Grok Code Fast 1 vs Llama 4 Scout

Across our 12-test benchmark suite, Grok Code Fast 1 wins on 3 tests, Llama 4 Scout wins on 1, and they tie on the remaining 8.

Where Grok Code Fast 1 leads:

• Agentic planning (5 vs 2): This is the most decisive gap in the comparison. Grok Code Fast 1 ties for 1st among 54 tested models (shared with 14 others), while Llama 4 Scout ranks 53rd of 54 — near the bottom of the field. Agentic planning tests goal decomposition and failure recovery, which are essential for autonomous coding agents, multi-step API orchestration, and any workflow where the model needs to self-correct.

• Strategic analysis (3 vs 2): Grok Code Fast 1 scores 3/5, ranking 36th of 54. Llama 4 Scout scores 2/5, ranking 44th of 54. Neither model shines here — both sit below the median of 4/5 — but the gap is real for nuanced tradeoff reasoning tasks.

• Persona consistency (4 vs 3): Grok Code Fast 1 scores 4/5, ranking 38th of 53 (shared with 6 others). Llama 4 Scout scores 3/5, ranking 45th of 53. This matters for chatbot and roleplay deployments where the model must maintain character under adversarial prompts.

Where Llama 4 Scout leads:

• Long context (5 vs 4): Llama 4 Scout ties for 1st among 55 tested models (shared with 36 others) with a 5/5. Grok Code Fast 1 scores 4/5 but ranks 38th of 55 — a meaningful difference in retrieval accuracy at 30K+ tokens. Scout also has a larger context window (327,680 tokens vs 256,000) and higher max output tokens (16,384 vs 10,000), reinforcing its suitability for long-document tasks.

Where they tie (8 of 12 tests):

Both models score identically — and share identical rankings — on structured output (4/5, rank 26 of 54), tool calling (4/5, rank 18 of 54), constrained rewriting (3/5, rank 31 of 53), creative problem solving (3/5, rank 30 of 54), faithfulness (4/5, rank 34 of 55), classification (4/5, tied 1st of 53), safety calibration (2/5, rank 12 of 55), and multilingual (4/5, rank 36 of 55). The tied safety calibration score of 2/5 is worth noting — both models score below the 75th percentile, which sits at 2/5, indicating the broader model pool also struggles here, but developers with strict safety requirements should be aware.

Neither model has external benchmark scores (SWE-bench Verified, AIME 2025, MATH Level 5) in our data payload, so internal scores are the primary basis for this comparison.

Llama 4 Scout costs $0.08 per million input tokens and $0.30 per million output tokens. Grok Code Fast 1 costs $0.20 input and $1.50 output — a 2.5× input gap and a 5× output gap. In practice, output costs dominate most workloads. At 1M output tokens/month, Scout costs $0.30 versus $1.50 for Grok Code Fast 1 — a $1.20 difference that's negligible. At 10M output tokens, that's $3 vs $15 — still manageable. At 100M output tokens/month, the gap becomes $30 vs $150, a $120/month delta that starts to matter for cost-sensitive applications. The 5× output price ratio means developers running high-volume pipelines — content generation, classification at scale, document processing — should seriously evaluate whether Grok Code Fast 1's benchmark advantages justify the added cost. For agentic coding applications where output volume is moderate but task quality is critical, the premium is more defensible.

Choose Grok Code Fast 1 if: You're building agentic coding workflows, autonomous agents, or any application requiring multi-step planning and failure recovery — its 5/5 agentic planning score versus Scout's 2/5 is the clearest differentiator in this matchup. It also suits deployments needing stronger persona consistency (chatbots, roleplay) or slightly better strategic reasoning, and where output volume is moderate enough that the $1.50/M output cost is acceptable. The visible reasoning traces (via the include_reasoning parameter) are an added benefit for developers who want to audit or steer model behavior.

Choose Llama 4 Scout if: Your use case is long-document processing, RAG over large corpora, or any task that stresses retrieval at 30K+ tokens — Scout's 5/5 long-context score and 327K context window edge out Grok Code Fast 1 here. Scout is also the right call for high-volume pipelines where the 5× output cost difference ($0.30 vs $1.50 per million tokens) compounds significantly. For tasks where both models tie — classification, tool calling, structured output, faithfulness — Scout delivers equivalent results at a fraction of the price. Scout also supports image input (text+image→text modality), which Grok Code Fast 1 does not, making it the only option for multimodal workloads based on available payload data.