Grok 3 vs Llama 4 Scout

All benchmark claims below are from our testing across the 12-test suite. Summary: Grok 3 wins 6 categories, Llama 4 Scout wins none, and 6 are ties. Detailed walk-through: - Structured_output: Grok 3 scores 5 vs Llama 4 Scout 4; in our rankings Grok 3 is tied for 1st ("tied for 1st with 24 other models out of 54 tested"), indicating strong JSON/schema compliance—useful for extraction and API output. - Strategic_analysis: Grok 3 5 vs Scout 2; Grok 3 ranks 1st of 54 (tied) while Scout ranks 44 of 54, so Grok 3 gives better nuanced tradeoff reasoning with numbers. - Faithfulness: Grok 3 5 vs Scout 4; Grok 3 ties for 1st ("tied for 1st with 32 other models out of 55 tested"), so it better sticks to source material in our tests. - Persona_consistency: Grok 3 5 vs Scout 3; Grok 3 ties for 1st in our ranking, while Scout sits near the bottom (rank 45 of 53), meaning Grok 3 resists prompt injection and maintains character more reliably. - Agentic_planning: Grok 3 5 vs Scout 2; Grok 3 is tied for 1st on decomposition and failure recovery, Scout ranks 53 of 54. - Multilingual: Grok 3 5 vs Scout 4; Grok 3 ties for 1st, so it produced higher-quality non-English outputs in our tests. Ties (no clear winner in our testing): constrained rewriting (3 vs 3), creative problem solving (3 vs 3), tool calling (4 vs 4; both rank 18 of 54 tied), classification (4 vs 4; both tied for 1st), long context (5 vs 5; both tied for 1st), and safety calibration (2 vs 2; both rank 12 of 55). Practical meaning: choose Grok 3 when you need reliable schema outputs, faithful summaries, complex planning, or consistent persona; choose Llama 4 Scout when equivalent performance on long-context retrieval, tool calling, and classification suffices but you need much lower inference cost and multimodal (text+image->text) input support. Also note context windows: Grok 3 = 131,072; Llama 4 Scout = 327,680 with a 16,384 max_output_tokens—relevant for very long documents or image+text inputs.

Per the payload, Grok 3 charges $3 input and $15 output per mTok; Llama 4 Scout charges $0.08 input and $0.30 output per mTok (priceRatio = 50). Raw costs per 1M tokens (1,000 mTok): Grok 3 = $3,000 input / $15,000 output; Llama 4 Scout = $80 input / $300 output. If you split tokens 50/50 into input/output as an example: Grok 3 ≈ $9,000/month (1M tokens), $90,000/month (10M), $900,000/month (100M). Llama 4 Scout ≈ $190/month (1M), $1,900/month (10M), $19,000/month (100M). The 50x output-price gap means teams with heavy traffic (customer chat, bulk summarization, large-scale inference) should prefer Llama 4 Scout to control costs; teams that need top-tier structured outputs, fidelity, and planning should budget for Grok 3 and expect substantially higher monthly bills.

Choose Grok 3 if you need production-grade structured outputs, high faithfulness, strong agentic planning, or top multilingual/persona consistency in our tests — e.g., enterprise extraction, deterministic API responses, or complex decisioning. Choose Llama 4 Scout if cost is the primary constraint or you need large-context or multimodal inputs (text+image->text) at scale — e.g., high-volume chat, large-batch summarization, or image+text pipelines where the $0.30 vs $15/mTok output gap makes a practical difference.