> For the complete documentation index, see [llms.txt](https://docs.sea-lion.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sea-lion.ai/models/sea-lion-v4.5.md).

# SEA-LION v4.5 (Latest)

SEA-LION version 4.5, released in May 2026, is our latest collection of foundational, agentic, and multimodal models optimized for Southeast Asia. Utilizing advanced post-training methodologies—such as knowledge distillation and model merging—this suite delivers state-of-the-art regional fluency, precise tool use, and high computational efficiency.

## Gemma-SEA-LION-v4.5 (E2B Series)

Our highly efficient 4-billion parameter series built on Gemma 4. It is optimized for precise function-calling, structured JSON outputs, and autonomous agentic tool-use with minimal memory overhead.

* [Gemma-SEA-LION-v4.5-E2B-IT](/models/sea-lion-v4.5/gemma-sea-lion-v4.5.md)

  Refer to the detailed page to access model cards, usage scripts, and deployment configurations for the entire Gemma family:

  * Core Agentic Foundations

## Qwen-SEA-LION-v4.5 (27B Series)

Our high-capacity flagship causal and vision-language series built on Qwen3.6. It features a native 262K context window, robust multi-turn reasoning, and repository-level coding adapted for regional linguistic and cultural contexts.

* [Qwen-SEA-LION-v4.5-27B-IT](/models/sea-lion-v4.5/qwen-sea-lion-v4.5.md)

  Refer to the detailed page to explore full benchmarks, technical specifications, and configuration details for the entire Qwen family:

  * Base & Multimodal Foundations
  * High-Throughput Booster (SpecDecoder version)


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.sea-lion.ai/models/sea-lion-v4.5.md?ask=<question>&goal=<endgoal>
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