The Clarity platform offers multiple agents, enabling you to experiment with agents that use different AI models from different AI companies and choose the appropriate model for your use case. This page provides an overview of the featured agents available on the Clarity platform to help you understand their capabilities, differences, and how to choose the right one for your needs.
The 🌱 emoji indicates an agent which uses a more environmentally friendly model.
OpenAI Models
GPT-5.2 agent
The default agent in Clarity uses OpenAI’s GPT-5.2 model, an advanced large language model developed to handle complex, multi-step tasks with a high degree of accuracy. GPT-5.2 excels at generating nuanced and contextually relevant responses, making this agent suitable for sophisticated applications and datasets.
GPT-5 mini agent 🌱
This agent uses Open AI’s GPT-5 mini model, a more compact and cost-effective version of GPT-5. It offers a balance between performance and resource efficiency, making it a significantly more environmentally friendly choice while maintaining robust performance. GPT-5 mini is optimal for basic and lightweight tasks and surpasses other small models on academic benchmarks, while supporting the same range of languages as GPT-5.2.
o3 agent
o3 is a powerful reasoning model from OpenAI and is intended for tasks requiring deep analytical thinking, problem-solving and complex reasoning. It takes longer to respond to queries and users more energy due to its longer responses.
| Feature | GPT-5.2 | GPT-5 mini | o3 |
|---|---|---|---|
| Performance[i] | Higher accuracy, coherence, and fluency | Lower accuracy, coherence, and fluency | Higher accuracy, coherence, and fluency |
| Environmental Impact | Higher energy consumption due to greater computational requirements | Lower energy consumption, making it more eco-friendly | Higher energy consumption due to greater computational requirements |
| Use Cases | Suitable for complex queries, creative writing, and detailed analysis | Geared towards basic tasks, and lower-resource applications | Suitable for complex queries, reasoning and complex analysis |
| Image Generation | No | No | No |
| Code execution | Can execute code to create charts or calculate answers | Can execute code to create charts or calculate answers | Can execute code to create charts or calculate answers |
| File uploads | Yes | Yes | Yes |
| Knowledge cutoff | August 2025 | May 2024 | May 2024 |
[i]LLM Stats provides scores for these models used by these agents on common AI benchmarks.
Anthropic Models
Claude Sonnet 4.6 agent
This agent uses Anthropic’s Claude Sonnet 4.6 model to generate text responses and code. The Claude Sonnet 4.6 model is suitable for coding tasks, summarizing and reasoning on large amounts of text, and supporting creative and academic writing.
| Feature | Claude Sonnet 4.6 |
|---|---|
| Performance[ii] | Higher accuracy, coherence, and fluency |
| Environmental Impact | Higher energy consumption due to greater computational requirements |
| Use Cases | Suitable for complex queries, creative writing and code generation |
| Image Generation | No |
| Code execution | Yes |
| File uploads | Yes |
| Knowledge cutoff | August 2025 |
[ii]LLM Stats provides scores for these models used by these agents on common AI benchmarks.
Google Models
Gemini 2.5 Pro agent
The Gemini 2.5 Pro model is designed for advanced reasoning and complex problem solving. With a 1-million token context window it can handle large amounts of data and documents. Gemini 2.5 Pro does sacrifice some speed in responding for more thinking.
Gemini 2.5 Flash agent 🌱
Gemini 2.5 Flash has a 1 million-token context window and lower latency, enabling quicker responses and suitability for chatting. Additionally, it consumes less energy and is more cost-effective than Gemini 2.5 Pro.
| Feature | Gemini 2.5 Pro | Gemini 2.5 Flash |
|---|---|---|
| Performance[iii] | Higher accuracy, coherence, and fluency | Lower accuracy, coherence, and fluency |
| Environmental Impact | Higher energy consumption due to greater computational requirements | Lower energy consumption, making it more eco-friendly |
| Use Cases | Suitable for complex queries, creative writing, and detailed analysis | Geared towards basic tasks, and lower-resource applications |
| Image Generation | No | No |
| Code execution | Can execute code to create charts or calculate answers | Can execute code to create charts or calculate answers |
| File uploads | Yes | Yes |
| Knowledge cutoff | January 2025 | January 2025 |
[iii]LLM Stats provides scores for these models used by these agents on common AI benchmarks.