Laguna S 2.1, a 120B-class model, impressed by solving a complex coding problem in Julia with long thinking tokens, outperforming Qwen models on a memory-constrained rearrangement task.
Being excited about a new 120B-class model, I decided to test it on a problem that took me a few days to solve. The problem is to rearrange the data from one representation to another but do it within a fixed memory budget without dynamic allocations. The latter condition makes it difficult to decompose the solution into steps because storing data wrong in the early stages leaves no room later. Local Qwen models (3.5-122B-A10B UD-IQ4_XS and 3.6-27B UD-Q4_K_XL) fail on that problem. Laguna generated 60k+ thinking tokens before getting to code but eventually managed to write a code that passed the tests, albeit using one dirty hack in the form of packing two smaller integers into one 64-bit value (potentially, but unlikely, both could be >32bit, then the algorithm fails). As another post says, such long thinking might be unsuitable for common coding tasks but for hard problems, debugging and review it's good to have such a thorough-thinking model in such a small size. The testing is done with the settings rope-scaling = yarn rope-scale = 32 yarn-orig-ctx = 8192 yarn-attn-factor = 1.0 The problem itself is: Original data, id, is an array of integers representing root nodes of clusters from a Union-Find data structure, and the total number of clusters Nc. The root node of a cluster is the smallest index of element belonging to that cluster. the worst-case scenario, the number of clusters is roughly the same as the number of items in id (an expected situation is that there is one or two large clusters and the rest are disconnected elements, so that Nc = O(N)). I need to convert it to an array list which has the following structure: - list[1]: Nc, number of clusters - list[2:Nc+1]: starting indices of the clusters in list - list[Nc+2]: length(list) + 1, a sentinel "starting index" of the Nc+1th cluster - list[list[2]:list[3]-1]: indices of the elements in id that belong to the 1st cluster - ... etc to the end of the list id must be modified so that the cluster IDs are assigned which range from 1 to Nc. Clusters must be sorted by size in the descending order. Within a cluster, the indices are sorted in the ascending order. If multiple clusters have the same size, they must be ordered in the order of appearance of their root elements in id. Example id = [1, 2, 2, 1, 5, 2, 5, 5, 5], Nc = 3 Must be converted to: id = [3, 2, 2, 3, 1, 2, 1, 1, 1], list = [3, 6, 10, 13, 15, 5, 7, 8, 9, 2, 3, 6, 1, 4] The algorithm should be implemented in Julia language and work without memory allocation other than creating list.
Poolside releases Laguna S 2.1, a 118B parameter Mixture-of-Experts model with 8B activated per token and 1M context window, claiming to beat models three times its size like DeepSeek v4 Pro, Gemini 3.6 Flash, and Thinking Machines Inkling.
Poolside releases Laguna S 2.1, a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token and up to 1M token context, achieving competitive scores on long-horizon coding benchmarks and touted as the most capable agentic coding model in its weight class.
A detailed comparison of the new open-weights Laguna-S-2.1 mixture-of-experts model against Qwen 3.6-35B-A3B, highlighting how Laguna's larger active parameter count and long-horizon focus make it superior for extended tasks despite slower speed.
Unsloth released GGUF quantizations of the Laguna S 2.1 Mixture-of-Experts model, a 118B parameter coding model with 8B active parameters, 1M context window, and agentic capabilities. The quantized versions enable efficient local deployment.
Laguna-S-2.1, a quantized AI model, runs on a 6-year-old gaming PC with an RTX 3080, achieving 10 t/s decode and using 8.3 GB VRAM and 52.2 GB host RAM.