GPT-5.2 vs Llama 4 Scout

Across our 12-test suite GPT-5.2 wins the majority (8 wins), Llama 4 Scout wins none, and 4 tests tie. Head-to-head highlights (scores from our testing):

• Strategic analysis: GPT-5.2 5 vs Llama 4 Scout 2 — GPT-5.2 tied for 1st of 54 models; Scout ranks 44 of 54. This means GPT-5.2 is markedly better at nuanced tradeoff reasoning with numbers (financial planning, multi-criteria decisions).
• Agentic planning: 5 vs 2 — GPT-5.2 is tied for 1st of 54, Scout ranks 53 of 54; expect stronger goal decomposition and recovery from failures with GPT-5.2.
• Creative problem solving: 5 vs 3 — GPT-5.2 tied for 1st of 54; Scout is mid-pack. Expect more non-obvious, feasible ideas from GPT-5.2.
• Faithfulness: 5 vs 4 — GPT-5.2 tied for 1st of 55; Scout ranks 34 of 55. GPT-5.2 is less likely to hallucinate on source-grounded tasks.
• Safety calibration: 5 vs 2 — GPT-5.2 tied for 1st of 55; Scout ranks 12 of 55. GPT-5.2 better distinguishes harmful vs legitimate requests in our tests.
• Persona consistency & multilingual: GPT-5.2 scores 5 vs Scout 3 and 4 respectively — GPT-5.2 ties for 1st in both (persona: tied for 1st of 53; multilingual: tied for 1st of 55). Expect stronger character retention and non-English parity from GPT-5.2.
• Constrained rewriting: 4 vs 3 — GPT-5.2 (rank 6 of 53) handles hard character/space limits better. Ties (identical scores in our testing): structured output 4/4 (both rank 26 of 54) — both handle JSON/schema compliance similarly; tool calling 4/4 (both rank 18 of 54) — equivalent function-selection behavior; classification 4/4 (both tied for 1st of 53) — both excel at routing/categorization; long context 5/5 (both tied for 1st of 55) — both retrieve accurately at 30K+ tokens. External benchmarks (supplementary): on SWE-bench Verified (Epoch AI) GPT-5.2 scores 73.8% (rank 5 of 12 in our reference) and on AIME 2025 (Epoch AI) GPT-5.2 scores 96.1% (rank 1 of 23 in our reference). Llama 4 Scout has no external SWE/AIME scores in the payload. Overall, GPT-5.2 provides stronger reasoning, safety, and creativity for high-complexity tasks; Scout matches GPT on long context, structured outputs, tool calling, and classification but at a much lower cost.

Costs are per the payload (dollars per 1k tokens assumed). GPT-5.2 input = $1.75/mTok, output = $14/mTok; Llama 4 Scout input = $0.08/mTok, output = $0.30/mTok. That makes GPT-5.2 output tokens 46.67× more expensive (priceRatio = 46.6667). Example costs for 1M total tokens (assuming a 50/50 input/output split): GPT-5.2 ≈ $7,875 per 1M tokens; Llama 4 Scout ≈ $190 per 1M tokens. At 10M tokens/month: GPT-5.2 ≈ $78,750 vs Scout ≈ $1,900. At 100M tokens/month: GPT-5.2 ≈ $787,500 vs Scout ≈ $19,000. If your workload is heavily output-weighted (all-output tokens), 1M output tokens cost $14,000 on GPT-5.2 vs $300 on Scout. Teams doing high-volume, low-margin inference (consumer chat, large batch classification, embeddings-like workloads) should care about the Scout cost advantage; teams that need top-tier strategy, safety, and creative output may justify GPT-5.2’s premium.

Choose GPT-5.2 if you need best-in-class strategic reasoning, agentic planning, safety calibration, creative problem solving, faithfulness, or multilingual persona consistency (it wins 8 of 12 benchmarks and ranks top in several categories). Choose Llama 4 Scout if your priority is cost-efficiency at scale and you mainly need long-context retrieval, classification, structured-output compliance, or tool-calling parity — Scout delivers comparable performance on those four tests at a fraction of the price.