Llama 4 Scout vs davinci-002
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Llama 4 Scout | davinci-002 |
|---|---|---|
| Provider | Meta | OpenAI |
| Input Price (1M) | $0.10 | $2.00 |
| Output Price (1M) | $0.30 | $2.00 |
| Context Window | 10,000,000 | 16,384 |
Verdict
Llama 4 Scout costs $0.10 per 1M input tokens and $0.30 per 1M output tokens. davinci-002 costs $2.00 per 1M input tokens and $2.00 per 1M output tokens. Llama 4 Scout is 95% cheaper on input tokens than davinci-002. For output tokens, Llama 4 Scout is the more affordable option at $0.30/1M vs $2.00.
On context window, Llama 4 Scout supports 10,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Llama 4 Scout
- ✓ You need the lowest input token cost ($ 0.10/1M)
- ✓ Your workload is output-heavy — Llama 4 Scout generates text cheaper
- ✓ You need a larger context window (10,000,000 tokens)
- ✓ You are already integrated with Meta
When to choose davinci-002
- ✓ You are already integrated with OpenAI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Llama 4 Scout cheaper than davinci-002? ▼
Llama 4 Scout is cheaper on input tokens at $0.10/1M vs $2.00/1M for davinci-002 — a 95% saving.
What is the context window of Llama 4 Scout vs davinci-002? ▼
Llama 4 Scout has a 10,000,000-token context window. davinci-002 has a 16,384-token context window. Llama 4 Scout supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Llama 4 Scout or davinci-002? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Llama 4 Scout is the cheaper option. For maximum context length, Llama 4 Scout supports 10,000,000 tokens. Use the comparison table above to find the right fit for your workload.