Llama 3.1 405B vs o1
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 3.1 405B and o1.
Llama 3.1 405B
Llama 3.1 405B is Meta's largest open-weight language model and one of the most capable openly available models in the world. With 405 billion parameters, it achieves performance competitive with GPT-4 and Claude Opus across benchmarks spanning general knowledge, mathematics, coding, and multilingual tasks. Llama 3.1 405B is released under Meta's custom commercial license, supporting broad use cases including deployment via major cloud providers (AWS, GCP, Azure) and self-hosted inference with multi-GPU configurations.
o1
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...
Technical Specifications
| Specification | Llama 3.1 405B | o1 |
|---|---|---|
| Provider | Meta | OpenAI |
| Context Window | 131,072 tokens | 200,000 tokens |
| Agent Suitability | 90/100 | 88/100 |
| Time to First Token (TTFT) | 550 ms | 2500 ms |
| Deployment Model | self hostable | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-23 | 2024-12-17 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.1 405B
$0.80
o1
$15.00
Output Price per Million Tokens
Llama 3.1 405B
$0.80
o1
$60.00
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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 3.1 405B Quirks & Gotchas
- ▸Massive model — requires 8× A100 80GB for FP16 inference
- ▸Available via Together AI, Fireworks, and Bedrock as managed API
o1 Quirks & Gotchas
- ▸Reasoning model — high latency by design, not for real-time use
- ▸Best for complex math/code reasoning where accuracy > speed
- ▸Use o3-mini when you need reasoning with lower latency