GPT-5 Nano vs Llama 4 Scout

Head-to-head by test (our 12-test suite):

• GPT-5 Nano wins (A): structured output 5 vs 4 — tied for 1st in our ranking ("tied for 1st with 24 other models out of 54 tested"); this means better JSON/schema adherence for integrations that require strict formats.
• GPT-5 Nano wins: strategic analysis 4 vs 2 — rank for A is 27 of 54 (shows stronger nuanced tradeoff reasoning in our tests).
• GPT-5 Nano wins: safety calibration 4 vs 2 — A ranks 6 of 55 (permits legitimate requests while refusing harmful ones more reliably in our testing).
• GPT-5 Nano wins: persona consistency 4 vs 3 — A ranks 38 of 53 (better at maintaining voice and resisting injection).
• GPT-5 Nano wins: agentic planning 4 vs 2 — A ranks 16 of 54 (stronger goal decomposition and recovery in our scenarios).
• GPT-5 Nano wins: multilingual 5 vs 4 — A is tied for 1st with many top models ("tied for 1st with 34 other models out of 55 tested"), so non‑English outputs are higher quality in our checks.
• Llama 4 Scout wins (B): classification 4 vs 3 — B is tied for 1st on classification ("tied for 1st with 29 other models out of 53 tested"), so routing and categorization tasks favored Scout in our runs.
• Ties: constrained rewriting (3/3), creative problem solving (3/3), tool calling (4/4), faithfulness (4/4), long context (5/5) — both models performed equivalently on these. Notably both tie for top long context rank ("tied for 1st with 36 other models out of 55 tested"), so retrieval across 30K+ tokens behaved similarly in our testing.
• External math benchmarks (supplementary): GPT-5 Nano scored 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI), indicating strong formal math performance on those external measures. Overall: GPT-5 Nano wins 6 tests, Llama 4 Scout wins 1, and 5 are ties (per our win/tie list). Those wins map to concrete strengths for strict-format outputs, multilingual correctness, and safety behavior — all important for developer-facing integrations.

Per-token rates from the payload: GPT-5 Nano charges $0.05 per 1k input tokens and $0.40 per 1k output tokens; Llama 4 Scout charges $0.08 per 1k input and $0.30 per 1k output. For a 50/50 input/output split: 1M tokens (1,000k) costs GPT-5 Nano = $225 (500k input @ $0.05 + 500k output @ $0.40) and Llama 4 Scout = $190 (500k @ $0.08 + 500k @ $0.30) — a $35/month gap. Scale that linearly: at 10M tokens/month the gap is $350 (GPT-5 Nano $2,250 vs Scout $1,900); at 100M tokens/month it’s $3,500 (GPT-5 Nano $22,500 vs Scout $19,000). If your workload is output-heavy (long replies or many returned tokens), the output-rate gap ($0.40 vs $0.30 per 1k) dominates: 1M output-only tokens cost $400 vs $300 (GPT-5 Nano vs Scout). If your workload is input-heavy (short replies, long prompts), GPT-5 Nano’s cheaper input rate ($0.05 vs $0.08) can reduce bills. Teams with multi-million token usage should care about the output-rate delta; smaller projects will prioritize capability differences over these per-token gaps.

Choose GPT-5 Nano if: you need reliable structured outputs (JSON/schema compliance), better multilingual quality, stronger safety calibration, longer context (400K tokens), or superior agentic / strategic reasoning in integrations — accept higher output costs. Choose Llama 4 Scout if: classification and per‑token output cost matter more (it charges $0.30/1k output vs $0.40/1k), you want a slightly lower bill on output-heavy workloads, or you prioritize the lowest output cost while keeping comparable tool calling, faithfulness, long-context, and creative capabilities.