GPT-5.4 Mini vs Llama 4 Maverick

GPT-5.4 Mini wins 10 of 11 scored benchmarks (tool calling was rate-limited for Llama 4 Maverick and excluded from that head-to-head) with no losses, and ties on safety calibration and persona consistency.

Strategic Analysis (5 vs 2): The largest gap in this comparison. GPT-5.4 Mini is tied for 1st of 54 models; Llama 4 Maverick ranks 44th of 54. In our testing, this benchmark measures nuanced tradeoff reasoning with real numbers — the kind of analysis needed for business decisions, financial modeling, or evaluating competing options. A 3-point gap here is significant.

Faithfulness (5 vs 4): GPT-5.4 Mini ties for 1st of 55 models; Maverick ranks 34th of 55. This measures how well a model sticks to source material without hallucinating. For RAG pipelines, document summarization, or any task where accuracy to the source is critical, GPT-5.4 Mini has a meaningful edge.

Long Context (5 vs 4): GPT-5.4 Mini ties for 1st of 55; Maverick ranks 38th of 55. Our test evaluates retrieval accuracy at 30K+ tokens. Despite Maverick having a much larger raw context window (1,048,576 vs 400,000 tokens), GPT-5.4 Mini outperforms it on in-context retrieval quality at the tested depth.

Agentic Planning (4 vs 3): GPT-5.4 Mini ranks 16th of 54; Maverick ranks 42nd of 54. Goal decomposition and failure recovery — core to agentic workflows — favor GPT-5.4 Mini. This matters for autonomous agents, multi-step pipelines, and tool-use orchestration.

Structured Output (5 vs 4): GPT-5.4 Mini ties for 1st of 54; Maverick ranks 26th of 54. JSON schema compliance and format adherence are stronger with GPT-5.4 Mini — important for any API integration or data extraction pipeline.

Multilingual (5 vs 4): GPT-5.4 Mini ties for 1st of 55; Maverick ranks 36th of 55. For non-English language products, GPT-5.4 Mini consistently produces higher-quality output in our testing.

Classification (4 vs 3): GPT-5.4 Mini ties for 1st of 53; Maverick ranks 31st of 53. Categorization and routing tasks favor GPT-5.4 Mini, though at Maverick's price point, it may still be cost-effective for simpler classification at high volume.

Constrained Rewriting (4 vs 3): GPT-5.4 Mini ranks 6th of 53; Maverick ranks 31st of 53. Compressing content within hard character limits — useful for ad copy, push notifications, and SEO snippets — is more reliable with GPT-5.4 Mini.

Creative Problem Solving (4 vs 3): GPT-5.4 Mini ranks 9th of 54; Maverick ranks 30th of 54. Generating non-obvious, feasible ideas favors GPT-5.4 Mini.

Safety Calibration (tie, 2 vs 2): Both models share the same score and rank (12th of 55). Neither excels here relative to the field — the p50 across all tested models is also 2, so both sit at the median.

Persona Consistency (tie, 5 vs 5): Both models tie for 1st of 53 alongside 36 other models. For character-driven applications, either model holds up equally well.

Tool Calling: Llama 4 Maverick hit a 429 rate limit on OpenRouter during our tool calling test (noted in the data as likely transient). GPT-5.4 Mini scored 4/5, ranking 18th of 54. No head-to-head comparison is available for this benchmark.

GPT-5.4 Mini costs $0.75/MTok input and $4.50/MTok output. Llama 4 Maverick costs $0.15/MTok input and $0.60/MTok output. On output tokens — the dominant cost driver for most workloads — GPT-5.4 Mini is 7.5x more expensive.

At 1M output tokens/month: GPT-5.4 Mini runs $4.50 vs Llama 4 Maverick's $0.60 — a $3.90 gap that's negligible for most budgets.

At 10M output tokens/month: $45 vs $6 — still manageable, but the difference starts to register for bootstrapped teams.

At 100M output tokens/month: $450 vs $60 — a $390/month delta that meaningfully affects unit economics for high-throughput consumer products.

Developers running classification pipelines, summarization at scale, or any batch processing workflow should model this gap carefully. GPT-5.4 Mini also supports a 400K context window versus Llama 4 Maverick's 1,048,576 token window — so for tasks requiring extremely long context (beyond 400K tokens), Maverick is the only option regardless of cost. For interactive, lower-volume use cases, the quality gap likely justifies the premium.

Choose GPT-5.4 Mini if: Quality is the priority — especially for strategic analysis, faithfulness to source material, long-context retrieval, or agentic workflows. At volumes under 10M output tokens/month, the cost difference is unlikely to be a deciding factor. Also choose it if you need file input support (the payload shows text+image+file modality) or parameters like include_reasoning and seed for reproducibility. Developers building RAG pipelines, autonomous agents, or multilingual products where output accuracy is non-negotiable should default here.

Choose Llama 4 Maverick if: You are running high-volume workloads — 100M+ output tokens/month — where the 7.5x cost difference translates to hundreds of dollars in monthly savings, and your use case can tolerate lower scores on strategic analysis, faithfulness, and agentic planning. Maverick's 1,048,576-token context window also makes it the only option when inputs exceed GPT-5.4 Mini's 400K limit. It's a reasonable fit for bulk classification, lightweight summarization, or experimentation where cost efficiency matters more than top-tier performance. Note that Llama 4 Maverick has a lower max output ceiling (16,384 tokens vs 128,000 for GPT-5.4 Mini), which matters for long-form generation tasks.