GPT-4.1 vs Llama 4 Scout

Overview (in our testing): GPT-4.1 wins 7 tests, Llama 4 Scout wins 1, and 4 tests tie. Detailed walk-through: - Tool calling: GPT-4.1 = 5 vs Scout = 4. GPT-4.1 ranks "tied for 1st with 16 other models out of 54 tested" for tool calling; Scout ranks 18 of 54. This means GPT-4.1 is stronger at selecting functions, composing arguments, and sequencing calls for multi-step tool workflows. - Faithfulness: GPT-4.1 = 5 vs Scout = 4. GPT-4.1 is tied for 1st (with 32 others out of 55), while Scout ranks 34 of 55 — GPT-4.1 better resists hallucination and sticks to source material in our tests. - Multilingual: GPT-4.1 = 5 vs Scout = 4; GPT-4.1 tied for 1st (with 34 others), Scout ranks 36 of 55 — GPT-4.1 delivers higher-quality non-English output in our benchmarks. - Strategic analysis: GPT-4.1 = 5 vs Scout = 2; GPT-4.1 tied for 1st (with 25 others) — it handles nuanced tradeoffs and numeric reasoning better in our suite. - Constrained rewriting: GPT-4.1 = 5 vs Scout = 3; GPT-4.1 tied for 1st with 4 others — better at strict compression and exact-format rewrites. - Persona consistency: GPT-4.1 = 5 vs Scout = 3; GPT-4.1 tied for 1st (with 36 others) — keeps character and resists prompt injection better. - Agentic planning: GPT-4.1 = 4 vs Scout = 2; GPT-4.1 ranks 16 of 54 while Scout ranks 53 of 54 — GPT-4.1 decomposes goals and plans recovery steps more reliably. - Safety calibration: Scout wins 2 vs GPT-4.1's 1; Scout ranks 12 of 55 vs GPT-4.1 rank 32 — Scout is more likely in our tests to refuse clearly harmful requests while allowing legitimate ones. - Ties: structured output (4/4), creative problem solving (3/3), classification (4/4), long context (5/5). Notably both models score 5 on long context and tie for 1st with many models, so retrieval and accuracy at 30K+ tokens are similar in our tests. External benchmarks (Epoch AI): GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 — we report these as supplementary, sourced to Epoch AI.

Per the payload: GPT-4.1 charges $2 per million input tokens and $8 per million output tokens; Llama 4 Scout charges $0.08 per million input and $0.30 per million output. Assuming a 50/50 split of input vs output tokens, combined cost per 1M total tokens is $5.00 for GPT-4.1 vs $0.19 for Llama 4 Scout. At scale (50/50 split): 1M tokens/month = $5.00 vs $0.19; 10M = $50.00 vs $1.90; 100M = $500.00 vs $19.00. The payload also reports a priceRatio of ~26.7x. Who should care: startups, high-volume SaaS, and consumer apps will feel the difference at 10M+ tokens/month; teams building low-volume prototypes or tight-margin products will find Llama 4 Scout far more economical.

Choose GPT-4.1 if you need best-in-class tool calling, faithfulness, multilingual output, constrained rewriting, and strategic analysis for production-grade apps and can justify higher inference spend. Choose Llama 4 Scout if budget is the primary constraint, you need high-context passages at lower cost, or you prioritize a model that scored better on safety calibration in our tests.