Llama 4 Scout vs MiniMax M2
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 4 Scout and MiniMax M2.
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...
MiniMax M2
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...
Technical Specifications
| Specification | Llama 4 Scout | MiniMax M2 |
|---|---|---|
| Provider | Meta | MiniMax |
| Context Window | 10,000,000 tokens | 204,800 tokens |
| Agent Suitability | 82/100 | N/A |
| Time to First Token (TTFT) | 350 ms | N/A |
| Deployment Model | self hostable | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2025-10-23 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
MiniMax M2
$0.26
Output Price per Million Tokens
Llama 4 Scout
$0.30
MiniMax M2
$1.02
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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 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling
MiniMax M2 Quirks & Gotchas
No developer gotchas reported.