explainx / blog
Poolside released Laguna S 2.1 on July 22, 2026 — a 118B MoE model with 8B active params, 1M context, thinking mode, and 70.2% on Terminal-Bench 2.1. Open weights, free on OpenCode, runs on a single NVIDIA DGX Spark.
Jul 22, 2026
The model gets the headline; the harness decides whether the agent actually finishes the task. Here are the top 10 closed-source and top 10 open-source agent harnesses builders are running in 2026 — what each one does differently, what it costs, and who should pick it.
Jun 19, 2026
OpenCode lets you connect any model from any provider — Claude, GPT, Gemini, GLM-5.2, local Ollama — in terminal, desktop, or IDE. With LSP auto-loading, parallel sessions, share links, and optional OpenCode Zen curated models, it is one of 2026's default open-source harnesses. How it maps to agent harness design and when to pick it over Pi or Claude Code.
Jul 22, 2026
Codeberg e.V. members passed a Terms of Use amendment prohibiting "LLM-extrusions" — projects mostly generated by AI without meaningful human review. explainx.ai breaks down the exact policy, the copyright reasoning behind it, and the open questions the Codeberg community itself raised.
A 118-billion-parameter model that only activates 8 billion of those parameters per token just beat a larger closed competitor on a real agentic coding benchmark — and it's free to run today. Poolside released Laguna S 2.1 on July 22, 2026, its newest open-weight coding model, built as a Mixture-of-Experts (MoE) architecture with a 1-million-token context window, separate thinking and no-thinking modes, and a training run Poolside says took under nine weeks on 30 trillion tokens.
OpenCode added support the same day, making Laguna S 2.1 free to use with the full 1M context window through one of 2026's default open-source agent harnesses — no separate API key or hosting setup required to start using it.
| Question | Answer |
|---|---|
| What is it? | Poolside's newest open-weight coding model |
| Released? | July 22, 2026 |
| Size? | 118B total parameters, 8B active per token (MoE) |
| Context window? | Up to 1 million tokens |
| Terminal-Bench 2.1 score? | 70.2% — ahead of DeepSeek-V4-Pro-Max |
| Memory footprint? | ~71GB |
| Local speed? | 50-77 tokens/second on a single NVIDIA DGX Spark |
| Training time? | Under 9 weeks on 30 trillion tokens |
| License? | Open weights, permissive license |
| Free access? | Yes — via OpenCode, full 1M context |
The number that matters most in Poolside's release isn't 118 billion — it's 8 billion, the count of parameters actually activated per token in this Mixture-of-Experts design. MoE architectures route each token through only a subset of the model's total "expert" sub-networks rather than computing through every parameter on every forward pass, which is exactly why Poolside can claim the model fits into roughly 71GB of memory and runs at 50-77 tokens per second on a single NVIDIA DGX Spark — a meaningfully smaller and more accessible hardware footprint than most 100B+ parameter dense models require.
Co-CEO Eiso Kant framed the release around exactly this efficiency angle rather than raw parameter count: "At 118B total parameters, with 8B active per token, it does the work of models several times its size on agentic coding. It is remarkably persistent across long-horizon tasks. And it is small enough to run on a single NVIDIA DGX Spark." That's a direct pitch toward what Kant separately described as the goal — making frontier-adjacent intelligence "a commodity for everyone," rather than something that requires a data-center-scale deployment to run.
Poolside's own benchmark chart puts Laguna S 2.1 at 70.2% on Terminal-Bench 2.1, ahead of DeepSeek-V4-Pro-Max on the same evaluation, and reports it topping charts on SWE-Bench-style coding tasks as well. Terminal-Bench measures an agent's ability to complete real terminal-based coding tasks autonomously — closer to how these models actually get used inside a harness like OpenCode or Claude Code than a static code-completion benchmark would be.
X user sudoingX captured the reaction from the local-AI community directly: "this is the drop the local ai crowd should be losing their minds over. poolside just dropped laguna s 2.1: 118b total parameters, only 8b active per token, a full 1m context window, open weights under a real open license, on huggingface today." The emphasis on "a real open license" is notable — plenty of nominally "open" model releases in 2026 have shipped with usage restrictions that limit commercial deployment; Poolside's framing suggests Laguna S 2.1 avoids that pattern, though teams evaluating it for production use should verify the exact license terms on Hugging Face directly before committing.
Laguna S 2.1 ships with two distinct operating modes rather than requiring separate model variants for reasoning-heavy versus fast-response tasks. Thinking mode lets the model work through a problem step by step before producing output — the mode Poolside credits for the model being "remarkably persistent across long-horizon tasks," the exact quality that matters most inside an agent harness's loop controller, where a model needs to stay coherent across many tool calls without losing track of the original goal. No-thinking mode skips that reasoning phase for lower-latency responses on simpler requests, letting one model cover both ends of the speed-versus-depth tradeoff instead of forcing a choice between two separately trained models.
The fastest path to trying Laguna S 2.1 is through OpenCode, which posted its own confirmation the same day: "Laguna S 2.1 is now free on OpenCode. 1M Context · fully open source. Poolside's most capable model to date." That gives immediate access through OpenCode's terminal TUI, desktop app, or IDE integration without needing to provision local hardware or manage API keys — consistent with OpenCode's broader positioning, covered in explainx.ai's OpenCode guide, as a harness that supports 75+ LLM providers behind one consistent interface.
For teams wanting to self-host, Poolside's own guidance — a single NVIDIA DGX Spark handling 50-77 tokens/second — puts local deployment within reach of a well-equipped workstation setup rather than requiring a multi-GPU cluster, a direct consequence of the 8B active-parameter MoE design keeping compute requirements down even at 118B total parameters.
Laguna S 2.1 lands in a crowded field of open-weight coding models competing on the same rough axes — parameter efficiency, context length, agentic benchmark scores, and training cost — that DeepSeek, GLM, Kimi, and Qwen have all been pushing on throughout 2026. What differentiates Poolside's entry is the training-time claim specifically: under nine weeks on 30 trillion tokens is a notably lean run for a 118B-parameter model, and if that figure holds up to scrutiny, it says as much about Poolside's training infrastructure and data pipeline efficiency as it does about the resulting model's benchmark scores. Whether Laguna S 2.1's real-world coding-agent performance matches its Terminal-Bench numbers once more developers put it through varied, messy production codebases — rather than a curated benchmark suite — is the next thing worth tracking as adoption through OpenCode grows.
Benchmark figures, training details, and pricing reflect Poolside's July 22, 2026 announcement and OpenCode's same-day integration post. Independent reproduction of the reported Terminal-Bench and SWE-Bench scores had not been published as of this post.
