GPT-5.4 Nano vs Llama 4 Scout

GPT-5.4 Nano wins 8 of 12 benchmarks, ties 3, and loses 1 in our testing. Here's the test-by-test breakdown:

Strategic Analysis (5 vs 2): GPT-5.4 Nano scores 5/5, tied for 1st among 54 models with 25 others. Llama 4 Scout scores 2/5, ranking 44th of 54. This is the widest gap in the comparison — for nuanced tradeoff reasoning with real numbers, Llama 4 Scout is a poor choice.

Agentic Planning (4 vs 2): GPT-5.4 Nano scores 4, ranking 16th of 54. Llama 4 Scout scores 2, ranking 53rd of 54 — near the bottom of all models tested. For goal decomposition and failure recovery in agentic workflows, this is a significant liability.

Persona Consistency (5 vs 3): GPT-5.4 Nano scores 5, tied for 1st among 53 models. Llama 4 Scout scores 3, ranking 45th of 53. Character-driven applications, chatbots, and injection-resistant deployments should strongly prefer GPT-5.4 Nano here.

Multilingual (5 vs 4): GPT-5.4 Nano scores 5, tied for 1st among 55 models. Llama 4 Scout scores 4, ranking 36th of 55. Both are capable, but GPT-5.4 Nano is in the top tier.

Structured Output (5 vs 4): GPT-5.4 Nano scores 5, tied for 1st among 54 models. Llama 4 Scout scores 4, ranking 26th. For JSON schema compliance in production pipelines, GPT-5.4 Nano has the edge.

Constrained Rewriting (4 vs 3): GPT-5.4 Nano scores 4, ranking 6th of 53. Llama 4 Scout scores 3, ranking 31st of 53.

Creative Problem Solving (4 vs 3): GPT-5.4 Nano scores 4, ranking 9th of 54. Llama 4 Scout scores 3, ranking 30th of 54.

Safety Calibration (3 vs 2): GPT-5.4 Nano scores 3, ranking 10th of 55. Llama 4 Scout scores 2, ranking 12th. Both are below the median for this benchmark (p50 = 2), but GPT-5.4 Nano is marginally better calibrated — refusing harmful requests while permitting legitimate ones.

Classification (3 vs 4 — Llama 4 Scout wins): Llama 4 Scout's only benchmark win. It scores 4, tied for 1st among 53 models. GPT-5.4 Nano scores 3, ranking 31st. For routing, tagging, and categorization pipelines, Llama 4 Scout matches the best models at a fraction of the cost.

Tool Calling (4 vs 4 — tie): Both models score 4, both rank 18th of 54. Neither has an edge on function selection and argument accuracy.

Faithfulness (4 vs 4 — tie): Both score 4, both rank 34th of 55. Neither model stands out on sticking to source material.

Long Context (5 vs 5 — tie): Both score 5, tied for 1st among 55 models. GPT-5.4 Nano offers a larger context window (400K tokens vs 327K tokens), but both excel at retrieval accuracy at 30K+ tokens.

External Benchmark — AIME 2025 (Epoch AI): GPT-5.4 Nano scores 87.8% on AIME 2025, ranking 8th of 23 models with scores available — above the median of 83.9% for this benchmark set. No AIME 2025 score is available for Llama 4 Scout in our data.

GPT-5.4 Nano costs $0.20/MTok input and $1.25/MTok output. Llama 4 Scout costs $0.08/MTok input and $0.30/MTok output. At 1M output tokens/month, GPT-5.4 Nano costs $1.25 versus $0.30 for Llama 4 Scout — a $0.95 difference that's negligible. At 10M output tokens, the gap grows to $9.50 ($12.50 vs $3.00). At 100M output tokens — typical for a production chatbot or high-volume API integration — GPT-5.4 Nano costs $125 versus $30 for Llama 4 Scout, a $95/month difference. For developers running classification pipelines or high-volume routing tasks where Llama 4 Scout ties or wins on benchmarks, the cost savings are hard to ignore. For applications requiring strategic analysis, agentic workflows, or persona-consistent chat, GPT-5.4 Nano's performance edge likely justifies the premium.

Choose GPT-5.4 Nano if: you need reliable agentic workflows (scored 4 vs Llama 4 Scout's near-bottom 2), strategic analysis (5 vs 2), persona-consistent chat (5 vs 3), or high-quality structured output (5 vs 4). Also choose it for multilingual applications where top-tier output quality matters, or for math-heavy tasks where its 87.8% AIME 2025 score (Epoch AI) gives confidence. Its 400K context window also beats Llama 4 Scout's 327K if you're pushing context limits.

Choose Llama 4 Scout if: your workload is primarily classification, routing, or tagging — the one benchmark where it outscores GPT-5.4 Nano (4 vs 3, tied for 1st among 53 models). At $0.08/$0.30 per MTok versus $0.20/$1.25, Llama 4 Scout is the rational choice for high-volume, classification-heavy pipelines where the 4× output cost premium of GPT-5.4 Nano would compound quickly without a meaningful quality return on that specific task.