GPT-4.1 Nano vs Llama 4 Scout

Across our 12-test suite GPT-4.1 Nano wins 5 tests, Llama 4 Scout wins 3, and 4 tests tie. GPT-4.1 Nano wins: structured output (score 5 vs 4) — tied for 1st of 54 models (tied with 24 others), meaning it’s among the top models for JSON/schema compliance; faithfulness (5 vs 4) — tied for 1st of 55 (tied with 32 others), indicating strong adherence to source material; constrained rewriting (4 vs 3) — rank 6 of 53 for GPT-4.1 Nano, useful for strict-length copy compression; persona consistency (4 vs 3) — GPT rank 38 of 53; and agentic planning (4 vs 2) — GPT rank 16 of 54, so GPT-4.1 Nano is notably better at goal decomposition and recovery. Llama 4 Scout wins: long context (5 vs 4) — Llama tied for 1st of 55 (tied with 36 others), so it performs best at retrieval/accuracy across 30K+ tokens; classification (4 vs 3) — Llama tied for 1st of 53 (tied with 29 others), so routing/categorization tasks favor Scout; and creative problem solving (3 vs 2). Ties: tool calling (4/4, both rank 18 of 54), safety calibration (2/2, both rank 12 of 55), strategic analysis (2/2). Additional model-specific math scores are present only for GPT-4.1 Nano: MATH Level 5 = 70 and AIME_2025 = 28.9 in our tests, where GPT ranks 11/14 on MATH Level 5 and 20/23 on AIME_2025. In practice this means GPT-4.1 Nano is the safer bet for APIs that must emit correct schemas and minimize hallucinations, while Llama 4 Scout is better when long-context retrieval or top-tier classification is primary.

Costs from the payload: GPT-4.1 Nano charges $0.10 input / $0.40 output per mTok; Llama 4 Scout charges $0.08 input / $0.30 output per mTok. Assuming industry shorthand mTok = 1,000 tokens, a 50/50 input/output split per 1,000,000 total tokens gives: GPT-4.1 Nano = (500 mTok * $0.10) + (500 mTok * $0.40) = $50 + $200 = $250 per 1M tokens. Llama 4 Scout = (500 mTok * $0.08) + (500 mTok * $0.30) = $40 + $150 = $190 per 1M tokens. At 10M tokens/month those totals scale to $2,500 vs $1,900; at 100M they scale to $25,000 vs $19,000. The per-1M difference (~$60 with a 50/50 split) means high-volume apps and startups will save materially with Llama 4 Scout. If your usage is low (<<1M tokens/mo) or you need the specific strengths GPT-4.1 Nano shows, the higher cost can be justified; if you operate at tens of millions of tokens, Llama 4 Scout’s lower rates are worth optimizing for.

Choose GPT-4.1 Nano if you need: reliable structured outputs and schema compliance (score 5/5, tied for 1st), high faithfulness (5/5), stronger agentic planning (4/5), or you prioritize fewer format and hallucination errors in production. Choose Llama 4 Scout if you need: best-in-class long-context retrieval (5/5, tied for 1st), top classification performance (4/5, tied for 1st), or a lower per-token bill (input $0.08 / output $0.30 per mTok) for high-volume workloads.