Grok 4.20 vs Llama 4 Scout
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Grok 4.20 and Llama 4 Scout.
Grok 4.20
Grok 4.20 is xAI's most advanced reasoning model, combining powerful analytical capabilities with unique real-time integration to the X (formerly Twitter) platform data stream. This live data access allows Grok 4.20 to answer queries with up-to-the-minute context, making it invaluable for financial analysis, current events research, and real-time market monitoring. Exceptionally strong in physics, advanced mathematics, and code synthesis, it operates with a 2-million-token context window and is priced at $1.25/MTok for input — delivering frontier-tier reasoning at a highly competitive cost for developers building intelligent agents and analytical tools.
Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Technical Specifications
| Specification | Grok 4.20 | Llama 4 Scout |
|---|---|---|
| Provider | xAI | Meta |
| Context Window | 2,000,000 tokens | 10,000,000 tokens |
| Agent Suitability | 90/100 | 82/100 |
| Time to First Token (TTFT) | 350 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-05-05 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Grok 4.20
$1.25
Llama 4 Scout
$0.10
Output Price per Million Tokens
Grok 4.20
$2.50
Llama 4 Scout
$0.30
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Grok 4.20 Quirks & Gotchas
- ▸Real-time X data access — unparalleled for current events / financial analysis
- ▸Tool calling API is still maturing — test thoroughly for production agentic use
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling