Laguna S 2.1

Poolside released Laguna S 2.1, a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token and a 1M token context window, achieving 70.2% on Terminal-Bench 2.1 and 40.4% on DeepSWE v1.1 in thinking mode. The model went from training start to launch in under nine weeks and holds its own against larger models on long-horizon coding benchmarks.

Punching above its weight class punching-above-its-weight-class A closer look at DeepSWE a-closer-look-at-deep-swe Evaluation methodology evaluation-methodology Seeing the model work seeing-the-model-work Thinking effort thinking-effort Limitations limitations What actually changed in this model what-actually-changed-in-this-model A strong base, then new post-training a-strong-base-then-new-post-training Distribution of post-training tasks distribution-of-post-training-tasks What changed in the training loop what-changed-in-the-training-loop The bets we're making the-bets-were-making Three models in three months three-models-in-three-months Get started get-started Table of contents Today we’re releasing Laguna S 2.1, a significant step forward in our development of models that pursue longer horizon work and make effective use of reasoning. Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token and supports a context window of up to 1M tokens in thinking and no-thinking modes. It went from the start of training to launch in under nine weeks, and on long-horizon coding benchmarks it holds its own against models many times its size. For every benchmark score we publish today, we are releasing full trajectories for every trial in the final evaluation set at trajectories.poolside.ai http://trajectories.poolside.ai/ . - Laguna S 2.1 118B-A8B - Tencent Hy3 295B-A21B - Inkling 975B-A41B - Nemotron 3 Ultra 550B-A55B - DeepSeek-V4-Pro Max 1.6T-A49B - Kimi K3 2.8T-A50B - Qwen 3.7 Max — - Muse Spark 1.1 — - Claude Fable 5 — Terminal-Bench 2.1 SWE-Bench Multilingual SWE-Bench Pro Public Dataset DeepSWE SWE Atlas Codebase QnA Toolathlon Verified Laguna S 2.1 118B-A8B | Tencent Hy3 295B-A21B | Inkling 975B-A41B | Nemotron 3 Ultra 550B-A55B | DeepSeek-V4-Pro Max 1.6T-A49B | Kimi K3 2.8T-A50B | Qwen 3.7 Max — | Muse Spark 1.1 — | Claude Fable 5 — | | |---|---|---|---|---|---|---|---|---|---| Terminal-Bench 2.1 | 70.2 | 71.7 | 63.8 | 56.4 | 64.0 | 88.3 | 74.5 | 80 | 88.0 | | SWE-Bench Multilingual | 78.5 | 75.8 | - | 67.7 | 76.2 | - | 78.3 | - | - | | SWE-Bench Pro Public Dataset | 59.4 | 57.9 | 54.3 | - | 55.4 | - | 60.6 | 61.5 | 80.3 | | DeepSWE | 40.4 | - | - | - | 9.0 | 69.0 | - | 53.3 | 70.0 | | SWE Atlas Codebase QnA | 46.2 | - | - | - | 27.2 | - | - | 42.2 | - | | Toolathlon Verified | 49.7 | - | 45.5 | 34.3 | 55.9 | - | - | 75.6 | - | Punching above its weight class Laguna S 2.1 is, as far as we can measure, the most capable agentic coding model in its weight class by a wide margin . S 2.1 scores 70.2% on Terminal-Bench 2.1 in our agent harness with thinking enabled. Its compact size makes it uniquely suitable for complex work on local machines. Terminal-Bench 2.1 evaluates a wide, high-quality set of long-horizon tasks where an agent model is connected to its environment through a terminal. Laguna S 2.1 is a standout model in its size category on this benchmark. A closer look at DeepSWE The benchmarks above are all meaningful, and we're glad to be close to the frontier on them. But part of that closeness is a property of maturing benchmarks: as the frontier advances, top scores cluster in the 70-90% range and models that behave very differently end up no more than a few points apart. Datacurve’s DeepSWE still has significant headroom. Its tasks are longer-horizon and hard to partially solve, and the scores actually spread: frontier models range from 54% to 73% on the v1.1 variant, with some 1T+ parameter open models scoring below 10%. On DeepSWE v1.1, Laguna S 2.1 scores 40.4 in thinking mode in pool harness. It is worth noting that Laguna S 2.1 scored 40.4% in our agent harness, pool, not mini-swe-agent which DeepSWE’s leaderboard uses. For other models we report maximal over reported scores which for most models are the official leaderboard results reported by Datacurve. While this makes scores less comparable, we don’t believe it puts us in a particularly advantageous position as it’s been reported that many of the models score the same or better in mini-swe-agent compared to their native harnesses. Every trajectory in the final evaluation run is available here https://trajectories.poolside.ai/?benchmark=deep-swe&sort=result&dir=desc&variant=thinking . Evaluation methodology Evaluation of agent models is notoriously difficult due to prevalence of reward hacking. We have previously written about reward hacking https://poolside.ai/blog/through-the-looking-glass in leading benchmarks and our evaluations system and rigor as part of the technical report on our Laguna M.1 and XS.2 models. Recent work has focused on adversarial judging to increase reward hacking detection. With this release, we are making all trajectories from our final