Grok 4.20 vs Llama 4 Scout
In our testing Grok 4.20 is the better pick for product-grade agents and structured, faithful outputs — it wins 9 of 12 benchmarks. Llama 4 Scout wins safety calibration and is the clear cost-efficient choice for high-volume deployments ($0.08 input / $0.30 output vs Grok's $2 / $6).
xai
Grok 4.20
Benchmark Scores
External Benchmarks
Pricing
Input
$2.00/MTok
Output
$6.00/MTok
modelpicker.net
meta-llama
Llama 4 Scout
Benchmark Scores
External Benchmarks
Pricing
Input
$0.080/MTok
Output
$0.300/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite Grok 4.20 wins 9 tests, Llama 4 Scout wins 1, and 2 tie. Where Grok wins: structured output (5 vs 4) — Grok is tied for 1st (tied with 24 others out of 54), so expect stronger JSON/schema adherence in production; strategic analysis (5 vs 2) — Grok ranks tied for 1st of 54, meaning better nuanced tradeoff reasoning with numbers; constrained rewriting (4 vs 3) — Grok ranks 6 of 53 (25 models share this score), useful for strict character-limited transformations; creative problem solving (4 vs 3) — Grok ranks 9 of 54, giving more specific feasible ideas; tool calling (5 vs 4) — Grok tied for 1st with 16 others out of 54, which matters for function selection, argument accuracy and sequencing; faithfulness (5 vs 4) — Grok tied for 1st with 32 others out of 55, so lower hallucination risk in our tests; persona consistency (5 vs 3) and classification (tie at 4) — Grok tied for 1st in persona and classification (classification is tied for 1st with 29 others for Llama as well); agentic planning (4 vs 2) — Grok ranks 16 of 54, better at goal decomposition and failure recovery; multilingual (5 vs 4) — Grok tied for 1st with 34 others. Llama 4 Scout wins safety calibration (2 vs 1) and ranks 12 of 55 for safety calibration (tied with 19 others), meaning it better balances refusing harmful requests while permitting legitimate ones in our testing. Both models tie on long context (5 vs 5) and rank tied for 1st with many models, so retrieval accuracy at 30K+ tokens appears equivalent in our suite. In short: Grok shows clear advantages for structured outputs, agentic/tool workflows, faithfulness and complex analysis; Llama’s single measurable win is safety calibration plus a far lower cost per token.
Pricing Analysis
Grok 4.20 input: $2/mTOK, output: $6/mTOK. Llama 4 Scout input: $0.08/mTOK, output: $0.30/mTOK. Using a 50/50 input/output split: for 1M tokens (1,000 mTOK → 500 mTOK in / 500 mTOK out) Grok costs $4,000 (500*$2 + 500*$6) vs Llama $190 (500*$0.08 + 500*$0.30). For 10M tokens that’s $40,000 vs $1,900; for 100M tokens it’s $400,000 vs $19,000. The ~20× priceRatio from the payload means startups and high-volume apps should prefer Llama 4 Scout when cost per token dominates; product teams building agentic workflows, tool-driven pipelines, or strict-schema outputs may justify Grok’s higher spend for its quality wins.
Real-World Cost Comparison
Bottom Line
Choose Grok 4.20 if you need agentic tool calling, strict JSON/schema compliance, lower hallucination risk, or better strategic and planning outputs and can absorb higher inference costs. Choose Llama 4 Scout if budget and scale matter — it costs $0.08/mTOK in and $0.30/mTOK out (vs Grok’s $2/$6) and wins safety calibration while matching Grok on long-context retrieval.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.