Model Selection Guide

Compare supported models and choose one for a Prompt LLM step

Choose a Model

The model you select affects output quality, speed, cost, and the features available in a Prompt LLM step. Use the model picker as the source of truth for availability in your workspace.

What to Consider

Compare these factors before selecting a model:

1. Context window: Check how much input the model can process. One token is roughly four characters in English.
2. Task complexity: Use stronger reasoning models for planning, analysis, and multi-step logic.
3. Web access: Confirm that the model supports web research when the task needs current information.
4. Cost: Compare the model's cost tier in the picker, especially for high-volume Workflows.
5. Speed: Balance response time against the reasoning depth the task requires.

Common AirOps Models

The following models cover common use cases in new Workflows. A checkmark means the capability is available in the AirOps Prompt LLM step.

Model	Provider	Description	Context Window	Vision	JSON Mode	Web Access
GPT-5.6 Sol	OpenAI	Flagship GPT-5.6 model for complex reasoning and coding	1M	✓	✓	✓
GPT-5.6 Terra	OpenAI	Balanced GPT-5.6 model for strong performance at lower cost	1M	✓	✓	✓
GPT-5.6 Luna	OpenAI	Cost-efficient GPT-5.6 model for high-volume workloads	1M	✓	✓	✓
GPT-5.5	OpenAI	Previous flagship for complex reasoning tasks	1M	✓	✓	✓
GPT-5.4	OpenAI	Advanced model for professional work	1M	✓	✓	✓
GPT-5.4 Mini	OpenAI	Faster GPT-5.4 model for well-defined tasks	400K	✓	✓	✓
GPT-5.4 Nano	OpenAI	Cost-efficient GPT-5.4 model for lightweight tasks	400K	✓	✓	✓
GPT-5.2	OpenAI	Flagship with enhanced long-context reasoning	400K	✓	✓	✓
GPT-5.1	OpenAI	Model for coding and agentic tasks	200K	✓	✓	✓
GPT-5	OpenAI	Model for complex tasks	200K	✓	✓	✓
O3 Pro	OpenAI	Advanced model for complex reasoning	200K	✓	✓	-
O3	OpenAI	Reasoning model with web research	200K	✓	✓	✓
Claude Opus 5	Anthropic	For complex agentic coding and enterprise work	1M	✓	-	✓
Claude Fable 5	Anthropic	Demanding reasoning and long-horizon agentic work	1M	✓	-	✓
Claude Sonnet 5	Anthropic	Hybrid reasoning for fast answers or deep thinking	1M	✓	-	✓
Claude Opus 4.8	Anthropic	Previous flagship for complex multi-step tasks	1M	✓	-	✓
Claude Opus 4.7	Anthropic	Complex reasoning, coding, and long-context work	1M	✓	-	✓
Claude Opus 4.6	Anthropic	Complex reasoning and coding	200K	✓	-	✓
Claude Sonnet 4.6	Anthropic	Current Sonnet option for complex tasks	200K	✓	-	✓
Claude Haiku 4.5	Anthropic	Fast model for lightweight tasks	200K	✓	-	✓
Gemini 3.5 Flash	Google	Fast model with Google Search grounding	1M	-	✓	✓
Gemini 3.1 Pro Preview	Google	Advanced reasoning with Google Search grounding	1M	-	✓	✓
Gemini 3 Flash Preview	Google	Fast model for lightweight tasks	1M	-	✓	✓
Gemini 3.1 Flash Lite	Google	Cost-efficient model for lightweight tasks	1M	-	✓	✓
Perplexity Sonar	Perplexity	Balanced model for online web research	128K	-	-	✓

OpenAI Models

GPT-5 Series

GPT-5 models combine reasoning with general-purpose generation. GPT-5.6 appears in the model picker as Sol, Terra, and Luna. AirOps selects GPT-5.6 Sol by default for new Prompt LLM steps.

Reasoning controls vary by model:

• GPT-5 supports minimal, low, medium, and high.
• GPT-5.1, GPT-5.2, GPT-5.4, GPT-5.5, and GPT-5.6 support none, low, medium, and high.

O3 Models

Use O3 for complex, multi-stage reasoning. O3 supports low, medium, and high reasoning levels. O3 Pro increases reasoning capability but does not support web research in the Prompt LLM step.

Differences between Claude Models

Claude Opus 5

Claude Opus 5 handles complex agentic coding and enterprise work. Turn on Enable Thinking in the Prompt LLM step when the task benefits from additional reasoning. Claude Opus 5 supports Web Research, but it does not support Web Fetch.

Claude Fable 5

Claude Fable 5 supports demanding reasoning and long-horizon agentic work.

Claude Sonnet 5

Claude Sonnet 5 balances fast responses with deeper reasoning and is starred in the LLM model picker.

Claude Opus 4.8 and 4.7

Claude Opus 4.8 and 4.7 support complex, multi-step Workflows, long-form content, and research tasks. Both models support 1M-token context windows.

Claude Sonnet 4.6 and Haiku 4.5

Use Claude Sonnet 4.6 for complex tasks that need a balance of reasoning and speed. Use Claude Haiku 4.5 for lighter workloads.

Model Availability

AirOps hides deprecated models from the picker for new Workflows. If an existing Workflow uses a deprecated model, the selected model remains visible so you can review the change. AirOps may automatically update the selection to a supported model when the Workflow loads.

Review the selected model and test the Workflow after an automatic update. Model behavior, cost, and supported settings can change between versions.

• Claude Opus 4.1 is deprecated and scheduled for removal on August 5, 2026. Select Claude Opus 4.8 or another current model and test affected Workflows before the removal date.
• Gemini 2.5 Pro and Gemini 2.5 Flash are deprecated and scheduled for removal on October 16, 2026. Select a current model from the picker and test affected Workflows before the removal date.

Web Search Capabilities

Several models support web research, allowing them to access current information during generation:

OpenAI: GPT-5, GPT-5.1, GPT-5.2, GPT-5.4, GPT-5.4 Mini, GPT-5.4 Nano, GPT-5.5, GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna, and O3 support web research when enabled in the Prompt LLM step.

Anthropic: Current Claude models support web research. Web Fetch availability depends on the selected model. Claude Opus 5 supports Web Research, but not Web Fetch.

Google: Current Gemini models support web research through Google Search grounding.

Perplexity: Sonar models include web access for online research.

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.