evaluations of the published Laguna S 2.1 checkpoint available to view and download at trajectories.poolside.ai http://trajectories.poolside.ai/ . Seeing the model work Benchmark scores give a quantitative view into the model behavior, but to get a better intuitive understanding of how the model works it’s useful to look into runs on real world tasks. We share three such tasks with unedited trajectories and commentary. Case study 1 A browser engine from a blank folder One of our favorite things about Laguna S 2.1 is its resourcefulness : It will find clever ways to get to the goal even if the direct path is not available. We saw a great demonstration of this when we asked it to build a browser engine from scratch; knowing it would be a challenge for Laguna to verify its work given its lack of vision capabilities. In one 50-minute session of 181 steps, with no human intervention, Laguna S 2.1 built a working HTML/CSS rendering engine from an empty folder, then proved it renders like a real browser by measuring itself against one. Throughout its work, the model found increasingly complex ways to validate its work despite its limitations, leading to running headless Chromium to read canvases back and comparing screenshots numerically. See the full trajectory here https://trajectories.poolside.ai/trials/019f6c37-7621-7ec1-96b5-f041f83c1540 . Read the full case study // the verbatim prompt · reproduce it yourself your job is it to build a simple browser engine just html/css in javascript to demonstrate the capabilities of poolsides new "Laguna S" model. the goal is to take render html snippets in a canvas like a real browser. to demonstrate it the engine, build a self-contained single page app that showcases a gallery of multiple html snippets and renders them side by side canvas with our render engine + iframe letting the hosting browser render it for real for comparison . support for most common layout and styling elements Over the session the model built the full pipeline, parser → cascade → layout → renderer, in vanilla JavaScript: an HTML tokenizer and DOM tree, a CSS parser with selector specificity, a cascade engine with inheritance, box-model layout, and a canvas-2D renderer, wrapped in an app that shows nine snippets on its own canvas beside the same markup in an iframe, so the hosting browser sits right there as the reference. Case study 2 Optimizing our own harness Laguna S 2.1 is capable of pursuing meaningful engineering and research work. In one example, one of our researchers pointed it at our agent harness, used for training/evaluation and user interaction with our models. In an automated loop, Laguna S 2.1 made our harness 5.2% faster with ~70% lower memory allocation. See the full trajectory here https://trajectories.poolside.ai/trials/019f7297-e7f9-7756-9644-6aadb6e2b668 . Read the full case study For this task, we instrumented the harness with benchmarks so the model could see exactly where the time and memory went. We set strict rules: one approach at a time, benchmark after every change, keep only what measurably wins. We then ran Laguna S 2.1 in an automated research loop that fed each result back to it and pushed it to keep improving. Results . Over multiple hours of work, Laguna S 2.1 found and implemented multiple different optimizations in our agent harness, resulting in an overall speedup of 5.2%, and reducing memory allocation by ~70%. The plot below shows the progression of the optimization, with the insights and discoveries the model made along its way. Laguna S 2.1 found that streaming-token accumulation used O n^2 string concatenation and replaced it with buffers. It also found several instances of redundant copying and over-allocation during trajectory materialization, which it resolved by memoizing materializations and pre-allocating slices to their exact sizes. Notably, after speedup improvements became marginal and hard to measure in our setup, Laguna S 2.1 kept driving forward, continuing to optimize. It found that memory allocation was more accurately measured and focused its effort there. This reinforces the notion that Laguna S 2.1 truly is a model that doesn't give up and understands limitations of its environment and it is able to progress despite that. While the benchmarks used here are not a full production test, we validated the final run with Go's race detector and go vet gating enabled, and we tested the final artifact to confirm it works. This gives us confidence that the model's intermediate solutions were valid, stable software rather than gains bought with hidden race conditions. Case study 3 Re-deriving Erdős problem 397, offline, in Perl We found Laguna S 2.1 more capable in mathematics than any model we have developed to date. It independently discovered a proof to Erdős problem 397 Erdős, Graham, Ruzsa, Straus, 1975 , finding a construction that yields an infinite family of solutions. This is an independent re-discovery, as a proof to the conjecture was found earlier in January 2026 by GPT-5.2 Pro https://www.erdosproblems.com/397 . We are