GPT-4o-mini 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 GPT-4o-mini and Llama 3.1 8B.
GPT-4o-mini
GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...
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 | GPT-4o-mini | Llama 3.1 8B |
|---|---|---|
| Provider | OpenAI | Meta |
| Context Window | 128,000 tokens | 131,072 tokens |
| Agent Suitability | 82/100 | 74/100 |
| Time to First Token (TTFT) | 150 ms | 80 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-18 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
GPT-4o-mini
$0.15
Llama 3.1 8B
$0.04
Output Price per Million Tokens
GPT-4o-mini
$0.60
Llama 3.1 8B
$0.04
Want to test both models live?
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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.
GPT-4o-mini Quirks & Gotchas
- ▸Ultra-low latency — best TTFT in the OpenAI lineup
- ▸Tool calling limited to single-step — not suitable for complex agentic pipelines
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