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Model selection

Olly lets you pick a specific model for the chat, so you own the tradeoff between response speed, reasoning depth, and cost. The model list spans GPT, Claude, and Gemini, with recommended defaults that are tried-and-tested for everyday observability work.

Your selection persists across chats and sessions until you change it.

Available models

ModelProviderNotesBest forExample questions
GPT-5.2OpenAIDeeper reasoning for complex investigations"Investigate the root cause of intermittent 5xx errors."
GPT-5.4OpenAIRecommendedDay-to-day investigations and root-cause analysis"Why did latency spike after the last deployment?"
GPT-5.4 miniOpenAIRecommendedQuick lookups and simple data queries"List alerts fired in the last 10 minutes."
Claude Sonnet 4.5ClaudeRecommendedDay-to-day investigations and root-cause analysis"Compare today's API errors to last week."
Claude Sonnet 4.6ClaudeDeeper reasoning for complex investigations"Correlate error logs with recent infrastructure changes."
Claude Haiku 4.5ClaudeQuick lookups and simple data queries"What is the current CPU usage of node-3?"
Gemini 3.1 ProGoogleRecommendedDay-to-day investigations and root-cause analysis"What's the current error rate for the checkout service?"
Gemini 3 FlashGoogleQuick lookups and simple data queries"Show error rate for checkout service in the last hour."

Models marked Recommended are Olly's tried-and-tested defaults. In the UI, recommended models are highlighted with a Recommended tag next to the model name.

Select a model

  1. In the Olly chat input bar, select the model dropdown.
  2. Select a model from the list.

The selected model applies immediately and is used for all following prompts, across chats and sessions, until you change it.

You can switch models at any time during a conversation. Switching does not clear the conversation history.

How recommendations work

The Recommended tag marks the models Olly uses by default if you have not made a selection. Recommended models are the ones we have validated most thoroughly for observability workloads.

New models are added to the list as they become available. Existing selections are preserved when the list changes.

Persistence

Your model choice is saved per user, across chats and browser sessions. Other users in your team keep their own independent selections.

Why this matters

  • Control the tradeoff: pick speed, depth, or cost on a per-task basis.
  • Stay current: new model tiers are added to the list as they are released.
  • Honest expectations: the answer you get reflects the model you picked — the tradeoff is deliberate.

For details on how each provider handles your data, including infrastructure, data protection, and training policies, see Data processing, privacy, and compliance.

Next steps

Customize how Olly responds to you by defining personal preferences in user rules.