Gemini 3.1 Flash 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 Gemini 3.1 Flash and Llama 4 Scout.
Gemini 3.1 Flash
Gemini 3.1 Flash is Google's high-speed, cost-efficient multimodal model in the 3.1 generation, purpose-built for high-volume content synthesis, classification, and intelligent routing at scale. Featuring a 1-million-token context window, it can process large batches of documents, customer data, or multimedia content in a single inference pass, dramatically reducing pipeline complexity. At just $0.25/MTok for input, it is one of the most affordable routes to Google-caliber multimodal AI, making it an ideal backbone for production pipelines, data enrichment workflows, and high-frequency API integrations.
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 | Gemini 3.1 Flash | Llama 4 Scout |
|---|---|---|
| Provider | Meta | |
| Context Window | 1,000,000 tokens | 10,000,000 tokens |
| Agent Suitability | 86/100 | 82/100 |
| Time to First Token (TTFT) | 150 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-04-20 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Flash
$0.25
Llama 4 Scout
$0.10
Output Price per Million Tokens
Gemini 3.1 Flash
$1.50
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.
Gemini 3.1 Flash Quirks & Gotchas
- ▸Most cost-effective Google model — ideal for high-volume pipelines
- ▸Context caching available via Vertex AI for repeated document processing
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling