LLM

Google Gemini LLM for Bolna AI voice agents

Google Gemini runs the conversation in a Bolna voice agent with a 1M token context and strong multilingual skills. gemini-2.5-flash is the recommended default.

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Official Documentation

At a glance

How Google Gemini fits in the stack

Best for

Reasoning, policy handling, tool use, and response generation.

Use this layer when

You want to tune quality, cost, latency, or function-calling behavior.

Connects to

Transcribed caller input upstream and voice or actions downstream.

Voice Stack

The LLM is the reasoning layer

This provider decides how the agent interprets intent, selects next actions, calls tools, and formulates responses. It is the decision engine inside the voice stack.

TelephonyPhone Network

→

STTListener

→

LLMReasoning

→

TTSVoice

→

ToolsActions

Last updated 25 October 2026

What is Google Gemini used for in a Bolna voice agent?

Google Gemini is an LLM option for a Bolna voice agent with large context windows (up to 1M tokens) and strong multilingual capability, and gemini-2.5-flash is the stable model the Bolna docs recommend for most agents. Pick it for multilingual agents, including Indian languages, or for agents that need a lot of context. The Gemini 3.x series is the newer generation with higher capability.

How do I set up Google Gemini with Bolna?

To use your own key, connect it at platform.bolna.ai/auth/google.
Open your agent and go to the Intelligence tab.
Set the provider to google and the model to gemini-2.5-flash.
Keep the token limit around 150 and the temperature around 0.2. For gemini-3.7-flash, raise max_tokens to 400 or more.
Set the language explicitly in your prompt, since auto-detection adds latency.

The Google Gemini setup guide in the Bolna docs has the JSON config.

Which Gemini models does Bolna support?

Model	Context	Best for	Notes
`gemini-3.7-flash`	1M tokens	Hardest reasoning, agent tasks	Newest generation. Needs `max_tokens` 400+
`gemini-3.5-flash`	1M tokens	Complex reasoning, agent tasks	Stable
`gemini-3.1-pro`	1M tokens	Complex tasks, highest quality	Gemini 3 premium model
`gemini-3.1-flash-lite`	1M tokens	Budget, high-volume agents	Fastest in the Gemini 3 family
`gemini-2.5-pro`	1M tokens	Advanced reasoning	Gemini 2.5 premium; stable
`gemini-2.5-flash`	1M tokens	Most production voice agents	Recommended
`gemini-2.5-flash-lite`	1M tokens	Cost-sensitive high volume	Cheapest in the 2.5 family

What can you build with Google Gemini and Bolna?

Indian language agents. Run Hindi, Tamil or Bengali conversations on Gemini when you need broader LLM capability alongside multilingual handling. Pair it with an Indian language voice such as Sarvam TTS.

Agents with long context. The 1M token window suits agents that carry long instructions or a lot of reference material in the prompt.

High-volume outbound calling. Use gemini-2.5-flash-lite or gemini-3.1-flash-lite for cost-sensitive campaigns. Gemini is not on Bolna's preferred model list, so check pricing for how the LLM part of the call is billed.

Model comparisons with GPT. The Bolna docs rate Gemini and GPT as comparable on latency, with Gemini ahead on multilingual and large-context work and GPT-5.4-mini slightly ahead on English instruction following. Test both, starting from the OpenAI integration.

FAQ

Google Gemini on Bolna: frequently asked questions

Use gemini-2.5-flash for proven production stability. Try gemini-3.5-flash or gemini-3.1-flash-lite for newer capability, and switch once you've validated quality on your agent.

Gemini 3.7 Flash always thinks before it answers, and that thinking uses part of the max_tokens budget. Set max_tokens to 400 or higher for this model.

Yes. Connect it at platform.bolna.ai/auth/google. API costs are then charged to your Google account.

Yes. The Bolna docs call Gemini a good alternative to Sarvam for Hindi, Tamil, Bengali and other Indian languages when you need broader LLM capability. Set the language explicitly in your prompt.

Other providers in this layer

LLM

Anthropic

Anthropic Claude writes your Bolna voice agent's replies. Use claude-sonnet-5 for most agents, or claude-haiku-4-5 for faster, high-volume calls.

LLM

Azure OpenAI

Azure OpenAI runs GPT models for your Bolna voice agent inside your own Azure account, for teams that need regional data residency and private networking.

LLM

DeepSeek

DeepSeek V4 runs the conversation in a Bolna voice agent at low cost per token. deepseek-v4-flash suits high-volume calls, with a 1M token context window.

Browse this layer

Keep exploring the voice stack

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Large Language Models

The LLM is the brain of your voice agent. It understands what callers say and decides how to respond. Bolna lets you switch between models like GPT-5.4 mini, Claude and DeepSeek by changing the provider and model in your agent LLM settings, so you can tune for speed, cost or reasoning depth.

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Telephony

Telephony providers connect your voice agents to the phone network so they can make and receive real calls. Bolna supports managed integrations with major carriers as well as bring-your-own-carrier via SIP trunking, giving you full control over call routing, number provisioning, and cost.

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Speech-to-Text

Speech-to-text converts what callers say into text that your LLM can process. Transcription accuracy and latency directly affect how natural a conversation feels. Bolna supports streaming STT providers optimized for telephony audio, including specialized models for Indian languages.

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Text-to-Speech

Text-to-speech turns your agent responses into spoken audio. Voice quality shapes how callers perceive your brand. Flat, robotic speech kills trust while natural, expressive voices build it. Bolna integrates with the fastest TTS providers so responses sound human and arrive without awkward pauses.

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Tools & Workflows

Tools let your voice agents take action during a call, not just talk. Book a calendar slot, look up an order through your own API, push a lead into your CRM, or trigger a multi-step automation in Zapier. These integrations turn voice agents from answering machines into workflow engines.

See where Google Gemini fits in your production workflow

Use the demo to walk through provider selection, stack tradeoffs, and the exact workflow you want Bolna to automate.

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