This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.
Model details →OpenAI: GPT-3.5 Turbo Instruct vs Sao10K: Llama 3.1 70B Hanami x1
OpenAI: GPT-3.5 Turbo Instruct wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
This is [Sao10K](/sao10k)'s experiment over [Euryale v2.2](/sao10k/l3.1-euryale-70b).
Model details →Side-by-side comparison
| Capability | OpenAI: GPT-3.5 Turbo Instruct | Sao10K: Llama 3.1 70B Hanami x1 | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 4K | 16K | 🏆 Sao10K: Llama 3.1 70B Hanami x1 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $1.50 | $3.00 | 🏆 OpenAI: GPT-3.5 Turbo Instruct |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $2.00 | $3.00 | 🏆 OpenAI: GPT-3.5 Turbo Instruct |
| Tool / function calling First-class support for emitting structured tool calls. | — | — | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | — | Tie |
Frequently asked questions
Is OpenAI: GPT-3.5 Turbo Instruct better than Sao10K: Llama 3.1 70B Hanami x1?
OpenAI: GPT-3.5 Turbo Instruct wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between OpenAI: GPT-3.5 Turbo Instruct and Sao10K: Llama 3.1 70B Hanami x1?
Input: $1.50 vs $3.00 per 1M tokens. Output: $2.00 vs $3.00 per 1M tokens.
What context windows do OpenAI: GPT-3.5 Turbo Instruct and Sao10K: Llama 3.1 70B Hanami x1 support?
OpenAI: GPT-3.5 Turbo Instruct supports up to 4K tokens. Sao10K: Llama 3.1 70B Hanami x1 supports up to 16K tokens.
Do both OpenAI: GPT-3.5 Turbo Instruct and Sao10K: Llama 3.1 70B Hanami x1 support tool calling?
OpenAI: GPT-3.5 Turbo Instruct: not advertised. Sao10K: Llama 3.1 70B Hanami x1: not advertised.