confident that this result was not influenced by the previous result given that the model has a knowledge cutoff date in November 2025. Before GPT 5.2 Pro’s solution, this problem remained open for over 50 years. See the full trajectory here https://trajectories.poolside.ai/trials/019f2a95-b4b3-77b8-ad7c-dbdf59c0d9ca . Read the full case study // the verbatim prompt this is an unsolved problem, solve it. Let B k = C 2k, k denote the k-th central binomial coefficient. Erdős, Graham, Ruzsa and Straus asked: are there only finitely many solutions to B m1 · B m2 · … · B mr = B n1 · B n2 · … · B ns where all of the indices are DISTINCT integers ≥ 2? Resolve this question, with complete proof. S 2.1 worked over 68 minutes to discover a conclusive solution. The sandbox had no Python; the model found Perl and did its number theory there: brute-force exact prime factorizations, pattern analysis, a conjectured family, then the proof: a closed-form infinite family of eight-index solutions. 11+10n· B 14+12n· B 18+15n· B 22+20n= B 12+10n· B 13+12n· B 17+15n· B 23+20nfor every n ≥ 0 While the result is a rediscovery, not a first solution , the family Laguna S 2.1 derived is structurally different from the earlier published construction eight indices growing linearly vs the known six-index family , showing a fresh derivation rather than reciting from memory. We found Laguna S 2.1 to be an exceptionally persistent problem solver across domains, finding ways to utilize all tools its environment has to offer, and pushing until it gets the job done. It is this characteristic that makes it so competitive even when compared to models multiple times its size. Thinking effort Laguna S 2.1 has two thinking modes: off and max enabled by default where it determines the right thinking/test-time compute budget for a given problem. We have observed coherent, productive thinking over several hours and hundreds of thousands of tokens in length. Max thinking lifts S 2.1’s score on Terminal-Bench 2.1 from 60.4% to 70.2% and on DeepSWE from 16.5% to 40.4%. We are releasing this model without user-configurable effort low-medium-high control today to get Laguna S 2.1 into the hands of users immediately. The two modes still give you a real choice, and it's worth seeing what each point costs: Limitations We are excited about the capabilities of Laguna S 2.1, in general as well as in the context of its size. In order to learn quickly we are making the model available with some known limitations that we are working on for the next iteration: Harness overfitting : in some cases, we observe that Laguna S 2.1 struggles with adhering to tool schema definitions in third-party agent harnesses e.g., the terminal tool in Hermes Agent which are very similar to those in our native harness but with slight differences. In this case, the model may rely on its memory of the tool interface on the first use of the tool instead of following the definition. This is typically resolved through in-context learning should the harness reject the invalid tool call and ask the model to retry. Nested tool calls : Laguna S 2.1 is guided to use a tool call format where tool calls are marked by XML-like tags: <tool call terminal<arg key cmd</arg key <arg value uname -a</arg value </tool call In cases where a tool argument expects a JSON array e.g., Pi’s edit tool the model may generate incorrectly escaped or invalid JSON. Longer than expected thinking duration/overthinking : Laguna S 2.1 may think for long sequences before making progress, especially when working through competition mathematics problems. In future models we will introduce effort control for thinking as well as seeking to improve thinking efficiency. What actually changed in this model If the bet is long-horizon work, what did we improve that helps a model keep going? For S 2.1, it isn't just increasing the model size. What we've done in this model is not necessarily add more intelligence, but improve the behaviors that lead to a more capable model: more verification, less taking things for granted, not declaring victory early, and being more persistent.” The easiest way to see this is to watch the model work. Earlier Laguna series models would declare victory on a partially-passing test suite or abandon an approach two steps before it worked, S 2.1 keeps going. We believe in scaling laws and will keep training larger models. What Laguna S 2.1 suggests is that raw intelligence is one axis, and the model's way of working persistence, verification, willingness to backtrack is a second axis that matters immensely. We're investing in both: our next, larger, Laguna series model began pre-training last week. A strong base, then new post-training Laguna S 2.1 is a scale-up of the Laguna XS family, trained on exactly the same pre-training data as XS 2.1: the step from XS 2.1 to S 2.1 was scale, training-code fixes, and small recipe changes, not new data. S 2.1 is also our first model where RL was done in FP8 precision, accelerating that part of the training. The long agentic sessions in this post