GPT-5.4 vs Llama 4 Maverick

GPT-5.4 wins every benchmark where the two models differ, and ties the two where they don't. Here's what that looks like test by test:

Agentic Planning (5 vs 3): GPT-5.4 ties for 1st among 54 models; Maverick ranks 42nd of 54. This is a wide gap — a 2-point difference on a 1-5 scale. For workflows that require goal decomposition, multi-step reasoning, and failure recovery, GPT-5.4 is meaningfully better.

Strategic Analysis (5 vs 2): GPT-5.4 ties for 1st among 54 models; Maverick ranks 44th of 54. This is the largest performance gap in the dataset. If you need nuanced tradeoff reasoning with real numbers — competitive analysis, investment memos, scenario planning — Maverick is a poor fit at its current scores.

Safety Calibration (5 vs 2): GPT-5.4 ranks tied for 1st among just 5 models out of 55 — a genuinely rare score. Maverick scores 2 and ranks 12th. Safety calibration measures whether a model refuses harmful requests while permitting legitimate ones; GPT-5.4's score is a material differentiator for regulated industries or public-facing deployments.

Faithfulness (5 vs 4): GPT-5.4 ties for 1st among 55 models; Maverick ranks 34th. Both are above the 50th percentile (p50 = 5 for faithfulness), but GPT-5.4's top score matters for RAG applications where hallucination risk is costly.

Long Context (5 vs 4): GPT-5.4 ties for 1st among 55 models; Maverick ranks 38th. Both models offer ~1M token context windows, but GPT-5.4's retrieval accuracy at 30K+ tokens is higher in our testing. Note also that GPT-5.4 supports up to 128K output tokens while Maverick caps at 16,384 — a significant architectural difference for long-form generation.

Structured Output (5 vs 4): GPT-5.4 ties for 1st among 54 models; Maverick ranks 26th. Both pass structured output, but GPT-5.4's JSON schema compliance is more reliable in our tests.

Multilingual (5 vs 4): GPT-5.4 ties for 1st among 55 models; Maverick ranks 36th. A one-point gap, but Maverick sits below the median here (p50 = 5), meaning it underperforms most models on non-English output quality.

Tool Calling (4 vs not scored): GPT-5.4 scores 4, ranking 18th of 54. Maverick's tool calling test hit a 429 rate limit on OpenRouter during our testing (noted as likely transient), so we have no comparable score for Maverick on this dimension. GPT-5.4's 4/5 is a solid but not elite result.

Creative Problem Solving (4 vs 3): GPT-5.4 ranks 9th of 54; Maverick ranks 30th. A one-point gap that reflects GPT-5.4's edge in generating non-obvious, feasible ideas.

Constrained Rewriting (4 vs 3): GPT-5.4 ranks 6th of 53; Maverick ranks 31st. Compression within hard character limits favors GPT-5.4.

Classification (3 vs 3, tied): Both models rank 31st of 53. Neither is strong here — both sit at the p50 for classification. If accurate categorization is your primary use case, look at other models.

Persona Consistency (5 vs 5, tied): Both tie for 1st among 53 models. Neither has an edge on maintaining character or resisting injection attacks.

External Benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested) and 95.3% on AIME 2025 (rank 3 of 23 models tested). These are strong results — SWE-bench Verified measures real GitHub issue resolution, and 76.9% places GPT-5.4 above the p75 for that benchmark (p75 = 75.25%). No external benchmark scores are available for Llama 4 Maverick in our dataset.

The cost gap here is stark. GPT-5.4 runs $2.50/M input and $15/M output tokens. Llama 4 Maverick runs $0.15/M input and $0.60/M output tokens — 25x cheaper on output.

At 1M output tokens/month: GPT-5.4 costs $15; Maverick costs $0.60. Negligible in absolute terms, but the ratio is already telling.

At 10M output tokens/month: GPT-5.4 costs $150; Maverick costs $6. The $144 difference starts to matter for side projects or small teams.

At 100M output tokens/month: GPT-5.4 costs $1,500; Maverick costs $60. That's a $1,440/month gap — enough to justify a serious architectural decision.

At 1B output tokens/month: GPT-5.4 runs $15,000; Maverick runs $600. The savings could fund additional engineering headcount.

Who should care? Consumer-facing products with high output volumes — chatbots, document processors, content pipelines — will feel this gap immediately. Developers running infrequent, high-value tasks (legal analysis, complex agentic pipelines, long-context document work) should lean toward GPT-5.4 and absorb the cost. For bulk inference or applications where Maverick's benchmark scores are sufficient, the economics strongly favor Maverick.

Choose GPT-5.4 if:

• You're building agentic workflows that require multi-step planning and failure recovery (scores 5 vs 3 in our testing)
• Strategic analysis or complex reasoning is central to your product — GPT-5.4 scores 5 vs Maverick's 2
• Safety calibration is non-negotiable: GPT-5.4 is among only 5 models to score 5/5 out of 55 tested
• You need long-form generation: GPT-5.4 supports up to 128K output tokens; Maverick caps at 16,384
• You're doing serious coding work: 76.9% on SWE-bench Verified (Epoch AI) puts GPT-5.4 at rank 2 of 12 models on that benchmark
• Your output volume is low enough that the 25x cost premium ($15 vs $0.60/M output tokens) is manageable

Choose Llama 4 Maverick if:

• You're processing high output volumes where $0.60/M output tokens vs $15/M is the deciding factor — at 100M tokens/month, you save $1,440
• Your use case centers on persona consistency or classification, where Maverick ties GPT-5.4 or is equal on our scale
• You need granular sampling control: Maverick exposes temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, and repetition_penalty — parameters GPT-5.4 does not list in the payload
• You can absorb Maverick's lower scores on strategic analysis (2/5) and safety calibration (2/5) without material risk to your product