GPT-5.4 Mini vs Llama 4 Scout

GPT-5.4 Mini outscores Llama 4 Scout on 8 of 12 benchmarks in our testing, with the two models tying on the remaining 4. Llama 4 Scout wins none.

Where GPT-5.4 Mini leads:

• Strategic analysis (5 vs 2): This is the widest gap in the comparison. GPT-5.4 Mini scores 5/5 and ties for 1st among 54 tested models. Scout scores 2/5, ranking 44th of 54. For tasks requiring nuanced tradeoff reasoning with real numbers — financial modeling, product strategy, risk analysis — this is a decisive difference.

• Agentic planning (4 vs 2): GPT-5.4 Mini ranks 16th of 54; Scout ranks 53rd of 54. Scout is near the bottom of the field on goal decomposition and failure recovery — a critical weakness for any workflow automation or multi-step agent use case.

• Persona consistency (5 vs 3): GPT-5.4 Mini ties for 1st among 53 models; Scout ranks 45th. For chatbot applications, roleplay, or any product where the AI maintains a defined character, Scout is materially weaker.

• Multilingual (5 vs 4): GPT-5.4 Mini ties for 1st among 55 models; Scout ranks 36th. Both score above the field median (p50 = 5), but Scout sits below it at 4.

• Faithfulness (5 vs 4): GPT-5.4 Mini ties for 1st among 55 models; Scout ranks 34th. For RAG pipelines and document summarization where hallucination risk matters, GPT-5.4 Mini is measurably more reliable in our tests.

• Structured output (5 vs 4): GPT-5.4 Mini ties for 1st among 54 models; Scout ranks 26th. Both pass basic JSON compliance, but GPT-5.4 Mini shows stronger schema adherence at the margin.

• Constrained rewriting (4 vs 3): GPT-5.4 Mini ranks 6th of 53; Scout ranks 31st. Compressing copy to hard character limits — ad copy, SMS messages, headline optimization — favors GPT-5.4 Mini.

• Creative problem solving (4 vs 3): GPT-5.4 Mini ranks 9th of 54; Scout ranks 30th. Neither is at the top of this category, but GPT-5.4 Mini is clearly in the upper half while Scout is mid-field.

Where they tie:

• Tool calling (4 vs 4): Both rank 18th of 54, sharing the score with 29 other models. Function selection and argument accuracy are equivalent — a meaningful tie for API-integrated workflows.

• Classification (4 vs 4): Both tie for 1st among 53 models, alongside 29 others. Routing and categorization tasks are a genuine strength for both.

• Long context (5 vs 5): Both tie for 1st among 55 models. Retrieval accuracy at 30K+ tokens is identical — no advantage to either model for large document processing.

• Safety calibration (2 vs 2): Both rank 12th of 55 with identical scores. Neither model stands out positively on refusing harmful requests while permitting legitimate ones — this is a shared weakness relative to the broader market where the p75 sits at only 2 as well, indicating this is an industry-wide challenge at this tier.

The pricing gap here is substantial and warrants real scrutiny. GPT-5.4 Mini costs $0.75 per million input tokens and $4.50 per million output tokens. Llama 4 Scout costs $0.08 input and $0.30 output — roughly 9x cheaper on input and 15x cheaper on output.

At 1M output tokens/month, GPT-5.4 Mini costs ~$4.50 versus Scout's ~$0.30 — a $4.20 monthly difference that's negligible for most teams. At 10M output tokens, that gap becomes $42. At 100M output tokens — the scale of a production API serving millions of requests — you're looking at $450/month for Scout versus $4,500/month for GPT-5.4 Mini, a $4,050 monthly difference.

For developers running high-throughput pipelines (content classification, document routing, structured data extraction at scale), Scout's matching scores on classification (tied for 1st) and tool calling (rank 18 of 54, same as GPT-5.4 Mini) may deliver equivalent results at a fraction of the cost. For lower-volume use cases where quality on agentic planning or strategic analysis matters more than margins, the price gap is easy to absorb.

Choose GPT-5.4 Mini if:

• You're building agentic workflows requiring multi-step planning and failure recovery (scored 4 vs Scout's 2 — Scout ranks near last in our tests)
• Your product requires strategic or analytical reasoning with quantitative nuance (5 vs 2)
• You're deploying a persona-driven chatbot or assistant where character consistency matters (5 vs 3)
• You need reliable multilingual output or strong faithfulness in RAG pipelines
• Output volume is under 10M tokens/month and the quality premium is worth $40 or less

Choose Llama 4 Scout if:

• Your primary use cases are classification, tool calling, or long-context retrieval — Scout ties GPT-5.4 Mini on all three at a 15x lower output cost
• You're running high-throughput pipelines where cost at 100M+ tokens/month is a real constraint ($300 vs $4,500)
• You need a capable model for structured output tasks at scale and can absorb slightly lower schema compliance
• Agentic planning and strategic analysis are not core to your application