GPT-5 vs Llama 4 Scout

Across our 12-test suite GPT-5 dominates: it wins 9 tests, Llama 4 Scout wins none, and they tie on 3 (classification, long context, safety calibration). Key head-to-heads from our scoring: - Tool calling: GPT-5 5 vs Llama 4 Scout 4 — GPT-5 is tied for 1st of 54 models (tied with 16) while Llama 4 Scout ranks 18 of 54. This matters for agentic workflows and accurate function selection/arguments. - Strategic analysis: GPT-5 5 vs 2 — GPT-5 is tied for 1st (of 54) vs Llama 4 Scout at rank 44; expect GPT-5 to produce better nuanced tradeoffs and numerical reasoning. - Faithfulness: GPT-5 5 vs 4 — GPT-5 tied for 1st of 55; Llama 4 Scout ranks 34. GPT-5 is less likely to hallucinate on source-driven tasks. - Persona consistency: GPT-5 5 vs 3 — GPT-5 tied for 1st; Llama 4 Scout ranks 45, so GPT-5 better maintains character and resists injection. - Creative problem solving: GPT-5 4 vs 3 — GPT-5 ranks 9 of 54 vs Llama 4 Scout at 30; better for concrete, non-obvious ideas. - Structured output: GPT-5 5 vs 4 — GPT-5 tied for 1st of 54 (better JSON/schema adherence); Llama 4 Scout mid-pack (rank 26). - Constrained rewriting & constrained tasks: GPT-5 4 vs 3 — GPT-5 rank 6 of 53 vs Llama 4 Scout rank 31; better when strict length/compression rules matter. - Classification: tie 4 vs 4 — both tied for 1st (with 29 others), so either model is sufficient for routing/categorization. - Long context: tie 5 vs 5 — both tied for 1st (36 models), so retrieval across 30K+ tokens performs similarly. - Safety calibration: tie 2 vs 2 — both rank 12 of 55; neither is a clear safety outlier in our tests. External benchmarks (Epoch AI) further support GPT-5 on code and math: GPT-5 scores 73.6% on SWE-bench Verified (Epoch AI), ranks 6 of 12 on that external coding benchmark; 98.1% on MATH Level 5 (Epoch AI), ranking 1 of 14; and 91.4% on AIME 2025 (Epoch AI), ranking 6 of 23. Llama 4 Scout has no external SWE/MATH/AIME scores in the payload. Overall, GPT-5’s higher ranks on tool calling, strategic analysis, faithfulness, and math benchmarks translate to stronger performance for complex decision-making, coding, and technical math tasks, while Llama 4 Scout offers close parity on long-context and classification at a fraction of the cost.

We compare costs using the model input+output rates from the payload and assume equal input/output token volume for a simple, illustrative calculation. Combined per‑mTok (1,000 tokens) rates: GPT-5 = $1.25 (input) + $10 (output) = $11.25 per mTok. Llama 4 Scout = $0.08 + $0.30 = $0.38 per mTok. At 1M tokens/month (1,000 mTok) that’s $11,250 (GPT-5) vs $380 (Llama 4 Scout). At 10M tokens: $112,500 vs $3,800. At 100M tokens: $1,125,000 vs $38,000. The ~33× output price ratio (priceRatio = 33.33) makes GPT-5 practical for short, high-value sessions (complex synthesis, mission-critical automation) but cost-prohibitive for heavy, low-margin batch workloads; teams with large throughput (APIs, analytics pipelines, high-volume chat) should care most about the gap.

Choose GPT-5 if you need best-in-class reasoning, tool-calling, structured output, faithfulness, or competition-grade math (it wins 9 of 12 benchmarks in our testing and holds top ranks on MATH Level 5 and SWE-bench Verified). Choose Llama 4 Scout if budget and per-token cost are the limiting factor, or if your workload is long-context retrieval, classification, or high-volume/low-margin throughput where its performance ties GPT-5 but costs far less ($0.38 vs $11.25 per mTok combined).