Claude Haiku 4.5 vs GPT-5

We ran both models across our 12-test internal suite and include external benchmarks where available. Summary from our testing: GPT-5 wins 2 tests (structured_output and constrained_rewriting), Claude Haiku 4.5 wins none, and the remaining 10 tests are ties. Detailed breakdown: - Structured output: GPT-5 scores 5 vs Claude Haiku 4.5’s 4 in our tests; GPT-5 ranks “tied for 1st” (rank 1 of 54, tied with 24) while Haiku ranks 26 of 54. This means GPT-5 is measurably better at JSON/schema compliance and strict format tasks in real workflows. - Constrained rewriting: GPT-5 4 vs Haiku 3; GPT-5 ranks 6 of 53 vs Haiku 31 of 53 — GPT-5 handles hard character/byte-limited compression more reliably. - Strategic analysis: both 5 — tied for 1st in our tests (tied with 25 others), so both handle nuanced tradeoff reasoning similarly. - Creative problem solving: both 4 — tied (rank 9 of 54), so neither has a decisive creative edge. - Tool calling: both 5 — tied for 1st, meaning both pick and sequence functions accurately in our scenarios. - Faithfulness: both 5 — tied for 1st; both stick to sources in our tests. - Classification: both 4 — tied for 1st. - Long context: both 5 — tied for 1st; both handle 30K+ retrieval tasks equally in our suite. - Safety calibration: both 2 — tied at rank 12 of 55; both had similar refusal/permissiveness behavior in our tests. - Persona consistency, agentic planning, multilingual: all ties at top scores (5), indicating parity on these axes. External benchmarks (supplementary): GPT-5 scores 73.6% on SWE-bench Verified (Epoch AI), 98.1% on MATH Level 5 (Epoch AI), and 91.4% on AIME 2025 (Epoch AI) — we cite Epoch AI for these tests. Those external results reinforce GPT-5’s advantage on coding and high-end math tasks. In short: GPT-5’s wins are concentrated on strict format and constrained-rewrite tasks and are supported by high external math/coding scores; Haiku matches GPT-5 across the majority of other internal tasks while being materially cheaper.

Per the payload, Claude Haiku 4.5 charges $1 per 1k-input MTOK and $5 per 1k-output MTOK; GPT-5 charges $1.25 input and $10 output. If you assume 1M input + 1M output tokens: Haiku = $1 + $5 = $6 per million (combined), GPT-5 = $1.25 + $10 = $11.25 per million. Scaled: at 1M tokens/month Haiku ≈ $6 vs GPT-5 ≈ $11.25; at 10M Haiku ≈ $60 vs GPT-5 ≈ $112.50; at 100M Haiku ≈ $600 vs GPT-5 ≈ $1,125. The gap grows linearly and is dominated by output token pricing — if your app produces large outputs (summaries, long responses, generated documents), GPT-5’s $10/m-token output cost will materially raise monthly bills. High-volume consumer apps, chat platforms, and automated document pipelines should care; small-scale experimentation or feature-limited deployments will find Haiku’s 50% cost advantage decisive.

Choose Claude Haiku 4.5 if: - You need near-frontier reasoning, long context (200k tokens), tool calling, agentic planning and persona consistency at the best price (Haiku costs ~50% of GPT-5). - You are cost-sensitive at scale (10M+ tokens/month) or your outputs are short/medium so output-cost impact is limited. Choose GPT-5 if: - You require the best structured-output compliance or reliable constrained rewriting under tight character limits (GPT-5 scored 5 vs Haiku 4). - You need top-tier math/coding performance supported by external benchmarks (MATH Level 5 98.1% and SWE-bench Verified 73.6% per Epoch AI). If you need both cost-efficiency and occasional high-precision schema work, evaluate hybrid routing (Haiku for general workloads, GPT-5 for schema-critical calls).