GPT-5.1 vs Llama 4 Scout

Overview: across our 12-test suite GPT-5.1 wins 7 tests, Llama 4 Scout wins 0, and 5 are ties. All statements below are from our testing. Ties (both models):

• structured output: both score 4 — both handle JSON/schema tasks similarly (rank 26 of 54).
• tool calling: both score 4 — equal function selection and argument accuracy (rank 18 of 54).
• classification: both score 4 — tied for 1st with many models; routing/categorization quality is indistinguishable in our tests.
• long context: both score 5 — both tied for 1st for 30K+ token retrieval accuracy.
• safety calibration: both score 2 — both moderate at refusing harmful requests while permitting legitimate ones (rank 12 of 55). GPT-5.1 wins (with scores):
• strategic analysis 5 vs 2: GPT-5.1 ranks tied for 1st (rank 1 of 54) — better at nuanced tradeoff reasoning with numbers, so choose it for forecasting, pricing, or policy tradeoffs.
• constrained rewriting 4 vs 3: GPT-5.1 (rank 6 of 53) compresses/rewrites into hard limits more reliably.
• creative problem solving 4 vs 3: GPT-5.1 (rank 9 of 54) produces more specific, feasible ideas for product design and ideation tasks.
• faithfulness 5 vs 4: GPT-5.1 is tied for 1st (rank 1 of 55) — sticks to source material with fewer hallucinations in our tests.
• persona consistency 5 vs 3: GPT-5.1 tied for 1st (rank 1 of 53) — maintains character and resists prompt injection better.
• agentic planning 4 vs 2: GPT-5.1 (rank 16 of 54) better decomposes goals and handles failure recovery for agentic workflows.
• multilingual 5 vs 4: GPT-5.1 tied for 1st (rank 1 of 55) — superior non-English parity in our samples. External benchmarks (supplementary): GPT-5.1 scores 68% on SWE-bench Verified and 88.6% on AIME 2025 (Epoch AI); these external results corroborate its coding/math strengths relative to models without listed external scores (attributed to Epoch AI). What this means for real tasks: choose GPT-5.1 when accuracy, faithfulness, multilingual parity, and complex planning matter (e.g., legal drafting, pricing models, multi-language customer support). Choose Llama 4 Scout when cost per token is the dominant constraint but you still need strong long-context, classification, and tool-calling performance.

Per-token pricing is a decisive practical difference. Costs per 1M tokens (per the payload: per mTok ×1000):

• GPT-5.1: input $1.25×1000 = $1,250; output $10×1000 = $10,000. Example 50/50 split = $5,625 per 1M tokens. For 10M/100M tokens: $56,250 / $562,500 respectively (50/50).
• Llama 4 Scout: input $0.08×1000 = $80; output $0.3×1000 = $300. Example 50/50 split = $190 per 1M tokens. For 10M/100M tokens: $1,900 / $19,000 respectively (50/50). GPT-5.1 is ~33.33× more expensive on output tokens (priceRatio 33.33). Teams with heavy volume (10M–100M tokens/month), consumer-facing products, or MLops cost constraints should care: Llama 4 Scout cuts monthly bill by an order of magnitude at scale; GPT-5.1 may only be justified where its quality advantages (reasoning, faithfulness, multilingual) materially affect product outcomes.

Choose GPT-5.1 if you need top-tier reasoning, faithfulness, multilingual capability, or better agentic planning in production — it wins 7 of 12 benchmarks in our tests and ranks tied for 1st on faithfulness, multilingual, long-context, and persona consistency. Choose Llama 4 Scout if budget and scale are the primary drivers: it ties GPT-5.1 on long-context, classification, and tool calling at a fraction of the price ($0.30 vs $10 per mTok output). Specific picks:

• Pick GPT-5.1 for pricing/forecasting models, legal/medical drafting, multilingual customer-facing assistants, or agentic tool-driven pipelines.
• Pick Llama 4 Scout for high-volume chatbots, inexpensive batch classification, or projects where cost per token dominates and occasional quality tradeoffs are acceptable.