Llama 3.3 70B Instruct 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 Llama 3.3 70B Instruct and Llama 4 Scout.
Llama 3.3 70B Instruct
Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.
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 | Llama 3.3 70B Instruct | Llama 4 Scout |
|---|---|---|
| Provider | Meta | Meta |
| Context Window | 131,072 tokens | 10,000,000 tokens |
| Agent Suitability | 83/100 | 82/100 |
| Time to First Token (TTFT) | 280 ms | 350 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2024-12-06 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.3 70B Instruct
$0.10
Llama 4 Scout
$0.10
Output Price per Million Tokens
Llama 3.3 70B Instruct
$0.32
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.
Llama 3.3 70B Instruct Quirks & Gotchas
- ▸Stable, well-documented self-hosted option with strong community support
- ▸Outperformed by Llama 4 Maverick for agentic tool-calling workflows
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling