GPT-4.1 Nano vs Llama 4 Maverick

Summary of our 12-test suite (scores are from our testing unless noted). Wins, ties, and where each model stands:

• GPT-4.1 Nano wins (in our tests) on structured output (5 vs 4). Context: Nano is tied for 1st of 54 models on structured output — strongest choice for strict JSON/schema tasks.
• GPT-4.1 Nano wins on constrained rewriting (4 vs 3). Nano ranks 6 of 53 here vs Llama at rank 31 — useful when you must compress or rephrase within exact character limits.
• GPT-4.1 Nano wins on tool calling (4 vs no successful score for Llama). Nano ranks 18 of 54 (29 models share the score); Llama experienced a tool calling 429 rate-limit on OpenRouter during testing (noting a likely transient issue). For function selection, argument accuracy, and sequencing, Nano is the safer pick in our runs.
• GPT-4.1 Nano wins on faithfulness (5 vs 4). Nano is tied for 1st of 55 models on faithfulness, so it better sticks to source material and avoids hallucination in our tests.
• GPT-4.1 Nano wins on agentic planning (4 vs 3). Nano ranks 16 of 54 vs Llama at rank 42 — Nano performed better at goal decomposition and failure recovery in our scenarios.
• Llama 4 Maverick wins on creative problem solving (3 vs 2). Llama ranks 30 of 54 vs Nano at 47, so it produces more non-obvious, feasible ideas in our tests.
• Llama 4 Maverick wins on persona consistency (5 vs 4). Llama is tied for 1st of 53 models on persona consistency (36 models share the score), making it stronger for character-driven outputs and resisting prompt injection.
• Ties (no clear winner in our tests): strategic analysis (2 vs 2), classification (3 vs 3), long context (both 4), safety calibration (both 2), multilingual (both 4). For long-context retrieval (30K+ tokens) both scored 4 and show similar rankings (long context rank 38 for both).
• External math benchmarks (Epoch AI): GPT-4.1 Nano scores 70% on MATH Level 5 and 28.9% on AIME 2025 (Epoch AI); Llama 4 Maverick has no external math scores in this payload. On our internal math ranks GPT-4.1 Nano is rank 11 of 14 on MATH Level 5 and rank 20 of 23 on AIME 2025.
  Practical meaning: choose GPT-4.1 Nano for reliable schema output, tool-driven workflows, and extraction tasks requiring faithfulness. Choose Llama 4 Maverick when creative ideation or maintaining character/persona is the primary objective.

Pricing per mTok (input/output): GPT-4.1 Nano $0.10/$0.40; Llama 4 Maverick $0.15/$0.60. Assuming a 50/50 split of input vs output tokens, per-month costs are: 1M tokens — GPT-4.1 Nano $250 vs Llama 4 Maverick $375; 10M tokens — $2,500 vs $3,750; 100M tokens — $25,000 vs $37,500. GPT-4.1 Nano runs ~0.667x the cost of Llama 4 Maverick (priceRatio 0.6667). High-volume apps (1M+ tokens/mo), embedded SaaS, or any deployment sensitive to per-token spend should prefer GPT-4.1 Nano for cost savings; smaller projects or those prioritizing persona/creative quality may accept the higher Llama cost.

Choose GPT-4.1 Nano if: you run production APIs or agents that require strict JSON/schema outputs, reliable tool calling, high faithfulness, or you need the lower per-token cost and larger max_output_tokens (32,768). Specific use cases: API-backed form filling, tool orchestration, data extraction, and agent planning.
Choose Llama 4 Maverick if: creative problem solving and persona-driven content are top priorities and you can accept a higher price ($0.15/$0.60 per mTok). Specific use cases: characterful copy, brainstorming with stronger persona consistency, or when creative idea generation is the metric that matters most.