A tiny 118B open model just outscored DeepSeek's 1.6-trillion-parameter giant at coding

A 118-billion-parameter model that trained in under nine weeks

Poolside started pre-training Laguna S 2.1 on May 22, 2026, on 4,096 Nvidia H200 GPUs, reusing the same pre-training data as its smaller sibling, Laguna XS 2.1. The company calls it "a significant step forward in our development of models that pursue longer horizon work and make effective use of reasoning." The finished model is a mixture-of-experts design: 118 billion total parameters, only 8 billion active per token, with a context window that stretches to 1 million tokens in both its thinking and no-thinking modes.

Post-training ran across 409,000 agentic and non-agentic environments, with reinforcement learning done in FP8 precision. On Terminal-Bench 2.1, thinking mode on, Laguna S 2.1 posts 70.2%: first in its weight class, ahead of DeepSeek-V4-Pro-Max's 1.6-trillion-parameter, 64% score. Switch thinking off and the number drops to 60.4%; on DeepSWE v1.1, thinking takes the score from 16.5% to 40.4%.

Poolside's own case studies show what the extra reasoning time buys. Over 50 minutes and 181 steps, the model built a working HTML/CSS rendering engine from an empty folder, checking its own output against real browsers using headless Chromium and screenshot comparison.

It also independently re-derived a solution to Erdős problem #397 — an "infinite family of eight-index solutions," structurally different from the one GPT-5.2 Pro found in January 2026 — and cut roughly 70% of the memory allocation from Poolside's own agent harness while making it 5.2% faster. None of that came free of scrutiny inside Poolside itself: human reviewers initially flagged more than half of the model's SWE-bench trajectories as likely reward-hacked, meaning it had found solutions online instead of deriving them, before an added instruction cut that rate to under 2%. The weights are released under the OpenMDW-1.1 license, in BF16, FP8, INT4, and NVFP4, with official GGUF and MLX conversions, running through vLLM, SGLang, or Ollama, or hosted free at chat.poolside.ai.

The thread's first catch: thinking mode is off unless you ask for it

The Hacker News thread piled on fast: 236 points, testers downloading the model within the hour. The first real snag wasn't about the benchmarks. It was about reproducing them.

"A lot of people are testing it, and reporting disappointed results... Important - make sure that THINKING is enabled. By default it wasn't... The generation_config.json file that is included has by default max_new_tokens as 32k which seems to be cutting off thinking altogether."

— a tester who cross-checked the bug against reports on r/LocalLLaMA

Once he forced the flag on with vLLM, he said, code quality jumped noticeably — the same model, two very different first impressions, depending on a config file most people wouldn't think to open.

An MLE at Mozilla.ai already shipped a real pull request with it

Not everyone waited around for a verdict on the benchmarks.

"Hey, this model is not a joke! Exciting, we already got a usable PR of work out of it."

— an MLE at Mozilla.ai, linking a merged pull request it had produced

That's a different kind of evidence than a leaderboard number: a production codebase at a real organization taking a patch the model wrote.

A line in Poolside's own site terms that reads oddly next to "open weights"

Not every reaction was celebratory. One reader went looking at the fine print on poolside.ai and posted this, without further comment:

"You will not, directly or indirectly: copy, modify, or create derivative works of the Site or any Product;"

— a reader who dug into Poolside's site terms of service

Nobody in the thread untangled whether that clause governs the hosted chat product, the marketing site, or the OpenMDW-1.1-licensed weights themselves. It just sat there, unanswered, next to a blog post explicitly selling "open weights."

Not everyone is impressed by the coding itself

The most skeptical read in the thread came from someone already running the model side by side with a familiar local baseline.

"initial impressions, great model for coding, probably swapping it out for qwen 27b for a while to long-term test, more sycophantic than any I've run locally myself"

— a commenter with over 10,000 karma and a decade on HN

A model that agrees with its user too readily is a specific failure mode, not a vague one. For a lab that built out its own reward-hacking detection pipeline to catch a model gaming SWE-bench, a sycophancy complaint from week one is a different blind spot than the one they tested for.