GPT-4o-mini vs Llama 4 Scout

All scores below are from our 12-test suite. Wins, ties, and ranks are per our testing. Summary: the pair ties on six tests, GPT-4o-mini wins three, and Llama 4 Scout wins three. Detailed walk-through: - Safety calibration: GPT-4o-mini 4 vs Llama 4 Scout 2 — GPT-4o-mini ranks 6 of 55 (tied with 3 others), while Scout ranks 12 of 55. This means GPT-4o-mini is substantially better at refusing harmful prompts and permitting legitimate ones in our tests. - Persona consistency: GPT-4o-mini 4 vs Scout 3 — GPT-4o-mini ranks 38 of 53 vs Scout 45 of 53; GPT-4o-mini maintains character and resists injection better in our scenarios. - Agentic planning: GPT-4o-mini 3 vs Scout 2 — ranks 42 of 54 vs Scout 53 of 54; GPT-4o-mini decomposes goals and recovers from failures more reliably. - Long context (30K+ tokens): GPT-4o-mini 4 vs Scout 5 — Scout is tied for 1st (tied with 36 others) while GPT-4o-mini ranks 38 of 55. For retrieval, summarization, or RAG workflows over very long documents, Scout’s 5 indicates clearer advantages. - Faithfulness: GPT-4o-mini 3 vs Scout 4 — Scout ranks 34 of 55 vs GPT-4o-mini 52 of 55; Scout sticks to source material more in our tests. - Creative problem solving: GPT-4o-mini 2 vs Scout 3 — Scout ranks 30 of 54 vs GPT-4o-mini 47 of 54, so Scout produced more feasible, non-obvious ideas in our prompts. - Ties (structured output 4/4, strategic analysis 2/2, constrained rewriting 3/3, tool calling 4/4, classification 4/4, multilingual 4/4): both models performed equivalently on JSON/schema compliance, tradeoff reasoning, compression tasks, function selection/arguments sequencing, categorization, and non-English outputs in our suite. - Context window & modalities: GPT-4o-mini supports a 128,000-token window and modalities text+image+file->text; Llama 4 Scout supports a larger 327,680-token window and text+image->text. That aligns with Scout’s long-context win. - Supplementary math benchmarks present for GPT-4o-mini in the payload: MATH Level 5 = 52.6% and AIME 2025 = 6.9% (these external-style tests are included in the data). These are additional datapoints from our payload but do not override the 12-test summary above. Overall interpretation: pick GPT-4o-mini when safety, persona, and agentic planning matter; pick Llama 4 Scout when you need maximum long-context fidelity, faithfulness, or lower cost.

Per the payload, GPT-4o-mini charges $0.15 input and $0.60 output per mtok; Llama 4 Scout charges $0.08 input and $0.30 output per mtok (about 2× cost ratio). For a representative 50/50 input/output split: - 1M total tokens (500k input + 500k output): GPT-4o-mini ≈ $375; Llama 4 Scout ≈ $190. - 10M total tokens: GPT-4o-mini ≈ $3,750; Llama 4 Scout ≈ $1,900. - 100M total tokens: GPT-4o-mini ≈ $37,500; Llama 4 Scout ≈ $19,000. At these volumes the ~2× price gap becomes a major operating cost difference for high-throughput APIs, data pipelines, or consumer products; small teams or research experiments may accept GPT-4o-mini’s premium for its safety and assistant strengths, while high-volume services should prefer Llama 4 Scout to cut infrastructure spend.

Choose GPT-4o-mini if: - You run a consumer-facing assistant, moderation-sensitive app, or agentic workflow where safety calibration, persona consistency, and goal decomposition matter (GPT-4o-mini scores 4/4/3 vs Scout’s 2/3/2 on those tests). - You accept ~2× higher inference costs for clearer safety and assistant behavior. Choose Llama 4 Scout if: - Your primary need is long-context retrieval (Scout scores 5 vs GPT-4o-mini 4) or stronger faithfulness (4 vs 3), or you must minimize cost—Scout charges $0.08 input / $0.30 output per mtok vs GPT-4o-mini $0.15 / $0.60. - You operate at high token volumes (10M–100M tokens/month) where Scout’s lower price and larger 327,680-token context window materially reduce costs and improve retrieval accuracy.