GPT-5.4 Nano vs Llama 4 Maverick

GPT-5.4 Nano wins 9 of 12 benchmarks in our testing, ties 3, and loses 0. Here is the test-by-test breakdown:

Strategic Analysis (5 vs 2): This is the widest gap in the suite. GPT-5.4 Nano scores 5/5 and ranks tied for 1st of 54 models (with 25 others); Llama 4 Maverick scores 2/5 and ranks 44th of 54. For nuanced tradeoff reasoning with real numbers — financial modeling, competitive analysis, policy evaluation — Maverick is a material step down.

Agentic Planning (4 vs 3): GPT-5.4 Nano ranks 16th of 54; Llama 4 Maverick ranks 42nd of 54. Goal decomposition and failure recovery are foundational for multi-step AI agents, and Maverick's score of 3/5 places it well below the field median of 4.

Long Context (5 vs 4): GPT-5.4 Nano scores 5/5 and is tied for 1st of 55; Llama 4 Maverick scores 4/5 and ranks 38th of 55. This is a meaningful gap given Maverick's much larger context window — the raw window size doesn't compensate for lower retrieval accuracy at 30K+ tokens in our tests.

Structured Output (5 vs 4): GPT-5.4 Nano ties for 1st of 54; Maverick ranks 26th of 54. For JSON schema compliance and API-integrated workflows, GPT-5.4 Nano is more reliable.

Multilingual (5 vs 4): GPT-5.4 Nano ties for 1st of 55; Maverick ranks 36th of 55. One score point separates them, but the ranking gap signals Maverick is noticeably weaker for non-English tasks.

Creative Problem Solving (4 vs 3): GPT-5.4 Nano ranks 9th of 54; Maverick ranks 30th of 54.

Constrained Rewriting (4 vs 3): GPT-5.4 Nano ranks 6th of 53; Maverick ranks 31st of 53.

Tool Calling (4 vs unscored): GPT-5.4 Nano scores 4/5 and ranks 18th of 54. Llama 4 Maverick's tool calling test hit a 429 rate limit on OpenRouter during our testing (noted as likely transient), so no score was recorded for Maverick. This means agentic tool-use comparisons cannot be made directly.

Safety Calibration (3 vs 2): GPT-5.4 Nano scores 3/5 and ranks 10th of 55 — notably, it shares that rank with only one other model, placing it in the top tier. Maverick scores 2/5, ranking 12th of 55 but tied with 19 others at that lower score. Neither model tops out on this dimension, but GPT-5.4 Nano is meaningfully more calibrated.

Ties — Faithfulness, Classification, Persona Consistency: Both models score 4/5 on faithfulness and 3/5 on classification, and both tie for 1st on persona consistency with 5/5. For chat applications requiring character consistency, both are equally strong.

External Benchmark — AIME 2025 (Epoch AI): GPT-5.4 Nano scores 87.8% on AIME 2025, ranking 8th of 23 models with that score (sole holder of that exact result). No AIME 2025 score is available for Llama 4 Maverick in our data. This places GPT-5.4 Nano above the field median of 83.9% on this math olympiad benchmark, suggesting strong quantitative reasoning capability.

GPT-5.4 Nano costs $0.20/Mtok input and $1.25/Mtok output. Llama 4 Maverick costs $0.15/Mtok input and $0.60/Mtok output — making the output cost roughly 2.1x cheaper. In practice, output tokens dominate most workloads, so the difference compounds quickly. At 1M output tokens/month: GPT-5.4 Nano costs $1.25 vs Llama 4 Maverick's $0.60 — a $0.65 difference that's trivial. At 10M output tokens/month: $12.50 vs $6.00 — $6.50/month gap, still manageable for most applications. At 100M output tokens/month: $1,250 vs $600 — a $650/month difference that matters to cost-sensitive, high-volume operators. Developers running classification pipelines or chat products at scale will feel the gap; those running low-volume analysis, agentic, or document-processing tasks will likely find GPT-5.4 Nano's quality premium worth the price. Llama 4 Maverick also offers a much larger context window (1,048,576 tokens vs 400,000) at the lower price point, which is relevant for applications that need to process very long documents cheaply.

Choose GPT-5.4 Nano if your workload involves agentic pipelines (it scores 4/5 vs Maverick's 3/5 and ranks 16th vs 42nd of 54), strategic or financial analysis (5/5 vs 2/5 — the largest gap in the suite), structured output generation for APIs, multilingual applications, or tasks requiring reliable long-context retrieval. The $1.25/Mtok output cost is worth paying for any quality-sensitive use case. Choose Llama 4 Maverick if you are running extremely high-volume, low-complexity workloads where the $0.60/Mtok output cost matters at scale (100M+ tokens/month), and your tasks fall primarily into persona-consistent chat or basic faithfulness tasks where both models perform equally. Maverick's 1M-token context window also makes it worth considering if you need to ingest very long documents and GPT-5.4 Nano's 400K window is a hard constraint — just note that retrieval accuracy at depth was lower in our long-context tests.