Claude Haiku 4.5 vs GPT-4.1 Nano

Summary of head-to-head results (our 12-test suite):

• Strategic analysis: Claude Haiku 4.5 5 vs GPT-4.1 Nano 2 — Haiku wins and is tied for 1st overall in our rankings, so expect stronger nuanced tradeoff reasoning in planning and finance-style tasks. (Haiku ranking: tied for 1st of 54.)
• Creative problem solving: Haiku 4 vs Nano 2 — Haiku wins (rank 9 of 54 for Haiku vs rank 47 for Nano), meaning better, more specific idea generation in brainstorming or R&D prompts.
• Tool calling: Haiku 5 vs Nano 4 — Haiku wins (Haiku tied for 1st; Nano rank 18 of 54). In practice Haiku selects functions, arguments and sequences more accurately in our function-selection tests.
• Classification: Haiku 4 vs Nano 3 — Haiku wins (Haiku tied for 1st; Nano rank 31), so Haiku is more reliable for routing and label assignment in our tests.
• Long context: Haiku 5 vs Nano 4 — Haiku wins and is tied for 1st despite Claude Haiku 4.5’s 200,000-token window vs GPT-4.1 Nano’s larger 1,047,576-token window. In our retrieval-at-30K+ tests Haiku returned more accurate context-aware answers.
• Persona consistency: Haiku 5 vs Nano 4 — Haiku wins (tied for 1st), better at maintaining character and resisting injection in dialog tasks.
• Agentic planning: Haiku 5 vs Nano 4 — Haiku wins (tied for 1st), stronger at goal decomposition and failure recovery in our scenarios.
• Multilingual: Haiku 5 vs Nano 4 — Haiku wins (tied for 1st), better non-English parity in our tests.
• Structured output: Haiku 4 vs Nano 5 — GPT-4.1 Nano wins (Nano tied for 1st). If you need strict JSON/ schema compliance, Nano performed better in our format-adherence tests.
• Constrained rewriting: Haiku 3 vs Nano 4 — GPT-4.1 Nano wins (Nano rank 6 of 53), so Nano handles tight compression and hard character limits more reliably.
• Faithfulness: Haiku 5 vs Nano 5 — tie (both tied for 1st). Both models stick closely to source material in our fidelity checks.
• Safety calibration: Haiku 2 vs Nano 2 — tie (both rank 12 of 55). Both models show similar refusal/permissiveness on harmful prompts in our suite. External math benchmarks (Epoch AI): GPT-4.1 Nano scores 70% on math_level_5 and 28.9% on aime_2025 (Epoch AI). Claude Haiku 4.5 has no external math scores in the payload. These external results should be considered supplementary to our 12-test suite when choosing models for competitive math tasks.

Pricing in the payload is per mTok (per 1,000 tokens). Claude Haiku 4.5: $1 input / $5 output per 1k tokens. GPT-4.1 Nano: $0.1 input / $0.4 output per 1k tokens. For raw token volumes this maps to: per 1M tokens -> Claude Haiku 4.5 = $1,000 input + $5,000 output = $6,000 (1M in + 1M out = $6,000); GPT-4.1 Nano = $100 input + $400 output = $500 (combined $500). At 10M tokens/month: Haiku ≈ $60,000 vs Nano ≈ $5,000. At 100M: Haiku ≈ $600,000 vs Nano ≈ $50,000. The payload lists a priceRatio of 12.5 — Haiku is ~12x–12.5x more expensive per token. Who should care: high-volume deployments, embedded agents, or consumer-facing apps on tight margins must evaluate this gap; teams prioritizing quality for strategy, tool-calling, long-context tasks may accept Haiku’s higher cost; cost-sensitive bulk inference (prototyping, large-scale assistants, low-margin products) should favor GPT-4.1 Nano.

Choose Claude Haiku 4.5 if you need superior strategy, tool-calling, long-context retrieval, agentic planning, persona consistency or multilingual quality and can absorb ~12x higher token costs. Example use cases: production agent backends, complex planning assistants, long-document summarization, multi-language enterprise assistants. Choose GPT-4.1 Nano if budget and latency matter more than the last bit of reasoning quality — it wins for structured outputs and constrained rewriting and costs roughly $500 per 1M in+out tokens vs Haiku’s $6,000. Example use cases: high-volume chatbots with strict cost targets, schema-focused APIs, large-scale prototyping, or constrained-length content transforms.