GPT-5.4 vs Llama 4 Scout

Across our 12-test internal suite, GPT-5.4 outscores Llama 4 Scout on 9 benchmarks, ties on 2, and loses 1.

Where GPT-5.4 wins decisively:

• Agentic planning: GPT-5.4 scores 5/5 (tied for 1st of 54 models with 14 others); Scout scores 2/5, ranking 53rd of 54 — near last. For any multi-step agent workflow requiring goal decomposition or failure recovery, Scout is a serious liability here.
• Strategic analysis: GPT-5.4 scores 5/5 (tied for 1st of 54 with 25 others); Scout scores 2/5, ranking 44th of 54. Complex tradeoff reasoning — business analysis, financial modeling prompts, competitive strategy — heavily favors GPT-5.4.
• Safety calibration: GPT-5.4 scores 5/5, tied for 1st of 55 with only 4 other models — a rare distinction. Scout scores 2/5, ranking 12th of 55. In our testing, safety calibration measures both refusal of harmful requests and avoidance of over-refusal on legitimate ones. The gap is significant for production deployments.
• Faithfulness: GPT-5.4 scores 5/5 (tied 1st of 55 with 32 others); Scout scores 4/5, ranking 34th of 55. RAG pipelines and summarization tasks where hallucination is costly favor GPT-5.4.
• Persona consistency: GPT-5.4 scores 5/5 (tied 1st of 53 with 36 others); Scout scores 3/5, ranking 45th of 53 — bottom quartile. Chatbot and assistant products that rely on stable personas should note this gap.
• Multilingual: GPT-5.4 scores 5/5 (tied 1st of 55 with 34 others); Scout scores 4/5, ranking 36th of 55. Both are capable, but GPT-5.4 edges out Scout for non-English workflows.
• Structured output: GPT-5.4 scores 5/5 (tied 1st of 54 with 24 others); Scout scores 4/5, ranking 26th of 54. JSON schema compliance is strong on both, but GPT-5.4 has the edge.
• Constrained rewriting: GPT-5.4 scores 4/5, ranking 6th of 53; Scout scores 3/5, ranking 31st of 53.
• Creative problem solving: GPT-5.4 scores 4/5, ranking 9th of 54; Scout scores 3/5, ranking 30th of 54.

Ties:

• Tool calling: Both score 4/5, both ranked 18th of 54 (29 models share this score). Function selection and argument accuracy are equivalent — neither has an edge here.
• Long context: Both score 5/5, both tied for 1st of 55 with 36 other models. At 30K+ token retrieval, both perform equally well within our tests. Note that GPT-5.4 has a 1,050,000-token context window vs Scout's 327,680 tokens — a structural difference for extremely long documents, though both are well beyond typical use cases.

Where Scout wins:

• Classification: Scout scores 4/5 (tied 1st of 53 with 29 others); GPT-5.4 scores 3/5, ranking 31st of 53. For document routing, intent detection, and categorization tasks, Scout matches the top tier while GPT-5.4 sits in the bottom half of tested models on this dimension.

External benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified, ranking 2nd of 12 models with this score in our dataset — placing it among the strongest coding models by that external measure. It also scores 95.3% on AIME 2025, ranking 3rd of 23 models. No external benchmark scores are available in the payload for Llama 4 Scout.

GPT-5.4 is priced at $2.50/M input tokens and $15.00/M output tokens. Llama 4 Scout costs $0.08/M input and $0.30/M output — a 31x input gap and 50x output gap. In practice, at 1M output tokens/month, GPT-5.4 costs $15 vs Scout's $0.30 — a $14.70 difference that's easy to absorb. At 10M output tokens, that's $150 vs $3 — still manageable for many API budgets. At 100M output tokens/month, the gap becomes $1,500 vs $30, a $1,470 monthly difference that meaningfully impacts unit economics for consumer-scale products or high-throughput pipelines. Developers building classification systems, document routers, or long-context summarization pipelines at scale have a concrete financial case for Scout. Anyone building agents, copilots, or systems requiring strategic reasoning should evaluate whether GPT-5.4's performance advantage justifies the cost at their volume.

Choose GPT-5.4 if you're building agents, copilots, or any system that requires multi-step planning, strategic reasoning, or reliable safety behavior. Its 5/5 scores on agentic planning, strategic analysis, safety calibration, faithfulness, and persona consistency are material advantages for production AI applications. Its 76.9% SWE-bench Verified score (Epoch AI, ranked 2nd of 12) and 95.3% AIME 2025 score (ranked 3rd of 23) also make it a strong candidate for coding assistants and math-intensive applications. The $15/M output token price is justified if quality and reliability directly affect your product's value.

Choose Llama 4 Scout if your primary workload is classification, document routing, or long-context retrieval — the three areas where Scout either matches or beats GPT-5.4. At $0.30/M output tokens, Scout is 50x cheaper, making it the economically rational choice for high-volume inference pipelines where those specific capabilities are sufficient. Developers running 100M+ output tokens per month will save over $1,400/month by using Scout where it's competitive. Scout also ties GPT-5.4 on tool calling, so agentic workflows that rely on function calls — but don't require complex multi-step planning — may find Scout adequate at a fraction of the cost.