Choosing a Model

Determine which large language model to use

Which Model Should I Use?

What to Consider

Choosing a model depends on the following:

1. Context Window: the context window refers to the number of tokens you can provide to a LLM. ~1 Token = ~4 characters

2. Task Complexity: more capable models are generally better suited for complex logic.

3. Cost: more capable models are generally more expensive - for example, o1 is more expensive than GPT-4o.

4. Speed: more capable models are generally slower to execute.

AirOps Popular LLMs

Model	Provider	In-App Description	Context Window	Vision	JSON Mode	Web Research
O1	OpenAI	Advanced multi-step reasoning for complex tasks	200K	-	-	-
O3 Mini	OpenAI	Small reasoning model optimized for complex tasks	200K	-	-	-
GPT-4o	OpenAI	Flagship for complex tasks, vision-capable	128K	✓	✓	-
GPT-4o Mini	OpenAI	Fast and intelligent model for lightweight tasks	128K	✓	✓	-
Claude 3.5 Sonnet	Anthropic	Flagship intelligent model for complex tasks	200K	✓	-	-
Perplexity Sonar	Perplexity	Intelligent model for online web research	128K	-	-	✓
Gemini Pro 2.0	Google	Flagship for complex tasks, vision-capable	2M	-	✓	✓

How much will it cost to run?

The cost to run a model depends on the number of input and output tokens.

Token Approximation

Input tokens: to approximate the total input tokens, copy and paste your system, user, and assistant prompts into the OpenAI tokenizer

Output tokens: to approximate the total output tokens, copy and paste your output into the OpenAI tokenizer

Cost Approximation

OpenAI: divide the input and output tokens by 1000; then multiply by their respective costs based on OpenAI pricing*

Anthropic: divide the input and output tokens by 1,000,000; then multiply by their respective costs based on Anthropic pricing*

*This is the cost if you bring your own API Key. If you choose to use AirOps hosted models, you will be charged tasks according to your usage.