Grok 3 Mini vs Llama 4 Scout

Grok 3 Mini wins 6 benchmarks outright; Llama 4 Scout wins none. The two models tie on 6 tests: structured output (both 4/5), creative problem solving (both 3/5), classification (both 4/5), long context (both 5/5), safety calibration (both 2/5), and multilingual (both 4/5).

Where Grok 3 Mini leads:

• Tool calling (5 vs 4): Grok 3 Mini scores 5/5, tied for 1st among 54 models in our testing. Llama 4 Scout scores 4/5, ranking 18th of 54. For function selection, argument accuracy, and sequencing in agentic or API-integration contexts, Grok 3 Mini is the stronger choice.
• Faithfulness (5 vs 4): Grok 3 Mini scores 5/5, tied for 1st among 55 models. Llama 4 Scout scores 4/5 (rank 34 of 55). When sticking to source material without hallucinating is critical — summarization, RAG, document Q&A — Grok 3 Mini is notably more reliable in our tests.
• Persona consistency (5 vs 3): A significant gap. Grok 3 Mini scores 5/5, tied for 1st among 53 models. Llama 4 Scout scores 3/5, ranking 45th of 53. This matters for chatbot applications, character-driven products, or any use case requiring stable identity and resistance to prompt injection.
• Agentic planning (3 vs 2): Both models underperform here relative to the field — Grok 3 Mini ranks 42nd of 54 (3/5), Llama 4 Scout ranks 53rd of 54 (2/5), near the bottom of all models tested. Neither is a strong multi-step reasoning agent, but Grok 3 Mini is the less weak option.
• Strategic analysis (3 vs 2): Grok 3 Mini scores 3/5 (rank 36 of 54); Llama 4 Scout scores 2/5 (rank 44 of 54). Neither excels at nuanced tradeoff reasoning, but Grok 3 Mini is a step ahead.
• Constrained rewriting (4 vs 3): Grok 3 Mini scores 4/5 (rank 6 of 53); Llama 4 Scout scores 3/5 (rank 31 of 53). Grok 3 Mini handles compression within hard character limits considerably better.

Where they tie:

• Long context (both 5/5): Both top-tier, tied for 1st among 55 models. Both handle 30K+ token retrieval tasks well in our testing, though Llama 4 Scout's 327,680-token window is physically larger than Grok 3 Mini's 131,072 — relevant if you need to process very large documents in a single call.
• Safety calibration (both 2/5): Both rank 12th of 55, sharing the same score with 18 other models. Neither model stands out on refusing harmful requests while permitting legitimate ones.
• Multilingual (both 4/5): Tied at rank 36 of 55 — solid but not top-tier for non-English output quality.
• Classification (both 4/5): Both tied for 1st among 53 models — strong for routing and categorization tasks.
• Structured output (both 4/5): Both rank 26th of 54 — reliable JSON schema compliance but not best-in-class.
• Creative problem solving (both 3/5): Both rank 30th of 54 — average performance for generating non-obvious, feasible ideas.

Grok 3 Mini costs $0.30/M input and $0.50/M output. Llama 4 Scout costs $0.08/M input and $0.30/M output — making it 3.75× cheaper on input and 1.67× cheaper on output. In practice: at 1M output tokens/month, Grok 3 Mini costs $0.50 vs Llama 4 Scout's $0.30 — a $0.20 gap that's negligible. At 10M output tokens, that's $5 vs $3 — still small. At 100M output tokens, it's $500 vs $300 — a $200/month difference that starts mattering for production-scale deployments. The cost gap is most relevant for developers running high-throughput pipelines where quality differences on agentic planning or tool calling don't justify the premium. For enterprise use cases relying on accurate function calling or multi-step agents, Grok 3 Mini's performance edge likely outweighs the $200/month difference at even high volumes. Llama 4 Scout also has a 327,680-token context window versus Grok 3 Mini's 131,072 — so very-long-document workloads may favor Scout on context capacity alone, independent of price.

Choose Grok 3 Mini if: you're building applications where tool calling, faithfulness to source material, or persona consistency are core requirements — it scores materially higher on all three in our testing. It's also the better choice for constrained rewriting tasks (4 vs 3) and multi-step agentic workflows (3 vs 2, though both are weak). The reasoning-token support and accessible thinking traces make it useful for debugging logic-heavy pipelines. Developers willing to pay $0.50/M output tokens for a more reliable agent or chatbot foundation should default here.

Choose Llama 4 Scout if: cost efficiency at scale is the primary constraint, you need multimodal input (image+text, which Grok 3 Mini does not support per the payload), or your use case demands a 327,680-token context window that exceeds Grok 3 Mini's 131,072-token limit. Llama 4 Scout also holds its own on classification, long context, structured output, and multilingual tasks — the areas where it ties Grok 3 Mini — so if your workload concentrates there and you're processing hundreds of millions of tokens monthly, the $200/month savings at 100M output tokens is real.