Grok 4.1 Fast vs Llama 4 Scout

Neither model has been through our full 12-test benchmark suite with an average score, so the comparison is score-by-score rather than aggregate. Grok 4.1 Fast wins 8 of 12 tests in our testing; Llama 4 Scout wins 1; they tie on 3.

Where Grok 4.1 Fast leads decisively:

• Strategic analysis: 5 vs 2. Grok 4.1 Fast ties for 1st among 54 models; Llama 4 Scout ranks 44th of 54. This is the widest gap in the comparison. For tasks requiring nuanced tradeoff reasoning with real numbers — investment decisions, competitive analysis, scenario planning — Llama 4 Scout is a significant step down.
• Agentic planning: 4 vs 2. Grok 4.1 Fast ranks 16th of 54; Llama 4 Scout ranks 53rd of 54, nearly last. Goal decomposition and failure recovery are near-bottom for Scout, which limits its usefulness in multi-step automated workflows.
• Persona consistency: 5 vs 3. Grok 4.1 Fast ties for 1st of 53; Llama 4 Scout ranks 45th of 53. Maintaining character and resisting prompt injection is a clear Grok 4.1 Fast strength — relevant for chatbots, roleplay, and branded AI products.
• Faithfulness: 5 vs 4. Grok 4.1 Fast ties for 1st of 55; Scout ranks 34th of 55. Sticking to source material without hallucinating is better on Grok 4.1 Fast, which matters for RAG pipelines and document-grounded tasks.
• Multilingual: 5 vs 4. Both perform well — the median model scores 5 — but Grok 4.1 Fast ties for 1st of 55 while Scout ranks 36th of 55.
• Structured output: 5 vs 4. Grok 4.1 Fast ties for 1st of 54; Scout ranks 26th of 54. JSON schema compliance is strong on both, but Grok 4.1 Fast is more reliable.
• Constrained rewriting: 4 vs 3. Grok 4.1 Fast ranks 6th of 53; Scout ranks 31st of 53. Compression within hard character limits favors Grok 4.1 Fast.
• Creative problem solving: 4 vs 3. Grok 4.1 Fast ranks 9th of 54; Scout ranks 30th of 54.

Where they tie:

• Tool calling: Both score 4, both rank 18th of 54 (29 models share this score). Function selection and argument accuracy are equivalent — neither has an edge here.
• Classification: Both score 4, both tie for 1st of 53 (30 models share this score). Accurate categorization is a wash.
• Long context: Both score 5, both tie for 1st of 55 (37 models share this score). Retrieval accuracy at 30K+ tokens is equal — though Grok 4.1 Fast's 2,000,000-token context window dwarfs Llama 4 Scout's 327,680 tokens, which could matter for extreme-length documents even if both ace the benchmark test.

Where Llama 4 Scout wins:

• Safety calibration: 2 vs 1. Scout ranks 12th of 55; Grok 4.1 Fast ranks 32nd of 55. Scout is better calibrated at refusing harmful requests while permitting legitimate ones — relevant for consumer-facing products with broad user bases. Neither score is excellent by absolute measure; the median model scores 2 on this test.

Grok 4.1 Fast costs $0.20 per million input tokens and $0.50 per million output tokens. Llama 4 Scout costs $0.08 input and $0.30 output — 2.5x cheaper on input and roughly 1.67x cheaper on output. In practice, for output-heavy workloads (where cost is dominated by generated tokens), the gap is real but not extreme:

• At 1M output tokens/month: Grok 4.1 Fast costs $0.50 vs Llama 4 Scout's $0.30 — a $0.20 difference.
• At 10M output tokens/month: $5.00 vs $3.00 — you're saving $2.00 with Scout.
• At 100M output tokens/month: $50 vs $30 — the $20/month gap starts to matter for cost-sensitive operations.

For most developers running moderate volumes, the $20/month difference at 100M tokens is unlikely to drive model selection on its own. Where the cost calculus matters most is high-throughput consumer applications or internal tools generating hundreds of millions of tokens monthly. In those cases, if Llama 4 Scout's lower scores on agentic planning and strategic analysis are acceptable for the task, it delivers real savings. But if task quality directly affects user outcomes or business results, the performance gap from our benchmarks makes Grok 4.1 Fast's premium defensible.

Choose Grok 4.1 Fast if your application involves multi-step agentic workflows (its agentic planning score of 4 vs Scout's 2, ranked 16th vs near-last), complex strategic or analytical tasks, maintaining a consistent AI persona, or you need the full 2,000,000-token context window. It's also the better pick for RAG and document-grounded tasks given its higher faithfulness score. The $0.20/$0.50 per million token pricing is a reasonable premium for these capabilities in production.

Choose Llama 4 Scout if your use case is limited to classification, tool calling, or long-context retrieval (where both models perform equally well in our testing), and you need to minimize output costs — $0.30/M output tokens vs $0.50/M. Scout also wins on safety calibration, making it worth considering for consumer-facing products where over-refusal risks are a concern. Avoid Scout for agentic, analytical, or persona-driven applications where its benchmark scores drop sharply.