Gemini 2.0 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 2.0 Flash and Llama 4 Scout.
Gemini 2.0 Flash
Gemini 2.0 Flash is Google's previous-generation fast, cost-efficient multimodal model, offering a compelling balance of speed, capability, and price. It supports text, image, and audio inputs with native multimodal understanding, making it well-suited for high-volume classification, real-time content moderation, and data extraction pipelines. Gemini 2.0 Flash introduced Google's context caching feature, significantly reducing costs for repeated document processing. While the 3.x series has since succeeded it, Gemini 2.0 Flash remains a popular cost-optimized choice for teams with established Vertex AI 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 | Gemini 2.0 Flash | Llama 4 Scout |
|---|---|---|
| Provider | Meta | |
| Context Window | 1,048,576 tokens | 10,000,000 tokens |
| Agent Suitability | 80/100 | 82/100 |
| Time to First Token (TTFT) | 180 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2025-02-05 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 2.0 Flash
$0.10
Llama 4 Scout
$0.10
Output Price per Million Tokens
Gemini 2.0 Flash
$0.40
Llama 4 Scout
$0.30
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
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 2.0 Flash Quirks & Gotchas
- ▸Context caching via Vertex AI — up to 75% cost reduction for repeated prompts
- ▸Legacy model — migrate to Gemini 3.1 Flash for improved accuracy
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling