Grok 4 vs Llama 4 Scout

Across our 12-test benchmark suite, Grok 4 wins 6 tests, Llama 4 Scout wins none, and they tie on 6. Neither model has been through our full average scoring, so these individual test scores are the primary signal.

Where Grok 4 wins clearly:

• Strategic analysis: 5 vs 2 — the largest gap in the comparison. Grok 4 ties for 1st among 54 models (with 25 others); Scout ranks 44th of 54. This test covers nuanced tradeoff reasoning with real numbers — a meaningful advantage for business analysis, policy evaluation, or any task requiring structured reasoning about competing factors.
• Agentic planning: 3 vs 2. Grok 4 ranks 42nd of 54; Scout ranks 53rd of 54 (near-bottom). Goal decomposition and failure recovery are critical for multi-step AI workflows. Scout's score here is a real limitation for autonomous agent use cases.
• Faithfulness: 5 vs 4. Grok 4 ties for 1st among 55 models; Scout ranks 34th. Faithfulness measures whether a model sticks to source material without hallucinating. For RAG pipelines, document QA, or any task where grounding matters, this gap is operationally significant.
• Persona consistency: 5 vs 3. Grok 4 ties for 1st among 53 models; Scout ranks 45th. Relevant for chatbots, roleplay applications, and any deployment where the model must maintain a defined identity and resist prompt injection.
• Constrained rewriting: 4 vs 3. Grok 4 ranks 6th of 53; Scout ranks 31st. Compression within hard character limits — useful for ad copy, SEO snippets, push notifications.
• Multilingual: 5 vs 4. Grok 4 ties for 1st among 55 models; Scout ranks 36th. Non-English use cases favor Grok 4.

Where they tie:

• Structured output: Both score 4/5, both rank 26th of 54. JSON schema compliance is equivalent — no edge for either on API integrations requiring strict formatting.
• Tool calling: Both score 4/5, both rank 18th of 54. Function selection and argument accuracy are matched.
• Classification: Both score 4/5, both tied for 1st among 53 models. Routing and categorization tasks can use either model without compromise.
• Long context: Both score 5/5, both tied for 1st among 55 models. Both handle 30K+ token retrieval at maximum performance. Note that Llama 4 Scout has a slightly larger context window (327,680 tokens vs Grok 4's 256,000 tokens).
• Creative problem solving: Both score 3/5, both rank 30th of 54. Neither model stands out for generating non-obvious ideas.
• Safety calibration: Both score 2/5, both rank 12th of 55. Both fall below the median on refusing harmful requests while permitting legitimate ones — worth noting for safety-critical deployments.

External benchmark note: The data payload does not include external benchmark scores (SWE-bench Verified, AIME 2025, MATH Level 5) for either model in this comparison, so we cannot reference third-party performance data here.

The pricing gap here is dramatic. Grok 4 costs $3.00/M input and $15.00/M output tokens. Llama 4 Scout costs $0.08/M input and $0.30/M output tokens. At 1M output tokens/month, you're paying $15 for Grok 4 versus $0.30 for Scout — a $14.70 difference that's nearly trivial. At 10M output tokens/month, that gap becomes $147. At 100M output tokens/month, you're looking at $15,000 versus $300 — a $14,700 monthly difference that changes the business case entirely. Developers running high-throughput pipelines — content classification, summarization at scale, translation — should seriously evaluate whether Grok 4's benchmark advantages are worth a 50x cost multiplier. For low-volume, high-stakes tasks like complex analysis, document review, or agentic workflows where quality failures are expensive, Grok 4's edge on strategic analysis (5 vs 2) and agentic planning (3 vs 2) may justify the spend. For anyone processing tens of millions of tokens monthly, Scout is the rational default unless specific quality requirements demand otherwise.

Choose Grok 4 if: You need reliable performance on strategic analysis, agentic workflows, faithfulness to source material, or multilingual output — and your token volumes are low enough that the 50x price premium is manageable. Grok 4's agentic planning score (3 vs Scout's 2, ranking 53rd of 54) makes Scout a risky choice for autonomous pipelines. Grok 4 also supports reasoning tokens, structured outputs, tool calling, and accepts image and file inputs — useful for multimodal or complex document workflows.

Choose Llama 4 Scout if: You're running high-volume workloads — classification, summarization, translation, or structured data extraction — where the benchmark parity on tool calling (4/5), structured output (4/5), and classification (tied for 1st) is sufficient. At $0.30/M output tokens versus $15.00/M, Scout costs 50x less, and for tasks where both models score identically, paying the premium is hard to justify. Scout's larger context window (327,680 vs 256,000 tokens) is also a minor advantage for very long document tasks. Be aware that Scout's agentic planning and strategic analysis scores are near the bottom of our tested models, so avoid it for complex reasoning pipelines.