Poolside releases Laguna XS 2.1 for coding agents
Poolside released Laguna XS 2.1, a coding-focused AI model built for and longer software tasks. It is a 33B-parameter model, but it uses about for each token, which is meant to lower the work needed for each response. Compared with Laguna XS.2, its SWE-bench Multilingual score rose from 57.7% to 63.1%; it also scored 70.9% on , 47.6% on SWE-Bench Pro, and 37.5% on .
It trails on several listed tests, but Reddit commenters described it as a competitive U.S.-based coding model. Poolside says it can run on a Mac with 36 GB of memory, and it offers quantized FP8, INT4, and NVFP4 versions for tighter hardware budgets. A helper model called DFlash roughly doubled in Poolside’s tests.
OpenRouter and Poolside’s API serve it with a 256K token context window, and paid pricing is listed at $0.10 per 1M input tokens, $0.20 per 1M output tokens, and $0.05 per 1M cache-read tokens. It supports Ollama, llama.cpp, TensorRT-LLM, vLLM, SGLang, and Transformers, and Poolside also offers a terminal coding agent called pool.
Key points
- Laguna XS 2.1 is aimed at coding agents and long software tasks.
- It has 33B total parameters but uses about per token.
- Its SWE-bench Multilingual score improved from 57.7% to 63.1% over the prior version.
- Quantized FP8, INT4, and NVFP4 versions plus DFlash are directly relevant to local cost and speed.
- API pricing is listed at $0.10 per 1M input tokens, $0.20 per 1M output tokens, and $0.05 per 1M cache-read tokens.