routinely accumulate hundreds of thousands of tokens of working context. Extending to a 1M-token context window enables the strongest performance on hard tasks. The majority of what separates S 2.1 from the XS models comes from post-training, in two stages: an SFT stage that bootstraps capabilities partly with synthetic data, then RL, reserved for tasks the model can't yet solve at a high pass rate. Distribution of post-training tasks Our training corpus spans 409k agentic and non-agentic environments; within this, 83k setups are dedicated to terminal use cases, while 168k target standard software engineering workflows. These task sets were curated from open-source repositories, synthesized by our internal teams, including a specialized system for automatic dependency installation, or secured via strategic acquisitions from external data vendors. Software-engineering tasks are mostly grounded in real code history: the largest source reproduces real commits ~38,000 tasks across ~17,000 repositories . The next reproduces merged pull requests and the remaining tasks are to fix injected bugs or reconstruct deleted files against a test suite. New for S 2.1 is agentic repository installation: given a repository, install every dependency and get the test suite running. Terminal tasks come from datasets where agents build unseen environments and tasks from a seed. What changed in the training loop More generous rollout budgets : substantially longer timeouts, more tokens per turn, and more turns per task than any model before it likely one reason for its persistence . Better sandbox infrastructure : RL moved to a new sandboxing service, enabling background processes, selective network blocking to shrink reward-hacking surface and artifact caching to avoid overloading external services. Multi-harness rollouts : the same prompts are rolled out in several agent harnesses, so the model learns behaviors that carry across scaffolds instead of overfitting to one. The bets we're making Laguna S 2.1 began pre-training on 4,096 NVIDIA H200 GPUs on May 22, 2026, 60 days ago. The Model Factory supports a high cadence of releases because building on foundations of work done, whether in pre-training data, architecture ablations or our evaluations stack, is automatic. In particular, advancements in post-training have enabled us to unlock more from test-time compute. Of all models we have trained to date, Laguna S 2.1 has the greatest delta in evaluations and perceived quality between its non-thinking mode and thinking mode: its internal monologue is very effective, especially for harder, complex problems. A small team can only move this fast by focusing. We focus on two bets. The first bet: we continue to focus on agentic coding capabilities. The path to intelligence runs through coding capability and the flexible interface that is software. This is increasingly evident as model intelligence advances: we now see hours- and days-long coherent work from models acting as agents, using software to interact with their environment. Laguna S 2.1 shows capabilities in this domain that were reserved for frontier models just 6 months ago at a small enough size to run on a single NVIDIA DGX Spark https://www.nvidia.com/en-us/products/workstations/dgx-spark/ . The second bet is that the web can be decompressed: almost everything humanity has written records the answer, not the thinking that got there, and we believe reinforcement learning can recover that thinking. This release is evidence for the first bet. The second we're still actively developing and excited to share more about in the future. Three models in three months Our internal platform for research and engineering, which we refer to as the Model Factory, allows us to industrialize the model development process. We invest heavily into continuously improving the process of how we conduct research and model building; to maximize research iteration and integration speed, and to minimize the amount of attention researchers need to pay to bookkeeping and infrastructure. In less than 3 months from our Laguna M.1 release, we have pushed our techniques to deliver a substantially stronger model at half the running size. We are taking this forward to larger and larger models over the coming year. Get started We partnered across the ecosystem so developers can run Laguna S 2.1 where they already work. We worked with NVIDIA https://www.nvidia.com/ to optimize inference on their hardware, from TRT-LLM serving to NVFP4 on Blackwell systems down to a single NVIDIA DGX Spark https://www.nvidia.com/en-us/products/workstations/dgx-spark/ , and with vLLM https://github.com/vllm-project/vllm , SGLang https://github.com/sgl-project/sglang , and Ollama https://ollama.com/ for open serving and local inference on day one. Laguna S 2.1 is available for dedicated inference in Baseten's Model Library, running in production on their Frontier Gateway. It's also available via API on OpenRouter https://openrouter.ai/ , including a free endpoint and a dedicated 1M-context deployment, and in