Gemini 3.5 Flash vs Llama 3.1 8B
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 3.5 Flash and Llama 3.1 8B.
Gemini 3.5 Flash
Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...
Llama 3.1 8B
Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization — making it the default choice for on-device AI applications and local prototyping.
Technical Specifications
| Specification | Gemini 3.5 Flash | Llama 3.1 8B |
|---|---|---|
| Provider | Meta | |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 88/100 | 74/100 |
| Time to First Token (TTFT) | 200 ms | 80 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-05-19 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.5 Flash
$1.50
Llama 3.1 8B
$0.04
Output Price per Million Tokens
Gemini 3.5 Flash
$9.00
Llama 3.1 8B
$0.04
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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 3.5 Flash Quirks & Gotchas
- ▸Excellent multimodal performance — native video understanding
- ▸Tool calling via Google's native function_declarations in Vertex AI
Llama 3.1 8B Quirks & Gotchas
- ▸Perfect for CPU/edge deployment — runs on Raspberry Pi with quantization
- ▸Limited tool calling vs larger models — best for simple classification and chat