4.1 is the clear choice.
• Ties (parity): structured_output 4/4, strategic_analysis 5/5, tool_calling 5/5, faithfulness 5/5, classification 4/4, long_context 5/5, persona_consistency 5/5, multilingual 5/5. In practical terms, both models are equally strong at following schemas, long-context reasoning, multilingual outputs, and tool selection/sequencing in our tests — ranks reflect many models share top scores (e.g., both tied for 1st in long_context and faithfulness).
• External third-party benchmarks (supplementary): GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 (Epoch AI). These external numbers are from Epoch AI and are useful supplemental evidence for GPT-4.1's coding and math capabilities but do not override our internal 12-test results. Claude Haiku 4.5 has no external scores in the payload to report. Net effect for real tasks: choose Haiku when you need cheaper, reliable agentic planning, creative generation, and slightly better safety calibration; choose GPT-4.1 for compression-heavy rewriting or when you want to consider its external SWE-bench/MATH evidence for some coding/math workflows.

Per the payload: Claude Haiku 4.5 charges $1 per 1M input tokens and $5 per 1M output tokens; GPT-4.1 charges $2 per 1M input and $8 per 1M output. Example cost snapshots (using only stated per‑token rates):

• 50/50 input/output split (common baseline): Haiku = $3.00 per 1M total tokens; GPT-4.1 = $5.00 per 1M. At scale that's Haiku $30 vs GPT $50 for 10M total tokens, and $300 vs $500 for 100M.
• Output-heavy (all tokens as output): Haiku $5 / 1M, $50 / 10M, $500 / 100M; GPT-4.1 $8 / 1M, $80 / 10M, $800 / 100M.
• Input-heavy (all tokens as input): Haiku $1 / 1M vs GPT $2 / 1M. Who should care: high-volume products (chatbots, analytics pipelines, large-scale agents) will see nontrivial monthly savings with Haiku — e.g., $200/month saved per 10M tokens (50/50) and $2000/month per 100M tokens. Small-volume experimenters won't feel the difference immediately, but teams running tens of millions of tokens monthly should prioritize the cheaper model unless a specific benchmark gap justifies the premium.

Choose Claude Haiku 4.5 if: you need a lower-cost model (Haiku is ~62.5% of GPT-4.1's token cost ratio), care about agentic planning (5 vs 4), creative problem solving (4 vs 3), better safety calibration (2 vs 1), or are running high token volumes where cost savings compound. Choose GPT-4.1 if: your primary workload is constrained rewriting/compression where it scored 5 vs Haiku's 3, or you want to factor in supplementary external benchmarks (SWE-bench Verified 48.5%, MATH Level 5 83%, AIME 2025 38.3% per Epoch AI) that support certain coding/math tasks.