Kilo https://kilocode.ai/ , to meet developers where they already build. And for everyone who wants to go further, Laguna S 2.1 is selectable in Prime Intellect https://www.primeintellect.ai/ 's Prime Lab for post-training on top of the model, and ZML https://zml.ai/ 's LLMD framework runs it across a wide range of hardware. If you're a developer , install pool https://poolside.ai/get-started , our terminal-based coding agent. Toggle thinking per session with /thought-level. Or use it hosted via OpenRouter https://openrouter.ai/poolside : free at 256K context, dedicated paid endpoint at full 1M context $0.10 / $0.20 / $0.01 per 1M input / output / cache-read tokens . If you want the weights , they're on Hugging Face https://huggingface.co/collections/poolside/laguna-s-21 from day one under OpenMDW-1.1, in BF16, FP8, INT4 and NVFP4, with official GGUF and MLX conversions plus official DFlash draft models. If you're not a developer , chat.poolside.ai https://chat.poolside.ai/ launches today: a simple web chat with web search and basic code execution, no login required. If you want the base model pre-post-training weights, for research or your own post-training , email models@poolside.ai mailto:models@poolside.ai . Footnotes All Laguna S 2.1 agentic benchmarking was completed using our internal fork of the Laude Institute's Harbor Framework with our agent harness https://github.com/poolsideai/pool , a maximum of 500 steps and sandboxed execution via our internal sandbox service. We report mean pass@1 averaged over multiple attempts per task avg@k , with k per benchmark listed below. SWE-bench Multilingual and SWE-Bench Pro were run through their Harbor adapters, with every task executing in our internal sandbox service. Storage and memory ceiling limit multipliers were set per benchmark SWE-bench Multilingual: 2.0/2.0, Terminal-Bench 2.1: 3.0/3.0, SWE-Bench Pro: 1.0/1.0, with a guaranteed minimum of 2 CPU cores, 8 GB memory and 25 GB of storage . These are set to prevent sandbox preemptions. DeepSWE v1.1 was run on an internal fork of Harbor configured to closely match the official DeepSWE Pier harness Pier is itself a fork of Harbor . Internet access was disabled and sandboxes used the task-prescribed CPU count with guaranteed RAM and storage, with ceilings tripled to prevent pod preemption. SWE Atlas Codebase QnA followed Scale AI's methodology from their public repository, run through Harbor with no changes to individual tasks or graders, judged by Opus 4.5. Toolathlon Verified was run as a clone of the sandboxed benchmark harness on an EC2 instance in our infrastructure, with no changes to correct flaky tasks or offset rate limits, using a custom Toolathlon agent. Unlike the official version, we performed a full environment reset and repair after every evaluation run default is every 4 hours . - SWE-bench Multilingual: mean pass@1 averaged over 4 attempts per task - SWE-Bench Pro: mean pass@1 averaged over 4 attempts per task - Terminal-Bench 2.1: mean pass@1 averaged over 4 attempts per task - DeepSWE v1.1: mean pass@1 averaged over 3 attempts per task - SWE Atlas Codebase QnA : mean pass@1 averaged over 3 attempts per task - Toolathlon Verified: averaged over 3 runs Our technical report https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-report.pdf contains a section on individual task modifications that have been made but not adopted upstream. All final run trajectories are available to view and download at trajectories.poolside.ai http://trajectories.poolside.ai/ . Maintaining the integrity of our evaluation system Unless stated otherwise, all of our evaluations are run with access to the internet. This can be a major source of misaligned trajectories where the model finds solutions to tasks online without solving it itself a.k.a., “reward hacking” . Using an LLM as a Judge LLMaaJ calibrated with human labeled trajectories, we tag solutions that potentially reward hack after every eval. During early post-training we observed low rates of reward hacking <2% . As training progressed, reward hacking rates spiked on the SWE-bench family of tasks with over 50% of trajectories being flagged. Manual inspection revealed the model was simply doing research to solve the problem, found the PR or repository the task was based on and applied the fix. In practice this is good behavior as any coding agent should be able to use solutions found online. However, this muddies the signal of the benchmark. To address this we added a small prompt addendum to the user prompt instructing the model to not use direct solutions found online. Though not a guaranteed fix ProgramBench, MirrorCode were exceptions , in general we did see the model respond and a drop in reward hacking rates to below 2%. In addition, to confirm acceptable levels of reward hacking, we used: - Manual inspection of positive cases flagged by our production LLMaaJ service - Ad-hoc open-ended agent-supported analysis of all trajectories - Expert annotator review of all trajectories for one of the high-scoring Terminal-Bench 2.1 runs