Model Selection Guide
Determine which large language model to use
Which Model Should I Use?
What to Consider
Choosing a model depends on the following:
Context Window: the context window refers to the number of tokens you can provide to a LLM. ~1 Token = ~4 characters
Task Complexity: more capable models are generally better suited for complex logic.
Web Access: whether the use case you're building require the model to have web access?
Cost: more capable models are generally more expensive - for example, o1 is more expensive than GPT-4o.
Speed: more capable models are generally slower to execute.
AirOps Popular LLMs
GPT-5
OpenAI
Flagship model for complex tasks
400K
✓
✓
✓
GPT-4.1
OpenAI
For complex tasks, vision-capable
1M
✓
✓
-
GPT-4o Search Preview
OpenAI
Flagship model for online web research
128K
✓
✓
✓
O4 Mini
OpenAI
Fast multi-step reasoning for complex tasks
128K
-
✓
-
O3
OpenAI
Advanced reasoning for complex tasks
128K
-
✓
-
O3 Mini
OpenAI
Fast multi-step reasoning for complex tasks
128K
-
✓
-
Claude Opus 4.1
Anthropic
Powerful model for complex and writing tasks
200K
✓
-
-
Claude Sonnet 4
Anthropic
Hybrid reasoning: fast answers or deep thinking
200K
✓
-
-
Gemini 2.5 Pro
Advanced reasoning for complex tasks
1M
✓
✓
✓
Gemini 2.5 Flash
Fast and intelligent model for lightweight tasks
1M
✓
✓
✓
Perplexity Sonar
Perplexity
Balanced model for online web research
128K
-
✓
✓
Differences between “o-series” vs “GPT” models
GPT-5 Series: Built-In Reasoning
GPT-5 Models: OpenAI's first model series to combine reasoning paradigm with traditional LLM capabilities. Features reasoning levels of minimal, low, medium, high that control how much reasoning the model performs.
O-series Models (o3, o4-mini): Pure Reasoning Specialists
Specialized exclusively for deep reasoning and step-by-step problem solving. These models excel at complex, multi-stage tasks requiring logical thinking and tool use. Choose these when maximum accuracy and reasoning depth are paramount. Features reasoning levels of low, medium, high for controlling reasoning token usage.
GPT Models (4.1, 4o): Traditional General-Purpose
Optimized for general-purpose tasks with excellent instruction following. GPT-4.1 excels with long contexts (1M tokens) while GPT-4o has variants for realtime speech, text-to-speech, and speech-to-text. GPT-4.1 also comes in mini and nano variants, while GPT-4o has a mini variant. These variants are cheaper and faster than their full-size counterparts. Strong in structured output generation.
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*
Last updated
Was this helpful?