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Lyria 3.5

Google (Gemini)

At a glance

Price per 1M tokens (input)
free
Price per 1M tokens (output)
free
Context
1,048,576 tokens
Free access
yes (see below)

Refreshed daily; data verified September 12, 2026. Published September 12, 2026.

Lyria 3.5 by Google is designed for developers and product teams building applications that require coherent, context-aware text generation. It supports tasks such as drafting documents, summarizing content, generating conversational responses, and assisting with code-related writing. What distinguishes Google's approach is the model's integration with the Gemini family's multimodal foundation, enabling consistent behavior across text-only and multimodal workflows while maintaining strong alignment with safety and usability principles.

Specifications & pricing

Input (per 1M tokens)free
Output (per 1M tokens)free
Context window1,048,576 tokens
Max output65,536 tokens
Capabilitiestext

LiteLLM community dataset (MIT), verified September 12, 2026. Official Google (Gemini) pricing.

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Frequently asked questions

What types of text generation tasks is Lyria 3.5 best suited for?+

Lyria 3.5 performs well in generating clear, structured text for business communication, content drafting, and summarization. It is also effective for creating responses in chat-based interfaces and supporting writing workflows where tone and coherence matter.

How do I begin using Lyria 3.5 in my application?+

Access to Lyria 3.5 is available through Google's AI platform via standard API calls. Users can integrate it using familiar SDKs or REST endpoints, with authentication handled through Google Cloud credentials.

How does Lyria 3.5 differ from other models in the Gemini line?+

Lyria 3.5 is optimized specifically for text generation tasks, offering a balance of quality and efficiency for language-focused use cases. Unlike broader multimodal variants, it focuses on linguistic performance without processing image or audio inputs.

What are the known limitations of Lyria 3.5?+

The model may occasionally produce inaccurate information when dealing with niche or rapidly changing topics. It does not perform real-time data lookup and relies solely on patterns learned during training, so outputs should be reviewed for factual correctness in critical applications.

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