Learnlm 1.5 Pro Experimental
Google (Gemini)
At a glance
- Price per 1M tokens (input)
- free
- Price per 1M tokens (output)
- free
- Context
- 32,767 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 12, 2026.
The Learnlm Pro Experimental model by Google, also known as Gemini, is designed for businesses and organizations that require advanced image understanding and tool calling capabilities. Tool calling refers to the ability of the model to invoke specific functions or tools to perform tasks, such as image analysis or data processing. This model is particularly suited for tasks that involve visual data, such as image classification, object detection, and image segmentation. The vendor's approach to this model is distinguished by its emphasis on flexibility and customization, allowing users to tailor the model to their specific needs. By leveraging the power of image understanding and tool calling, users can automate complex tasks and gain valuable insights from visual data.
Specifications & pricing
| Input (per 1M tokens) | free |
|---|---|
| Output (per 1M tokens) | free |
| Context window | 32,767 tokens |
| Max output | 8,192 tokens |
| Capabilities | images, tool calling |
LiteLLM community dataset (MIT), verified August 15, 2026. Official Google (Gemini) pricing.
What Learnlm 1.5 Pro Experimental would cost on your workload — run it through the cost calculator →
Where to try Learnlm 1.5 Pro Experimental for free
- Google (Gemini) offers a free chat — Gemini (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
- 🎁 On our promo-codes page: Gemini for free: access and discounts.
Frequently asked questions
What is this model good for+
This model is good for tasks that involve image understanding, such as image classification, object detection, and image segmentation, as well as tasks that require tool calling, such as data processing and analysis
How do I get started with this model+
To get started with this model, users should familiarize themselves with the model's capabilities and limitations, and then design a workflow that leverages the model's strengths, such as integrating it with other tools and systems to automate complex tasks
How does this model differ from other models in the line+
This model differs from other models in the line in its emphasis on image understanding and tool calling, making it a good choice for tasks that require these specific capabilities
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